FIRST NODE, COMMUNICATION SYSTEM AND METHODS PERFORMED THEREBY FOR HANDLING A PERIODICITY OF TRANSMISSION OF ONE OR MORE REFERENCE

SIGNALS

TECHNICAL FIELD

The present disclosure relates generally to a first node and methods performed thereby for handling a periodicity of transmission of one or more reference signals. The present disclosure also relates generally to a communication system, and methods performed thereby for handling the periodicity of transmission of the one or more reference signals. The present disclosure further relates generally to computer programs and computer-readable storage mediums, having stored thereon the computer programs to carry out these methods.

BACKGROUND

Communications systems may comprise one or more nodes. A node may comprise one or more processors which, together with computer program code may perform different functions and actions, a memory, a receiving port and a sending port. A node may be, for example, a server. Nodes may perform their functions entirely on the cloud.

The standardization organization 3GPP is currently in the process of specifying a New Radio Interface called Next Generation Radio/New Radio (NR) or 5G-UTRA, as well as a Fifth Generation (5G) Packet Core Network, which may be referred to as 5G Core Network, abbreviated as 5GC.

A 3GPP system comprising a 5G Access Network (AN), a 5G Core Network and a User Equipment (UE) may be referred to as a 5G system.

In 5G-NR, the Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS) may be combined with the Physical Broadcast Channel (PBCH) channel as a block referred to as Synchronization Signal Block (SSB) and may always be transmitted together. Each SSB block may occupy four Orthogonal Frequency Division Multiplexing (OFDM) symbols in the time domain, while in the frequency domain it may reserve 240 subcarriers or 20 Resource Blocks (RBs).

Figure 1 is a schematic diagram depicting a time and frequency representation of an SSB Block. The PSS may occupy the first OFDM symbol and span over 127 subcarriers. The SSS may be positioned in the third OFDM symbol and may span over 127 subcarriers. There may be 8 unused subcarriers below the SSS and 9 unused subcarriers above SSS. The PBCH may occupy two full OFDM symbols, the second and fourth, spanning 240 subcarriers, and the third OFDM symbol, spanning 48 subcarriers below and above the SSS. This may result in the PBCH occupying 576 subcarriers across three OFDM symbols, since 240+48+48+240 = 576. The PBCH DeModulation Reference Signal (DM-RS) may occupy 144 Resource Elements (REs), which may be understood to be one-fourth of the total REs and the remaining may be used for the PBCH payload, 576-144 = 432 REs.

Since in 5G beam-sweeping for PSS/SSS and PBCH may be required to be supported, SS burst sets may be defined. An SS burst set may be comprised of a set of SSBs, each SSB being potentially transmitted on a different beam. An SS burst set may consist of one or more SSBs, which may be transmitted in time-division multiplexing fashion.

As per 3GPP TS 38.213, v. 17.1.0, the SSB burst may be periodically transmitted with a periodicity of 5ms, 10ms, 20ms, 40ms, 80ms or 160ms. A UE may assume a default periodicity of 20ms during initial cell search or idle mode mobility. Figure 2 is a schematic diagram depicting a particular example of SSB periodicity. Based on frequency, the range number of SSB inside a burst may vary. In the case of Figure 2, the frequency range is 3-6 GHz. Hence, the maximum number of SSB in an SS burst set (L _ma x) is 8, with a periodicity of 20 ms, as indicated at the bottom of the Figure. In the example depicted, the SSB starting symbol indexes are 2, 8, 16, 22, 30, 36, 44 and 50. Existing static configuration frequent transmission may cause always ON SSB.

A 5G NR deployment may be carried out in the FR1 frequency range 1 , that is, 410-7125 GHz, and the FR2 frequency range 2, 24250-52600 GHz. Lower band deployment of Frequency 1 (FR1 ) bands may see 4-8 SSB candidates in an SSB burst, wherein each SSB may be understood to be a candidate of an SSB burst. In the FR2 range, the candidate count may go till 64. In the example depicted in Figure 2, the Subcarrier Spacing (SCS) is 15 kHz and the frequency carrier (fc) is between 3 GHz and 6 GHz.

The transmission pattern and periodicity of an SSB block may be informed via a Radio Resource Control (RRC) Information Element (IE) called ssb-periodicityServingCell. as follows:

ServingCellConfigCommonSIB ::= SEQUENCE { ssb-periodicityServingCell ENUMERATED { ms5, mslO, ms20, ms40, ms80, msl60,spare2, sparel }

}

In standalone 5G NR, the SSB periodicity may be configured via a System Information Block 1 (SIB1 ) message, while in Non-Non-standalone (NSA), the SSB periodicity may be configured in RRC Connection Reconfiguration in the Long-Term Evolution (LTE) anchor.

5G NR may be understood to provide flexibility in terms of a radio frame’s subcarrier spacings for user data and SSB. Table 1 summarizes the SSB numerologies and the corresponding operating bands, according to Table 5.4.3.3 from 3GPP TS 38.104, v. 17.5.0. Based on the numerology, the subcarrier spacing may be determined. Since, the SSB may always occupy 20 RBs in the frequency domain and there may be 12 subcarriers in each RB, each SSB may occupy a total of 240 subcarriers. In accordance with this, the bandwidth occupied by the SSB may be 240 * subcarrier spacing. Hence, based on the subcarrier bandwidth, the overall bandwidth consumed by an SSB burst may be calculated, as summarized in T able 1 . For instance, 15 kHz SCS may lead to 240*15 kHz = 3.6 MHz and 30 kHz SCS may lead to 240*30 kHz = 7.2 MHz, etc.

Table 1. SSB Bandwidth.

Static configuration of transmission of reference signals may lead to the reference signals being always transmitted. For example, as mentioned earlier, the existing static configuration of frequent transmission may cause always-ON SSB.

FR1 frequency bands may generally be used as a coverage layer for 5G deployment, and these bands may have smaller bandwidth in comparison to Frequency Range 2 (FR2). In the particular case of SSB, as mentioned in Table 1 , a significant amount of bandwidth may be used for the purpose of SSB in FR1 , which may be understood to correspond to a higher bandwidth reservation for signaling purposes, and a reduction in the available user plane bandwidth. For example, an n71 band (600 MHz) type deployment may have 20 MHz bandwidth, of which 3.6 MHz may be utilized for signaling alone, which means 18% of bandwidth may be utilized in SSB type signals alone, which may be understood to be a significant overhead. This may increase if the available bandwidth is reduced to 10 or 15 MHz. In case of FR2, although the bandwidth challenge may not be significant, the number of SSB transmissions becomes higher, as mentioned earlier. The number of SSBs in a single block may go up to 64 beams, resulting in considerable energy emittance.

While some efforts have been made to dynamically configure the periodicity of transmission of SSBs based on UEs geospatial performance statistics, such as in US20210297129A1 , US20200314738A1 and EP3845010A1 , in general, existing methods of transmission of reference signals may lead to wasted resources and reduced capacity of the communications system, which may significantly degrade its performance. Furthermore, energy resources in the devices may be depleted.

SUMMARY

As part of the development of embodiments herein, one or more challenges with the existing technology will first be identified and discussed.

In a stable environment with good radio conditions, the SSB requirement for synchronization and for reading mandatory system information may be sparser, and a fixed 20 ms periodicity may be required to be dynamically adjusted to match the condition. Based on the usage environment, e.g., indoor environment where UE mobility remains limited, transmission of reference signals, e.g., SSBs, may not be frequently required. For example, in massive Machine Type Communication (mMTC) and Internet of Things (loT) scenarios, the UE mobility may be minimal and the UEs may have low activity time. Deferring transmission of reference signals, such as SSBs, may save transmission at the base station side and monitoring at the UE side as well, which may result in battery saving at the UE side.

Current 3GPP recommendations may provide the range of periodicity of transmission of reference signals, but there is no proposal on how to adjust it as per varied environment demand. Current implementation follows a fixed periodicity, which does not leverage the measurements reported by a UE and hence, it does not provide a closed loop adjustment of periodicity.

According to the foregoing, it may be understood to be an object of embodiments herein to improve the handling of resources used for transmission of reference signals in a communications system so as to optimize the use of resources that may be used for user plane transmissions.

In accordance with this, it is an object of embodiments herein to improve the handling of a periodicity of transmission of one or more reference signals in a communications system.

According to a first aspect of embodiments herein, the object is achieved by a computer- implemented method, performed by a first node. The method is for handling a periodicity of transmission of one or more reference signals. The first node operates in a communications system. The first node determines the periodicity of transmission of the one or more reference signals by a first radio network node. The periodicity of transmission is based on one or more radio conditions and one or more indicators of a mobility of one or more devices served by the first radio network node. The first node provides an indication of the determined periodicity to the first radio network node or to another node operating in the communications system.

According to a second aspect of embodiments herein, the object is achieved by a computer-implemented method, performed by the communications system. The communications system comprises the first node and a second node. The method is for handling the periodicity of transmission of the one or more reference signals. The communication system obtains, by the second node, a first set of first information indicating a first set of the one or more first measurements from one or more first devices served by a second radio network node. The communication system also obtains, by the second node and using machine learning, based on the obtained first set of first information, a first respective predictive model of one or more first indications of a radio condition for the one or more first devices. The communication system additionally obtains, by the second node, a first set of second information indicating a first set of one or more second measurements from the one or more first devices served by the second radio network node. The communication system further obtains, by the second node and using machine learning, based on the obtained first set of second information, a second respective predictive model of one or more second indications. The one or more second indications indicate a mobility state of the one or more first devices. The communication system additionally obtains, by the second node and using machine learning, based on an obtained first set of one or more second indications, a third respective predictive model of one or more third indications. The one or more third indications are of the radio condition for the one or more first devices. The communication system further provides, by the second node, the respective predictive models to the first node. The communication system also determines, by the first node, and based on the respective predictive models, the periodicity of transmission of the one or more reference signals by the first radio network node. The periodicity of transmission is based on the one or more radio conditions and the one or more indicators of the mobility of one or more devices served by the first radio network node. The communication system further provides, by the first node, the indication of the determined periodicity to the first radio network node or to the another node operating in the communications system.

According to a third aspect of embodiments herein, the object is achieved by a computer- implemented method, performed by the first node. The first node is for handling the periodicity of transmission of the one or more reference signals. The first node operates in the communications system. The first node determines the periodicity of transmission of the one or more reference signals by the first radio network node. The periodicity of transmission is configured to be based on the one or more radio conditions and the one or more indicators of the mobility of the one or more devices configured to be served by the first radio network node. The first node also provide the indication of the periodicity configured to be determined, to the first radio network node or to the another node configured to operate in the communications system.

According to a fourth aspect of embodiments herein, the object is achieved by the communications system comprising the first node and the second node. The communications system is for handling the periodicity of transmission of the one or more reference signals. The communications system is further configured to obtain, by the second node, the first set of first information configured to indicate the first set of the one or more first measurements from the one or more first devices configured to be served by the second radio network node. The communications system is also configured to obtain, by the second node and using machine learning, based on the first set of first information configured to be obtained, the first respective predictive model of the one or more first indications of the radio condition for the one or more first devices. The communications system is further configured to obtain, by the second node, the first set of second information configured to indicate the first set of one or more second measurements from the one or more first devices configured to be served by the second radio network node. The communications system is additionally configured to obtain, by the second node and using machine learning, based on the first set of second information configured to be obtained, the second respective predictive model of the one or more second indications. The one or more second indications are configured to indicate the mobility state of the one or more first devices. The communications system is also configured to obtain, by the second node and using machine learning, based on the first set of one or more second indications configured to be obtained, the third respective predictive model of the one or more third indications. The one or more third indications are configured to be of the radio condition for the one or more first devices. The communications system is further configured to provide, by the second node, the respective predictive models to the first node. The communications system is additionally configured to determine, by the first node, and based on the respective predictive models, the periodicity of transmission of the one or more reference signals by the first radio network node. The periodicity of transmission is configured to be based on the one or more radio conditions and the one or more indicators of the mobility of one or more devices configured to be served by the first radio network node. The communications system is further configured to provide, by the first node, the indication of the periodicity configured to be determined to the first radio network node or to the another node configured to operate in the communications system.

According to a fifth aspect of embodiments herein, the object is achieved by a computer program, comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method performed by the first node. According to a sixth aspect of embodiments herein, the object is achieved by a computer- readable storage medium, having stored thereon the computer program, comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method performed by the first node.

According to a seventh aspect of embodiments herein, the object is achieved by a computer program, comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method performed by the communications system.

According to an eighth aspect of embodiments herein, the object is achieved by a computer-readable storage medium, having stored thereon the computer program, comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method performed by the communications system.

By determining the periodicity of transmission of the one or more reference signals, the first node may be enabled to determine if and how to adjust the periodicity of transmission of the one or more reference signals. Embodiments herein may be understood to enable a finegrained method to control the close loop management on the periodicity of transmission of the one or more reference signals, e.g., SSB, keeping the real network demand in consideration. For example, if the percentage of the one or more devices found as static or very slow moving may be over a certain threshold, in a particular cell, then that cell may become a candidate for adjustment of the periodicity of transmission of the one or more reference signals, e.g., SSB.

By determining the periodicity of transmission of the one or more reference signals, the first node may be enabled to identify the time frames to enlarge the duration of the periodicity of transmission of the one or more reference signals and hence, related slot count, thereby enabling to dynamically adopt the one or more reference signals. Based on slot count in time domain SSB may get repeated. Dynamically adjusting a delayed periodicity of transmission of the one or more reference signals, may not only free up physical resources but also reduce the energy transmission, thereby improving the spectral and energy efficiency.

Longer periodicities of transmission of the one or more reference signals, such as SSB, may help in reducing the ping-pong phenomenon where high and low RSRP values may be repeatedly measured, while shorter periodicities may facilitate faster cell search for the devices, but with an increased signaling load. Enabling a dynamic adjustment of longer and shorter transmission periodicity may be understood to have its tradeoffs for RSRP stability, HO success rate and attach times hence a periodicity of transmission of the one or more reference signals, e.g., SSB aware of the activity of the devices and the environment may ensure the optimized performance for signaling. By the first node then providing the indication of the determined periodicity to the first radio network node, or the another node, the first node may then provide a recommendation for the periodicity of transmission of the one or more reference signals. The provision of the recommendation may be in near real time at the granularity of the reported measurements by the one or more devices, instead of a reactive approach. By the first node providing the indication, the first node may identify the opportunities for delaying the transmission of the one or more reference signals while retracting back, in case of anomaly. The first radio network node, or the another node, may then be enabled to dynamically tune the periodicity for reduced frequent transmission resulting in, for example, lesser always ON signals and saving the bandwidth and energy requirement.

Before determining the periodicity, the periodicity, may be configured with a suitable value out of 3GPP specified ranges and/or values which may remain static. By performing the determination of the periodicity according to embodiments herein, the first node may be enabled to cater with network and UEs dynamic behavior, balancing faster cell search, stable RF measurements, such as RSRP, and using trade-off between longer/shorter periodicity of transmission of the one or more reference signals.

By obtaining the first set of first information, the communications system may be enabled to determine the first respective predictive model of the one or more first indications and thereby also be enabled to predict what the radio condition for the one or more first devices may be.

By obtaining the first set of second information, the communications system may be enabled to determine the second respective predictive model of one or more second indications and thereby be enabled to predict the mobility state of the one or more first devices.

By obtaining the first set of second information, the communications system may be enabled to determine the third respective predictive model of one or more third indications and thereby predict another aspect of what the radio condition for the one or more first devices may be.

By then providing the respective predictive models to the first node, the communications system may enable that the first node determines the periodicity of transmission of the one or more reference signals, and then provides the indication.

Embodiments herein may be particularly relevant for network performance when taken in reference to mMTC and/or the loT scenario. In such scenarios, the mobility of the devices may be understood to be minimal, and with low activity time. Hence, frequent transmission of the one or more reference signals, e.g., SSB, may be enabled to be controlled or modified in accordance with optimum resource utilization. On the side of the devices, embodiments herein may result in better battery life by enabling to defer transmission of the one or more reference signals, e.g., SSB for a delayed duration, thereby also suspending the measurement at side of the devices for the same duration.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of embodiments herein are described in more detail with reference to the accompanying drawings, according to the following description.

Figure 1 is a schematic diagram depicting a time and frequency representation of an SSB.

Figure 2 is a schematic diagram depicting a particular example of SSB periodicity.

Figure 3 is a schematic diagram illustrating a non-limiting example of a communications system, according to embodiments herein.

Figure 4 is a flowchart depicting embodiments of a method in a first node, according to embodiments herein.

Figure 5 is a flowchart depicting embodiments of a method in a communications system, according to embodiments herein.

Figure 6 is a flowchart depicting embodiments of a method performed according to embodiments herein.

Figure 7 is a schematic block diagram depicting a non-limiting example of a deployment that may be used according to embodiments herein.

Figure 8 is a schematic diagram depicting a non-limiting example of a method performed by a communications system, according to embodiments herein.

Figure 9 is a schematic diagram depicting some aspects of a method performed by the first node, according to embodiments herein.

Figure 10 is a schematic diagram depicting a non-limiting example, of deployment options, according to embodiments herein.

Figure 11 is a schematic block diagram illustrating two non-limiting examples, a) and b), of a first node, according to embodiments herein.

Figure 12 is a schematic block diagram illustrating two non-limiting examples, a) and b), of a communication system, according to embodiments herein.

DETAILED DESCRIPTION

Certain aspects of the present disclosure and their embodiments address one or more of the issues with the existing methods discussed in the summary section and provide solutions to the challenges discussed. Embodiments herein may be understood to relate in general to a dynamic adjustment of a periodicity of transmission of one or more reference signals, such as SSBs, based on device and the network behavior.

As a summarized overview, embodiments herein may be understood to relate to an approach for dynamic adjustment of a periodicity of transmission of one or more reference signals, such as SSBs, as per radio conditions and device mobility, rather than static configuration. For example, according to embodiments herein, if the radio environment suggests that a number of devices, e.g., UEs, may be in lower mobility state and with good radio conditions, then the decision of SSB transmission in delayed manner may be derived by a radio network node serving the device, that is, a base station. The determination may be performed based on device mobility and handover (HO) requirements, Radio Link Failure (RLF) probability, number of expected connections etc.

The radio network node may dynamically tune the periodicity for reduced frequent transmission, resulting in lesser always ON signals thereby saving the bandwidth and energy requirement. A consistent check may be understood to help to converge the periodicity to an earlier state if the radio conditions degrade or devices tend to move into a higher mobility state. This may be understood to result in gaining a better spectral efficiency and optimized energy efficiency.

Existing measurements from devices may be considered by the radio network node, and Machine Learning (ML) capabilities may be combined to predict the RLF probability and the connected users. A combination of multiple rules may help to determine the trigger point of delayed periodicity as well as the reversion point.

The categorization of cells a device may have been attached to, in relation with Radio Frequency (RF) performance with respect to location and traffic pattern, may help in deciding dynamic parameter configurations.

The embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which examples are shown. In this section, embodiments herein are illustrated by exemplary embodiments. It should be noted that these embodiments are not mutually exclusive. Components from one embodiment or example may be tacitly assumed to be present in another embodiment or example and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description.

Figure 3 depicts two non-limiting examples, in panels “a” and “b”, respectively, of a communications system 100, in which embodiments herein may be implemented. The communications system 100 may be understood to be a computer network. In example implementations, such as that depicted in the non-limiting examples of Figure 3, the communications system 100 may be a telecommunications network, sometimes also referred to as a cellular radio system, cellular network or wireless communications system. In some examples, the telecommunications network may comprise network nodes which may serve receiving nodes, such as wireless devices, with serving beams.

In some examples, the communications system 100 may for example be a network such as 5G system, or a newer system supporting similar functionality. The communications system 100 may also support other technologies, such as, for example, a Fourth Generation (4G) system, such as a Long-Term Evolution (LTE) network, e.g., LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), LTE Half-Duplex Frequency Division Duplex (HD- FDD), LTE operating in an unlicensed band, Wideband Code Division Multiple Access (WCDMA), Universal Terrestrial Radio Access (UTRA) TDD, Global System for Mobile communications (GSM) network, GSM/Enhanced Data Rate for GSM Evolution (EDGE) Radio Access Network (GERAN) network, Ultra-Mobile Broadband (UMB), EDGE network, network comprising of any combination of Radio Access Technologies (RATs) such as e.g. MultiStandard Radio (MSR) base stations, multi-RAT base stations etc., any 3rd Generation Partnership Project (3GPP) cellular network, Wireless Local Area Network/s (WLAN) or WiFi network/s, Worldwide Interoperability for Microwave Access (WiMax), IEEE 802.15.4-based low-power short-range networks such as IPv6 over Low-Power Wireless Personal Area Networks (6LowPAN), Zigbee, Z-Wave, Bluetooth Low Energy (BLE), or any cellular network or system. The communications system 100 may for example support a Low Power Wide Area Network (LPWAN). LPWAN technologies may comprise Long Range physical layer protocol (LoRa), Haystack, SigFox, LTE-M, and Narrow-Band loT (NB-loT).

Although terminology from Long T erm Evolution (LTE)/5G has been used in this disclosure to exemplify the embodiments herein, this should not be seen as limiting the scope of the embodiments herein to only the aforementioned system. Other wireless systems support similar or equivalent functionality may also benefit from exploiting the ideas covered within this disclosure. In future telecommunication networks, e.g., in the sixth generation (6G), the terms used herein may need to be reinterpreted in view of possible terminology changes in future technologies.

The communications system 100 may comprises a plurality of nodes, whereof a first node 111 , a second node 112 and a another node 113 are depicted in Figure 3.

Any of the first node 11 1 , the second node 112 and the another node 113 may be understood, respectively, as a first computer system, a second computer system and a third computer system. In some examples, any of the first node 11 1 , the second node 112 and the another node 113 may be implemented as a standalone server in e.g., a host computer in the cloud. Any of the first node 11 1 , the second node 112 and the another node 113 may in some examples be a distributed node or distributed server, with some of their respective functions being implemented locally, e.g., by a client manager, and some of its functions implemented in the cloud, by e.g., a server manager. Yet in other examples, any of the first node 11 1 , the second node 1 12 and the another node 1 13 may also be implemented as processing resources in a server farm.

In some embodiments, any of the first node 111 , the second node 1 12 and the another node 113 may be independent and separated nodes. In other embodiments, any of the first node 11 1 , the second node 1 12 and the another node 1 13 may be co-located or be the same node. All the possible combinations are not depicted in Figure 3 to simplify the Figure.

Any of the first node 1 11 and the second node 1 12 may be understood to be nodes having a capability to run Machine Learning (ML) procedures. In particular examples of embodiments herein, the second node 1 12 may be a node having a capability to train ML models. The first node 11 1 may be a node which may have a capability to execute ML models, which it may have trained itself, or which it may have obtained from another node, e.g., the second node 1 12.

The another node 1 13 may be a node having a capability to manage and/or implement changes in a periodicity of transmission of one or more reference signals.

The communications system 100 further comprises one or more devices 130. The communications system 100 may further comprise one or more first devices 131 . Any of the one or more devices 130 and the one or more first devices 131 may be also known as e.g., user equipment (UE), a wireless device, mobile terminal, wireless terminal and/or mobile station, mobile telephone, cellular telephone, or laptop with wireless capability, or a Customer Premises Equipment (CPE), just to mention some further examples. Any of the one or more devices 130 and the one or more first devices 131 in the present context may be, for example, portable, pocket-storable, hand-held, computer-comprised, or a vehicle-mounted mobile device, enabled to communicate voice and/or data, via a RAN, with another entity, such as a server, a laptop, a Personal Digital Assistant (PDA), or a tablet computer, sometimes referred to as a tablet with wireless capability, or simply tablet, a sensor, a Machine-to-Machine (M2M) device, a device equipped with a wireless interface, such as a printer or a file storage device, modem, Laptop Embedded Equipped (LEE), Laptop Mounted Equipment (LME), Universal Serial Bus (USB) dongles or any other radio network unit capable of communicating over a radio link in the communications system 100. Any of the one or more devices 130 may be wireless, i.e., it may be enabled to communicate wirelessly in the communications system 100 and, in some particular examples, may be able support beamforming transmission. The communication may be performed e.g., between two devices, between a device and a radio network node, and/or between a device and a server. The communication may be performed e.g., via a RAN and possibly one or more core networks, comprised, respectively, within the communications system 100.

Any of the one or more devices 130 and the one or more first devices 131 may be served by a respective radio network node 140 comprised in the communications system 100. The respective radio network node 140 may be one of a first radio network node 141 and a second radio network node 142 comprised in the communications system 100 and depicted in the non-limiting examples depicted in Figure 3. Any of the first radio network node 141 and the second radio network node 142 may typically be a base station or Transmission Point (TP), or any other network unit capable to serve a wireless device or a machine type node in the communications system 100. Any of the first radio network node 141 and the second radio network node 142 may be e.g., a 5G gNB, a 4G eNB, or a radio network node in an alternative 5G radio access technology, e.g., fixed or WiFi. Any of the first radio network node 141 and the second radio network node 142 may be e.g., a Wide Area Base Station, Medium Range Base Station, Local Area Base Station and Home Base Station, based on transmission power and thereby also coverage size. Any of the first radio network node 141 and the second radio network node 142 may be a stationary relay node or a mobile relay node. Any of the first radio network node 141 and the second radio network node 142 may support one or several communication technologies, and its name may depend on the technology and terminology used. Any of the first radio network node 141 and the second radio network node 142 may be directly connected to one or more networks and/or one or more core networks.

The communications system 100 covers a geographical area which may be divided into cell areas, wherein each cell area may be served by a radio network node, although, one radio network node may serve one or several cells.

The one or more devices 130 may be served by the first radio network node 141 . The one or more first devices 131 may be served by the second radio network node 142.

In some embodiments, any of the first radio network node 141 and the second radio network node 142 may be independent and separated nodes. In other embodiments, any of the first node 11 1 , the second node 1 12 and the another node 113 may be co-located or be the same node. All the possible combinations are not depicted in Figure 3 to simplify the Figure. In particular non-limiting examples, such as that depicted in panel b) of Figure 3, the first radio network node 141 may be co-located or be the same node as the second radio network node 142.

The first node 1 11 may communicate with the second node 1 12 over a first link 151 . The first node 1 11 may communicate with the another node 1 13 over a second link 152. The first node 1 11 may communicate with the first radio network node 141 over a third link 153. The second node 1 12 may communicate with the second radio network node 142 over a fourth link 154. The first radio network node 141 may communicate with the one or more devices 131 over a respective fifth link 155. The second radio network node 142 may communicate with the one or more first devices 131 over a respective sixth link 156.

Any of the links just described may be, e.g., a radio link or a wired link, and may be a direct link or it may go via one or more computer systems or one or more core networks in the communications system 100, or it may go via an optional intermediate network. The intermediate network may be one of, or a combination of more than one of, a public, private or hosted network; the intermediate network, if any, may be a backbone network or the Internet, which is not shown in Figure 3.

In general, the usage of “first”, “second”, “third”, “fourth”, “fifth” and/or “sixth” herein may be understood to be an arbitrary way to denote different elements or entities, and may be understood to not confer a cumulative or chronological character to the nouns they modify.

Embodiments of a computer-implemented method, performed by the first node 11 1 , will now be described with reference to the flowchart depicted in Figure 4. The method may be understood to be for handling a periodicity of transmission of one or more reference signals. The first node 1 11 operates in the communications system 100.

The method may comprise the actions described below. In some embodiments all the actions may be performed. In some embodiments some of the actions may be performed. In Figure 4, optional actions are indicated with a dashed box. One or more embodiments may be combined, where applicable. All possible combinations are not described to simplify the description. It should be noted that the examples herein are not mutually exclusive. Components from one example or embodiment may be tacitly assumed to be present in another example or embodiment and it will be obvious to a person skilled in the art how those components may be used in the other examples or embodiments.

Action 401

Embodiments herein may be understood to follow an approach for dynamic computing of the periodicity of transmission of the one or more reference signals based on radio conditions reported and statistics measured on a mobility of devices, rather than being based over a static configuration. For example, if a radio environment of the devices suggests that residing devices may be in a lower mobility state with good radio conditions, then the radio network node serving the devices may decide to transmit the one or more reference signals in a delayed manner.

In some embodiments, the one or more reference signals may be Synchronization Signal Bursts (SSBs). Other examples of the one or more reference signals may be Channel State Information-Reference Signal (CSI-RS), Physical Downlink Shared Channel (PDSCH) DeModulation Reference Signal (DM-RS), Tracking Reference Signal (TRS), and Wide Band (WB) Channel Quality Indicator (CQI)

In order to ultimately achieve the goal of dynamically computing the periodicity of transmission of the one or more reference signals, in this Action 401 , the first node 11 1 may obtain first information indicating one or more first measurements from the one or more devices 130. The one or more first measurements may be understood to be based on reference signals, e.g., on one or more first reference signals, that may be transmitted by radio network nodes in the surrounding area, which may include any serving radio network node and neighbor radio network nodes. The one or more devices 130 may be understood as a group of devices operating in the communications system during a certain period of time.

As per standards proposed by 3GPP, to have awareness of radio conditions observed by the one or more devices 130, the network, that is the first radio network node 141 , e.g., an eNB in for example NSA Mode, or a gNB in for example SA Mode, may have previously delivered measurement configurations to the one or more devices 130 based on a mobility trigger and policy settings. The measurement configuration may have been included in an RRC Reconfiguration message or an RRC Resume message. For each measurement configuration, a Reporting configuration or ReportConfig may be defined. Each reporting configuration may comprise the following: a reporting criterion, a reference signal type and a reporting format. The reporting criterion may be understood to be the criterion that may trigger any of the one or more devices 130 to send a measurement report. This may either be periodical or based on a triggered event. The reference signal type may be understood to be the reference signal that the one or more devices 130 may use for beam and cell measurement results, e.g., SS/PBCH block or Channel State Information-Reference Signal (CSI-RS). The reporting format may be understood to refer to the quantities, that is, metrics, per cell and per beam that the one or more devices 130 may include in the measurement report, e.g., Reference Signal Received Power (RSRP).

The one or more first measurements may be reported for measurement type, for example, as SSB or CSI-RS, relating to measurement object.

The one or more first measurements may comprise SS-RSRP, SS- Reference Signal Received Quality (RSRQ), SS- Signal to Interference Noise Ratio (SINR), SS- Received Signal Strength Indicator (RSSI).

If measurement reporting is configured as event-triggered, in that case, a Measurement reporting (MR) based on event triggers may be reported if any defined event may have been encountered, such as, for example any event from Table 2, which lists NR measurement events. Event Type Description

Event A1 Serving cell becomes better than threshold

Event A2 Serving cell becomes worse than threshold

Event A3 Neighbor NR cell becomes offset better than serving cell

Serving cell becomes worse than threshold 1 and neighbor cell better than Event A5 Threshold 2

Event A6 Neighbor cell becomes offset better than Secondary cell (Scell)

Table 2.

In accordance with the foregoing, in some embodiments, the one or more first measurements may be of a first type. The first type of measurements may be event triggered, in other words, triggered by occurrence of an event. The one or more first measurements may indicate occurrence of one or more of: an Event A1 , an Event A2, an Event A3 and an Event A5.

According to the foregoing, the one or more first measurements may be e.g., an RSRP measurement of a reference signal transmitted by the first radio network node 141 , the serving radio network node. The first information may then comprise a measurement report from the device having performed the measurement, reporting occurrence of Event A1 .

The obtaining in this Action 401 may be understood as receiving, e.g., via the third link 153, and optionally the respective fifth link 155, or retrieving, e.g., from a memory.

By obtaining the first information in this Action 401 , the first node 11 1 may then be enabled to determine a radio condition for the one or more devices 130, as described in the next Action 402.

Action 402

In this Action 402, the first node 11 1 may determine, based on the obtained first information, one or more first indications of a radio condition for the one or more devices 130.

The one or more first indications may be understood as one or more results of calculations performed using the obtained first information. Event A1 may be understood to be an indication that the radio condition in the serving cell may be good enough to sustain the performance. A2 and A3 events may be understood to indicate that the serving cell may be encountering the degraded radio conditions, and the respective device of the one or more devices 130 may need to switch to another cell.

According to embodiments herein, a ratio of A1/A2/A3 and A5 events may be used to determine the environment. A sustainable environment from event triggered measurements may be assumed. A sustainable environment may be understood to indicate that the serving cell may be good enough to handle UE sessions. UEs may not require HO in general, as the ratio of events A2/A3/A5 is lower. This may be understood to mean that UEs with low mobility may remain on the same serving cell.

In particular embodiments, one of the one or more first indications may indicate whether or not the following condition may be met:

A1 Event UE Ratio> K*(A2 Event UE Ratio+A3 Event UE Ratio+A5 Event UE Ratio), wherein K may be understood to be a constant value and “UE Ratio” may be understood to indicate a respective subset of the one or more devices 130 experiencing the respective Event.

The constant K may be used to provide a weight factor as per different scenarios and clutters. A clutter may be understood to indicate what type of terrain on demography, the serving respective network node 140 may be at, e.g., urban, rural, dense urban etc. Scenario may be understood to refer to indoor or enterprise type 5g connection or MMTC or IOT etc

Determining may be understood as calculating, deriving, estimating or similar. The one of the one or more first indications may be determined using machine learning, based on a respective predictive model, e.g., a first predictive model. The first predictive model may be for predicting a stability of the radio condition. For the first predictive model, the data extraction, modelling and inference during the training of the model may be performed in near real time, e.g., by a Radio Access Network (RAN) Intelligence Controller (RIC).

The first predictive model may use as input one or more of: periodicity of the one or more reference signals, pattern of the one or more reference signals, e.g., Case A/B/C/D/E, bandwidth part (BWP) type BWP Identifier (ID) of the BWP deployed frequency band, BAND and Bandwidth of the frequency which may be being used to carry all information including reference signals, the one or more reference signals NR-Absolute Radio-Frequency Channel Number (ARFCN), an ID of the one or more reference signals or Beam index, a serving cell type, e.g., Primary cell (PCell) or Secondary Cell (SCell), SS-RSRP, SS- Reference Signal Received Quality (RSRQ), SS- Signal to Interference Noise Ratio (SINR), SS- Received Signal Strength Indicator (RSSI), numerology, measurement period, measurement type, SSB, CSI-RS, PDSCH DM-RS, TRS, and WB Channel Quality Indicator (CQI), that is, the average received channel quality.

In some embodiments, the determining in this Action 402 may be performed in real time. By determining the one or more first indications in this Action 402, the first node 1 11 may be enabled to know what the radio condition for the one or more devices 130 may be.

Action 403

In this Action 403, the first node 1 11 may obtain second information indicating one or more second measurements from the one or more devices 130 served by the first radio network node 141.

The one or more second measurements may be understood to be also based on reference signals, e.g., on the one or more first reference signals or other reference signals, that may be transmitted by radio network nodes in the surrounding area, which may include any serving radio network node and neighbor radio network nodes.

The one or more second measurements may comprise, e.g., Event A1 , A2, A3, A4, A5, A6, B1 and/or B2.

In some embodiments, the one or more second measurements may be of a second type. The one or more second measurements may indicate periodic measurements.

In case the measurement reporting may be periodic such as in the case of the one or more second measurements, the content from Table 3 may be used to decide the health of the radio environment. As indicated in Table 3, reportQuantityCell may particularly indicate RSRP/ RSRQ/SINR, which may be understood to give an overview of the radio condition.

Table 3.

According to the foregoing, the one or more second measurements may be e.g., an RSRQ measurement of a reference signal transmitted by the first radio network node 141 , the serving radio network node. The second information may then comprise a measurement report from the device having performed the measurement, comprising for example, reportQuantityCell. The obtaining in this Action 403 may be understood as receiving, e.g., via the third link 153, and optionally the respective fifth link 155, or retrieving, e.g., from a memory.

By obtaining the second information in this Action 403, the first node 1 11 may then be enabled to determine a mobility state of the one or more devices 130, as described in the next Action 404.

Action 404

In this Action 404, the first node 1 11 may determine based on the obtained second information, one or more second indications. The one or more second indications may indicate a mobility state of the one or more devices 130.

In some embodiments, at least one of the following options may apply. According to a first option, at least one of the one or more second indications may indicate whether or not a number of handovers in time t may be smaller than a first threshold. The first threshold may be understood to be a threshold criterion, which may be referred to herein as an example, Low Observed Threshold (LOT). Since in periodic event reporting there may be understood to be no explicit identification of events, a separate step of device mobility state may be taken in this Action 404 to determine if high mobility state devices may exist in the cell serving area. A device may be determined to be a static device, or a very slow-moving device if the number of handovers in time t is smaller than the first threshold, wherein the first threshold is the LOT. This calculation may be performed in Action 404 to determine the mobility state of the one or more devices 130 in connected mode, while in idle mode, the number of cell reselections in time t may be used.

According to a second option, at least one of the one or more second indications may indicate velocity information. The classification of the one or more devices 130 as moving or static may be considered in accordance with the cells the one or more devices 130 may have been attached to, keeping the measurements with routing information. For example, the one or more second indications may comprise a serving radio network Cell ID changed to multiple new sells within certain duration. Keeping the measurements and routing information may be understood to be aimed at forming a virtual path by considering cells to which a device may get connected while moving. For example, a specific path may follow a Celli 1 -Cell21 -Cell31 sequence. Measurement reports may tell the best cell along with the best neighbors, which indirectly may help to determine a specific route the device may be covering.

The one of the one or more second indications may be determined using machine learning, based on a respective predictive model, e.g., a second predictive model.

In some embodiments, the determining in this Action 404 may be performed in real time. In particular examples, the determining in this Action 404 may be performed in Near Real Time by a RIC. The second predictive model may be for predicting the mobility of the one or more devices 130. For the second predictive model, the training of the model may be performed in non-real time, e.g., by a RAN RIC.

By determining the one or more second indications in this Action 404, the first node 1 11 may then be enabled to determine if and how to adjust the periodicity of transmission of the one or more reference signals. For example, if the percentage of the one or more devices 130 found as static or very slow moving may be over a certain threshold, mobility threshold “M”, in a particular cell, then that cell may become a candidate for adjustment of the periodicity of transmission of the one or more reference signals, e.g., SSB. M may be understood to be a threshold that may be tuned as per clutter type.

Action 405

In this Action 405, the first node 1 11 may determine, based on the obtained one or more second indications, one or more third indications. The one or more third indications may be of the radio condition for the one or more devices 130. The one or more third indications may be understood to be different than the one or more first indications.

Based on all periodic reporting, to determine the radio condition to be good, the one or more third indications may have to meet the criteria that a percentage of the one or more devices 130 meeting a state in which a serving cell may have better or equal radio conditions than a best neighbor by a configured margin during a period of time, which may be expressed as e.g., Best_Thresh_Neigh_Suspend For Neigh_Suspend_Time, may be larger than a second threshold, “II”. Best_Thresh_Neigh_Suspend may be understood to be larger or equal to a Best neighbour RSRP plus a delta decibel-milliwatts (dbm). To have stable radio where handovers may be understood to be least needed, the Best neighbor RSRP may be overestimated by the delta constant, which may be tuned as needed. Best_Thresh_Neigh_Suspend may be understood to indicate the state in which the serving cell may have better or equal radio conditions, e.g., RSRP, in comparison to a reported best neighbor RSRP, by a configured delta dbm margin.

The second threshold “U” may be understood as a threshold and may be adjusted. Neigh_Suspend_Time may be understood as a timer threshold for which Best_Thresh_Neigh_Suspend may need to hold true.

Accordingly, in some embodiments, at least one of the one or more third indications may indicate whether or not the percentage of the one or more devices 130 meeting the state in which the serving cell may have better or equal radio conditions than the best neighbor by the configured margin during the period of time may exceed the second threshold. That is, whether or not % UEs meeting Best_Thresh_Neigh_Suspend For Neigh_Suspend_Time > U %, wherein U is the second threshold.

In some embodiments, at least one of the following options may apply. According to a first option, as stated earlier, at least one of the one or more second indications may indicate whether or not the number of handovers in time t may be smaller than the first threshold. According to a second option, as stated earlier, at least one of the one or more second indications may indicate velocity information. According to a third option, at least one of the one or more third indications may indicate whether or not the percentage of the one or more devices 130 meeting the state in which the serving cell may have better or equal radio conditions than the best neighbor by the configured margin during the period of time may exceed the second threshold. That is, whether or not % UEs meeting Best_Thresh_Neigh_Suspend For Neigh_Suspend_Time > U %, wherein U is the second threshold.

The one or more third indications may be determined with the proviso the one or more second indications may indicate a number of the one or more devices 130 determined to be static or have mobility below a third threshold, e.g., the mobility threshold “M”, may be above a fourth threshold. The fourth threshold may be referred to herein as “X”.

The one of the one or more third indications may be determined using machine learning, based on a respective predictive model, e.g., a third predictive model. The third predictive model may be for predicting a different aspect of the stability of the radio condition. For the third predictive model, the data extraction, modelling and inference during the training of the model may be performed in near real time, e.g., by a RIC.

In some embodiments, the determining in this Action 405 may be performed in real time.

By determining the one or more third indications in this Action 405, the first node 11 1 may then be enabled to determine where any of the one or more devices 130 may have a risk of experiencing RLF, as described in the next Action 406.

Action 406

In case of an RLF, affected devices may need to perform a synchronization process. Hence, such devices may require detecting the one or more reference signals, and in such instance, delayed reference signals, e.g., a delayed SSB, may cause an induced delay in the attach process. Since picking such time frames or cells may cause performance degradation, the first node 1 1 1 may need to perform an RLF probability determination.

In this Action 406, the first node 1 11 may determine, based on the determined one or more third indications, a set of the one or more devices 130 having a risk of experiencing RLF. The set of the one or more devices 130 may be determined as a percentage of the one or more devices 130, e.g., UE%. To evaluate RLF, the radio conditions, as measured for example as the SS_RSRP of the serving cell and neighbour cells, the Uplink (UL) RSSI may be leveraged in terms of historical data to establish a relationship with the ongoing number of Downlink (DL) Radio link Control (RLC) retransmissions exceeding a threshold. The relationship may be utilized to model ongoing DL RLC retransmissions exceeding a threshold , along with predicted radio conditions to determine the expected DL RLC retransmissions using machine learning capabilities to identify the set of the one or more devices 130, e.g., as a UE%, which may encounter RLF.

The one of the one or more third indications may be determined using machine learning, based on a respective predictive model, e.g., a fourth predictive model. The fourth predictive model may be for predicting a probability of RLF for the one or more devices 130. For the fourth predictive model, the training of the model may be performed in non-real time, e.g., by a RAN RIC.

In some embodiments, the determining in this Action 406 may be performed in real time.

By receiving determining the set of the one or more devices 130 having a risk of experiencing RLF in this Action 406, the first node 1 11 may then be enabled to take this information into account to predict how many devices may request to establish a connection with radio network node, as will be described in the next Action 407.

Action 407

In this Action 407, the first node 1 11 may determine, based on the determined set of the one or more devices 130 having the risk of experiencing RLF, a predicted number of requests, by the one or more devices 130, to establish a connection with a respective radio network node 140. The connection may be understood to be a radio connection.

A respective machine learning model, e.g., a fifth predictive model, may be used to predict the expected attach requests, or the number of RRC connection requests, based on historical available data. For the fifth predictive model, the training of the model may be performed in non- real time, e.g., by a RAN RIC.

The fifth predictive model may use as input one or more of the Radio Access Channel (RACH) type, a RACH reason, a RACH grant, a Random Access (RA) response window, and a RA Radio Network Temporary Identifier (RNTI).

Table 4 shows the attributes to be considered for modelling across the respective threshold criterion considered.

Table 4.

By predicting the number of requests in this Action 407, the first node 1 11 may be enabled to determine if and how to adjust the periodicity of transmission of the one or more reference signals, as will be discussed in the next Action 408.

Action 408

In this Action 408, the first node 1 11 may determine the periodicity of transmission of the one or more reference signals by the first radio network node 141. The periodicity of transmission may be based on one or more radio conditions and one or more indicators of a mobility of the one or more devices 130 served by the first radio network node 141 .

The one or more radio conditions may be indicated by at least one of: the one or more first indications, the one or more third indications, the determined set, and the predicted number of requests.

Any of the one or more second indications may be considered an example of the one or more indicators of the mobility.

In some embodiments, the periodicity may be determined based on the determined one or more first indications.

In some embodiments, the periodicity may be determined based on the determined one or more second indications.

In some embodiments, the periodicity may be determined based on the determined one or more third indications.

Existing measurements, e.g., UE measurements, reported to the first radio network node 141 and ML capabilities may be combined to predict the RLE probability and connected users. Combination of multiple rules may help to determine the trigger point of delayed periodicity as well as the reversion point. After observation of a stable radio environment, wherein the serving cell may be considered to be good, and there may be no expectation of any degradation, so that the devices may be understood to not require frequency synchronization, instances where the one or more reference signals may be deferred may be identified based on least RLF probability and expected connections.

In some embodiments, the periodicity may be determined based on the determined set in Action 406. A cell may become a candidate for adjustment of the periodicity of transmission of the one or more reference signals, e.g., SSB, if the probability of RLF may be smaller than another threshold, e.g., “R”, where R may be understood to be a tunable threshold.

In some embodiments, the periodicity may be determined based on the predicted number of requests. For example, if the expected attach requests or RRC connection requests are less than a configurable threshold, e.g., “C”, then the periodicity may be adjusted, e.g., for example from SSB PeriodicityServingCell to 2* SSB PeriodicityServingCell. This may be understood to mean that e.g., if in frame 0, the first 5 ms subframe TO are used for the first one or more reference signals, e.g., an SSB block, then, as per default, the SSB-periodicityServingcell may mean that the T20 subframe may again be the starting point of a new SSB block. But if the above conditions are met, then the SSB-periodicityServingcell may now be the T40 subframe.

The determining in this Action 408 may be performed using ML, utilizing measurements performed by the one or more devices 130 combined with ML capabilities. A respective machine learning model, e.g., a sixth predictive model, may be used to predict the expected periodicity.

In some embodiments, at least one of the one or more first indications, the one or more second indications, the one or more third indications, the set of the one or more devices 130, the predicted number of requests, and the periodicity may be determined at least one of: using machine learning, based on a respective predictive model, and in real time.

The determining of the respective predictive model may further comprise, for the same, at least partially the same or other one or more devices 130, one of the following two options. According to a first option, the determining of the respective predictive model may comprise a) autonomously obtaining the respective predictive model by: i) training the respective predictive model during a training phase by providing input to the respective predictive model, ii) testing a respective accuracy of the trained respective predictive model during a testing phase, and iii) iterating the training phase and the testing phase until the respective accuracy exceeds a respective fifth threshold. According to a second option, the determining of the respective predictive model may comprise receiving one or more of the respective predictive models from the second node 112 operating in the communications system 100.

ML Modeling

The following description in reference to the ML modelling may be understood to apply to any of the respective predictive models described herein.

Embodiments herein may be employed in different type of RAN deployments whether it may be traditional Radio Access Network (RAN), Cloud-RAN or Open (O)-RAN.

Since the embodiments herein may be understood to aim to utilize ML capabilities under both traditional and O-RAN type of scenarios, ML training and inference may be comprised as a part of the process. In some embodiments, O-RAN and various Artificial Intelligence (AI)/ML may be utilize deployment options indicated in Table 5. Embodiments herein may use the ML training and inference options, which may suit the need of the use case. Table 5 summarizes the various tasks and roles that may be performed by the different network functions.

Here, an ML training host may be understood to refer to a network function which may host the training of the respective model, including offline and online training while an ML inference host may house the respective ML model during inference mode, which may include both the model execution, as well as any online learning if applicable.

In Option A, ML model training may be considered in the Non-RT RIC, and ML model inference may be considered in near Real Time (RT) RIC. In Option B, ML model training and inference may be happening in non-RT RIC. Option C may correspond to federated learning, where a non-RT RIC may serve as a central server, and its connected Near-RT RICs may serve as distributed AI/ML entities. Under Option D, continuous operation/model management/data preparation/ML training host may be in non-RT RIC (nRT-RIC) while O-CU/O-DU act as the ML inference host. In particular examples of this option D, continuous operation/model management/data preparation/ML training host may be performed or handled by the second node 112, while the ML inference host may be run by the first node 1 11.

Table 5.

In general, the following sequence may be assumed as part of overall ML flow. Based on the selected deployment option, the attributes mentioned earlier may be collected over the 01 interface between the SMO and O-CU/DU entities and an initial respective offline model may be trained in the Service Management and Orchestration (SMO)/Non-RT RIC. If inference is in near RT-RIC, then the respective offline trained model or the backup model may be moved to the Near-RT RIC. Based on deployment option and inference host, the AI/ML model may be deployed to the ML inference host, which may be the Near-RT RIC, Non- RT-RIC or Centralized Unit (CU)/Distributed Unit (DU). The O-CU/O-DU data for inference may be collected in an inference host via appropriate an interface. The ML inference host may perform inference using the deployed model and collected O-CU/O-DU data. The ML inference host may enforce control action/guidance via an appropriate interface. If there is online training involved, then O-CU/O-DU data for online learning may be collected over an appropriate interface. The ML inference host may provide performance feedback to the ML training host. An updated and well-performing model may be added to a model repository.

The proposed options to be considered for modelling and inferencing may be Option-A and option-C in case near RT-RIC data extraction may be available in the network infrastructure. Option-B and option-D may be to be considered when modelling and inferencing may have to be performed at a Non-RT-RIC.

The decision of having a specific network function as a training or inference host may be typically decided based on one or more of the following criteria: the availability of data across a given interface, the cost of data movement, latency considerations, compute resource availability considerations, and the local versus general significance of the data.

Embodiments herein may enable a user to have predicted dataset samples, that is, augmented dataset samples, by inherently using the training network architecture. Any of the respective predictive models referred to herein may be, for example, Recurrent Neural Network (RNN), Gated recurrent Unit(GRU), Long short term memory (LSTM), etc..

By determining the periodicity of transmission of the one or more reference signals in this Action 408, the first node 1 11 may be enabled to identify the time frames to enlarge the duration of the periodicity of transmission of the one or more reference signals and hence, related slot count, thereby enabling to dynamically adopt the one or more reference signals. Since each SSB may be understood to occupy 4 slots in the time domain, when the SSB periodicity is changed hence the slot count dedicated for it may be understood to also change. Dynamically adjusting a delayed periodicity of transmission of the one or more reference signals, may not only free up physical resources but also reduce the energy transmission, thereby improving the spectral and energy efficiency, as required by the user.

Before performing Action 408, the periodicity, may be configured with a suitable value out of 3GPP specified ranges and/or values which may remain static. By performing Action 408, the first node 1 11 may be enabled to cater with network and UEs dynamic behavior, balancing faster cell search, stable RF measurements, such as RSRP, using trade-off between longer/shorter periodicity of transmission of the one or more reference signals.

Action 409

In this Action 409, the first node 1 11 may provide an indication of the determined periodicity to the first radio network node 141 or to the another node 113 operating in the communications system 100.

Providing in this Action 409 may be understood as sending our outputting, and may be performed via the third link 153 to the first radio network node 141 , or via the second link 152 to the another node 113.

By the first node 1 11 providing the indication, the first node 1 11 may provide a recommendation for the periodicity of transmission of the one or more reference signals. The first radio network node 141 may then be enabled to dynamically tune the periodicity for reduced frequent transmission resulting in lesser always ON signals and saving the bandwidth and energy requirement. The provision of the recommendation may be in near real time at the granularity of the reported measurements by the one or more devices 130, instead of a reactive approach. By the first node 11 1 providing the indication, the first node 111 may identify the opportunities for delaying the transmission of the one or more reference signals while retracting back, in case of anomaly.

The method just described may be repeated periodically to dynamically adjust the determined periodicity. For example, in provided example of the T20 to the T40 shift, this delayed periodicity may be maintained for the next P periodic cycles, where P may be a threshold, and may be adjusted unless there may be any breach in the above conditions. In such case, the periodicity may be rolled back to the previous stage. If any particularly relevant condition occurs, such as high number of HO, higher number of RLF, degraded radio conditions etc,, then periodicity may be rolled back to the 20 ms periodicity. In provided example of the T20 to the T40 shift, the next one or more reference signals, e.g., SSB, may be expected on the T80 subframe, and post that, at the T120 subframe. However, if, for example, the UE ratio of static devices goes down from the prescribed threshold, or e.g., the RLF probability increases beyond the respective threshold, then after the T80 subframe, the next one or more reference signals, e.g., SSB, may be transmitted at the T100 subframe instead of at the T 120 subframe.

Similarly, if conditions hold for P=3 cycles of periodicity of the one or more reference signals, e.g., T40, T80 and T120, then the periodicity of the one or more reference signals may be increased to 80 ms. That is, the next one or more reference signals may be transmitted at the T200 subframe instead of at the T 160 subframe.

At the T200 subframe, if the RLF probability goes beyond a certain threshold, e.g., 50%, then the periodicity may be rolled back to 20 ms rather than going back to 40 ms. This dynamic adjustment may keep on adjusting the periodicity.

Embodiments of a computer-implemented method performed by the communications system 100 will now be described with reference to the flowchart depicted in Figure 5. The method may be understood to be for handling the periodicity of transmission of the one or more reference signals.

The method may comprise the following actions. In some embodiments all the actions may be performed. In some embodiments some of the actions may be performed. In Figure 5, optional actions are indicated with a dashed box. One or more embodiments may be combined, where applicable. All possible combinations are not described to simplify the description. It should be noted that the examples herein are not mutually exclusive. Components from one example or embodiment may be tacitly assumed to be present in another example or embodiment, and it will be obvious to a person skilled in the art how those components may be used in the other examples.

The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first node 1 11 and will thus not be repeated here to simplify the description. For example, the one or more reference signals may be SSBs. Action 501

In the communications system 100, the second node 1 12 may be understood to be a node performing the training of the respective predictive models described in relation to the first node 11 1 , whereas the first node 1 11 may be a node executing the respective predictive models, as trained by the second node 1 12.

In this Action 501 , the method performed by the communications system 100 comprises obtaining, by the second node 112, a first set of the first information indicating a first set of the one or more first measurements from one or more first devices 131 served by the second radio network node 142.

This Action 501 may be understood to have a description corresponding to that provided for Action 401 , during a training phase. The one or more first devices 131 may be understood to be those one or more devices whose data, e.g., measurements and/or attributes may be used during the training phase. The one or more first devices 131 may be the same as the one or more devices 130, a subset of it, may partially overlap with the one or more devices 130 or may be entirely different from the one or more devices 130.

The first set of the first information may be understood to have a description corresponding to that provided for the first information in Action 401 , during the training phase of the respective first predictive model.

Similarly, the first set of the one or more first measurements may be understood to have a description corresponding to that provided for the one or more first measurements in Action 401 , during the training phase of the respective first predictive model.

The obtaining in this Action 501 may be understood as receiving, e.g., via the fourth link 154, e.g., and optionally the respective sixth link 156, or retrieving, e.g., from a memory.

In some embodiments, the one or more first measurements may be of the first type. The one or more first measurements may indicate the occurrence of one or more of: the Event A1 , the Event A2, the Event A3 and the Event A5.

In some embodiments, one of the one or more first indications may indicate whether or not:

A1 Event UE Ratio> K*A2 Event UE Ratio+A3 Event UE Ratio+A5 Event UE Ratio, wherein K may be understood to be the constant value and UE Ratio may be understood to indicate a respective subset of the one or more first devices 131 experiencing the respective Event

Action 502

In this Action 502, the method performed by the communications system 100 comprises obtaining, by the second node 1 12 and using machine learning, based on the obtained first set of first information, the first respective predictive model of the one or more first indications of the radio condition for the one or more first devices 131 .

This Action 502 may be understood to have a description corresponding to that provided for Action 402, during the training phase.

Obtaining may be understood in this Action 505 as generating and or training.

Action 503

In this Action 503, the method performed by the communications system 100 comprises obtaining, by the second node 112, a first set of the second information indicating a first set of the one or more second measurements from the one or more first devices 131 served by the second radio network node 142.

This Action 503 may be understood to have a description corresponding to that provided for Action 403, during the training phase. The first set of the second information may be understood to have a description corresponding to that provided for the second information in Action 403, during the training phase of the respective second predictive model.

Similarly, the first set of the one or more second measurements may be understood to have a description corresponding to that provided for the one or more second measurements in Action 403, during the training phase of the respective first predictive model.

The obtaining in this Action 503 may be understood as receiving, e.g., via the fourth link 154, e.g., and optionally the respective sixth link 156, or retrieving, e.g., from a memory.

Action 504

In this Action 504, the method performed by the communications system 100 comprises obtaining, by the second node 1 12 and using ML, based on the obtained first set of second information, the second respective predictive model of the one or more second indications. The one or more second indications indicate the mobility state of the one or more first devices 131 .

This Action 504 may be understood to have a description corresponding to that provided for Action 404, during the training phase.

Obtaining may be understood in this Action 504 as generating and or training.

Action 505

In this Action 505, the method performed by the communications system 100 comprises obtaining, by the second node 112 and using machine learning, based on an obtained first set of one or more second indications, the third respective predictive model of the one or more third indications. The one or more third indications are of the radio condition for the one or more first devices 131. The first set of one or more second indications may have been obtained by the second node 112.

This Action 505 may be understood to have a description corresponding to that provided for Action 405, during the training phase.

Obtaining may be understood in this Action 505 as generating and or training.

In some embodiments, the obtaining in Action 502, in Action 504, and/or in Action 505 of the respective predictive model may further comprise, for the one or more first devices 131 , one of: a) autonomously obtaining the respective predictive model by: i) training the respective predictive model during the training phase by providing input to the respective predictive model, ii) testing the respective accuracy of the trained respective predictive model the testing phase, and iii) iterating the training phase and the testing phase until the respective accuracy exceeds the respective fifth threshold.

Action 506

In this Action 506, the method performed by the communications system 100 comprises providing, by the second node 112, the respective predictive models to the first node 1 11.

Providing may be understood as sending, e.g., via the first link 151 , or outputting.

Action 507

In this Action 507, the method performed by the communications system 100 may comprise obtaining, by the first node 1 11 , the second set of first information indicating the second set of the one or more first measurements from the one or more devices 130 served by the first radio network node 141 .

“Second set” may be referred to herein as that set obtained during an execution phase of the method, once the respective predictive models may have been trained, and they may then be respectively applied.

This Action 507 may be understood to have a description corresponding to that provided for Action 401 , during the execution phase.

Action 508

In this Action 508, the method performed by the communications system 100 may comprise determining, by the first node 11 1 , based on the obtained second set of first information and using the first respective predictive model, the one or more first indications of the radio condition for the one or more devices 130. The periodicity may later be determined based on the determined one or more first indications.

This Action 508 may be understood to have a description corresponding to that provided for Action 402, during the execution phase.

Action 509

In this Action 509, the method performed by the communications system 100 may comprise obtaining, by the first node 11 1 , a second set of second information indicating a second set of one or more second measurements from the one or more devices 130 served by the first radio network node 141 .

This Action 509 may be understood to have a description corresponding to that provided for Action 403, during the execution phase.

In some embodiments, the method performed by the first node 1 11 may comprise performing Action 509 and one of Action 510 and Action 51 1 .

Action 510

In this Action 510, the method performed by the communications system 100 may comprise determining 510, by the first node 1 11 , based on the obtained second set of second information and using the second respective predictive model, the one or more second indications. The one or more second indications may indicate the mobility state of the one or more devices 130. The periodicity may later be determined based on the determined one or more second indications.

This Action 510 may be understood to have a description corresponding to that provided for Action 404, during the execution phase.

Action 511

In this Action 51 1 , the method performed by the communications system 100 may comprise determining, by the first node 11 1 , based on the obtained second set of one or more second indications and using the third respective predictive model, the one or more third indications. The one or more third indications may be of the radio condition for the one or more devices 130. The periodicity may be determined based on the determined one or more third indications.

This Action 511 may be understood to have a description corresponding to that provided for Action 405, during the execution phase.

In some embodiments, the second set of one or more second measurements may be of the second type. The second set of the one or more second measurements may indicate the periodic measurements. At least one of the following options may apply. According to the first option, a) at least one of the one or more second indications may indicate whether or not the number of handovers in time t may be smaller than the first threshold. According to the second option, b) at least one of the one or more second indications may indicate the velocity information. According to the third option, c) at least one of the one or more third indications may indicate whether or not the percentage of the one or more devices 130 meeting the state in which the serving cell may have better or equal radio conditions than the best neighbor by the configured margin during the period of time may exceed the second threshold. That is, whether or not % of UEs meeting Best_Thresh_Neigh_Suspend For Neigh_Suspend_Time > U %, wherein U may be understood to be the second threshold.

The one or more third indications may be determined with the proviso the one or more second indications may indicate the number of the one or more devices 130 determined to be static or have mobility below the third threshold may be above the fourth threshold.

Action 512

In this Action 512, the method performed by the communications system 100 may comprise determining, by the first node 1 11 , based on the determined one or more third indications, the set of the one or more devices 130 having the risk of experiencing radio link failure. The periodicity may be determined based on the determined set.

This Action 512 may be understood to have a description corresponding to that provided for Action 406, during the execution phase.

Action 513

In this Action 513, the method performed by the communications system 100 may comprise determining, by the first node 1 11 , based on the determined set of the one or more devices 130 having the risk of experiencing radio link failure, the predicted number of requests, by the one or more devices 130, to establish the connection with the respective radio network node 140. The periodicity may later be determined based on the predicted number of requests.

This Action 513 may be understood to have a description corresponding to that provided for Action 407, during the execution phase.

In some embodiments, at least one of the set of the one or more devices 130 and the predicted number of requests, may be determined at least using machine learning, based on the respective predictive model.

The obtaining 502, 504, 505 of the respective predictive model may further comprise, for the one or more first devices 131 , one of: a) autonomously obtaining the respective predictive model by: i) training the respective predictive model during the training phase by providing input to the respective predictive model, ii) testing the respective accuracy of the trained respective predictive model during the testing phase, and iii) iterating the training phase and the testing phase until the respective accuracy may exceed the respective fifth threshold.

Any of the other respective predictive models may be obtained by equivalent actions i), ii) and iii), as described in the previous paragraph.

At least one of the one or more first indications, the one or more second indications, the one or more third indications, the set of the one or more devices 130, the predicted number of requests, and the periodicity may be determined in real time.

Action 514

In this Action 514, the method performed by the communications system 100 comprises determining, by the first node 1 11 , and based on the respective predictive models, the periodicity of transmission of the one or more reference signals by the first radio network node 141 . The periodicity of transmission is based on one or more radio conditions and the one or more indicators of the mobility of one or more devices 130 served by the first radio network node 141 .

This Action 514 may be understood to have a description corresponding to that provided for Action 408, during the execution phase.

Action 515

In this Action 515, the method performed by the communications system 100 comprises providing, by the first node 11 1 , the indication of the determined periodicity to the first radio network node 141 or to the another node 1 13 operating in the communications system 100.

This Action 515 may be understood to have a description corresponding to that provided for Action 409, during the execution phase.

Figure 6 is a signalling diagram depicting a non-limiting example of the sequence in which the actions of the method performed by the first node 11 1 may be executed according to embodiments herein. While the reference numbers indicated in Figure 6 are used in relation to the Actions as described for Figure 4, it may be understood that the Actions may also relate to those described in Figure 5 for the method performed by the communications system 100, as corresponding to the indicated Actions from Figure 4. The reference numbers from the Actions in Figure 5 are not included in Figure 6 to avoid cluttering the Figure. Particularly, Figure 6 shows the overall flow of the proposed approach in case of a traditional RAN system. In the non-limiting example of Figure 6, the one or more reference signals are SSB. The first phase may be assumed if the RAN type is traditional RAN. The flow may start with the measurement reporting, in accordance with Action 401 and Action 403. In accordance with Action 401 , the measurement reporting may be event triggered. This may lead to the calculation of the UE% for A1 , A2 and A3 events, in accordance with Action 402. Subsequently, according to the same action, the determination of whether the A1 UE Ratio may be larger than K* (A2 UE Ratio+A3 UE Ratio) may be performed. In accordance with Action 403, the measurement reporting may be periodic. According to Action 404, the first node 11 1 may determine if the percentage of devices, e.g., UE, with low mobility state are larger than a set threshold “M”. If the answer is “No”, the first node 1 11 may wait until the next periodic measurement report may be received. If the answer is “Yes”, the first node 111 , in accordance with Action 405, may calculate the percentage of devices meeting the Best_Thresh_Neigh_Suspend For Neigh_Suspend_Time. If the percentage exceeds a certain threshold “X”, and if the A1 UE Ratio may be larger than K* (A2 UE Ratio+A3 UE Ratio), next, according to Action 406, the first node 1 11 may perform an RLE probability evaluation (RL Prob), and determine, also in accordance with Action 406, if the probability of RLF is inferior to a determined threshold “R%”. If that is the case, indicated in Figure 6 by the “Yes” option, the first node 111 may then predict, in accordance with Action 407, the predicted number of requests by the one or more devices 130 to establish a connection with a respective radio network node 140, or number of expected attachments. If, also in accordance with Action 407, the predicted number of expected attachments or connection requests is lower than a respective threshold “C%”, indicated in Figure 6 by the “Yes” option, the first node 111 may then, in accordance with Action 408, set the SSB PeriodicityServingCell to 2*SSB_PeriodicityServingCell, thereby adjusting the periodicity to lower its frequency of transmission.

Figure 7 is a schematic block signalling diagram depicting a non-limiting example of a O- RAN type deployment that may be used according to embodiments herein for a cloud implementation, e.g., in a combination of distributed and centralized cloud and operator specific premises-based servers. The O-RAN Alliance defines O-Cloud as a cloud computing platform comprised of a collection of physical infrastructure nodes that may meet O-RAN requirements to host the relevant O-RAN functions, the supporting software components, and the appropriate management and orchestration functions. The Non-RT RIC 701 may be understood to enable non-real-time control and optimization of RAN elements and resources, it may include AI/ML workflow including model training and updates, and policy-based guidance of applications/features in the Near-RT RIC 702. This may relate to Near RT-RIC 702 with A1 Interface, which may be understood to be between the Non-RT RIC 701 in the SMO 703 and the Near-RT RIC 702 for RAN Optimization. The functionality of the Non-RT RIC 701 may be directly responsible for driving what may be sent and received across the A1 interface. The Non- RT RIC 701 may allow applications to run on it. These applications may be called “rApps”, where ‘r’ may be understood to stand for RAN. An rApp 704 is depicted in Figure 7. The Non-RT RIC 701 may expose SMO 703 Framework functions to “rApps” via a set of “rApps” Services Exposure” functions over the R1 interface. As the R1 interface may be the only interface between an “rApps” and the functionality of the Non-RT RIC 701 and the SMO 703, and may be defined to meet all functional needs of rApps, with appropriate interface extensibility capabilities as needed. The Near-RT RIC 702 may be understood to be a logical function that may enable near-real-time control and optimization of RAN elements and resources via fine-grained data collection and actions over an E2 interface. The E2 interface may enable a direct association between the “xApp” and the RAN functionality. Embodiments herein for dynamic configuration of the periodicity of transmission of the one or more reference signals may be based on the measurements reported by the one or more devices 130, in terms of xApp, to be deployed at Near RT-RIC 702 in association with model training performed at the Non-RT RIC 701 , using data measurements collected from E2 Nodes, O-RAN Central Unit Control Plane (O-CU-CP) 705, O-RAN Central Unit User Plane (O-CU-UP) 706, O-RAN Distributed Unit (O-DU) 707 etc.. E2 nodes may be understood to refer to nodes which may be connected to near RT RIC. It may be O-CU/O-DU or eNodeB. Further details on how the O-RAN type of deployment may be used in embodiments herein may be found above in relation to the description provided for Table 5.

Figure 8 is yet another signalling diagram depicting a non-limiting example of the proposal flow in case of O-RAN type deployment and in accordance with embodiments herein. In this non-limiting example, the second node 112 manages a Non-RT RIC 701 , and the first node 1 11 manages a Near RT-RIC 702. The Non-RT RIC 701 , in accordance with Action 501 , may capture L1 RSRP and the connected users from the O-DU 707 via the SMO 703. The Non-RT RIC 701 , in accordance with Action 503, may also capture enrichment information for UE position and velocity from an Application Server. Based on the captured information, the rApp 704 may then train to predict the RSRP in accordance with Action 502, the UE mobility, in accordance with Action 504, and the location and connected users. Once the respective predictive models are trained, they are transferred, along with policy and enrichment information, to the Near RT-RIC 702, in accordance with Action 506. Enrichment information may be supplementary information, e.g., location information of a device, which may be received by either an external server or may be internal to network. Policy may be understood to refer to a guiding factor to near RT RIC. On the basis of policy, the near RT RIC may decide what action may have to be performed. Next, during the execution phase of the models, the Near RT-RIC 702, in accordance with Action 401 , may capture L1 RSRP at a Nx100 ms granularity from the O-DU 707, and may perform inference for policy meeting criteria at 801 . The description of the flowchart depicted at 801 corresponds to that provided for the corresponding actions in Figure 6 and will not be repeated here. Figure 9 is a further signalling diagram depicting a non-limiting example of how different actions may be performed leading to Action 406, according to embodiments herein. To evaluate RLF, a) the radio conditions, as measured for example as the SS_RSRP of the serving cell and neighbour cells, and obtained in accordance with Action 403, b) the Uplink (UL) RSSI, also obtained in accordance with Action 403, may be leveraged, also in accordance with Action 403, in terms of historical data to establish a relationship with the ongoing number of Downlink (DL) Radio link Control (RLC) retransmissions exceeding a threshold. The relationship may be utilized to model ongoing DL RLC retransmissions exceeding a threshold, along with to determine the expected DL RLC retransmissions using machine learning capabilities to identify, in accordance with Action 406, the set of the one or more devices 130, e.g., as a UE%, which may encounter RLF. The modelling, e.g., using an ML regression model, may be performed using as well whether or not at least one of the one or more second indications may indicate whether or not the number of handovers in time t may be larger than the first threshold, in this example a High Observed Threshold (HOT). Since in periodic event reporting there may be understood to be no explicit identification of events, a separate step of device mobility state may be taken in Action 404 to determine the mobility state of the one or more devices 130 in connected mode, while in idle mode the number of cell reselections in time t may be used. The modelling, e.g., using an ML regression model, may be performed using as well, in accordance with Action 405, at least one of the one or more third indications indicate whether or not the Best_Thresh_Neigh_Suspend may be larger or equal to the Best neighbor RSRP plus the delta dbm.

Figure 10 is a schematic block signalling diagram depicting a non-limiting example of a O-RAN type deployment that may be used according to embodiments herein the ML training and inference options, which may be used according to embodiments herein, as described earlier in relation to Table 5. Here, an ML training host 1001 may be understood to refer to a network function which may host the training of the respective model, including offline and online training. The ML training host 1001 may be managed, for example, by the second node 112. An ML inference host, depicted in Figure 10 as the ML Model Host/ Actor 1002 may house the respective ML model during inference mode, which may include both the model execution, as well as any online learning if applicable. The ML inference host 1002 may be managed, for example, by the first node 1 11. The ML Model Host/ Actor 1002 may act as inference host. In Option A, ML model training may be considered in the Non-RT RIC 701 , and ML model inference may be considered in the near Real Time (RT) RIC 702. In Option B, ML model training and inference may be happening in non-RT RIC 701. Option C may correspond to federated learning, where a non-RT RIC 701 may serve as a central server, and its connected Near-RT RICs 702 may serve as distributed AI/ML entities. Under Option D, a continuous operation/model management/data preparation/ML training host 1001 may be in non-RT RIC 701 , while the O-CU/O-DU 707 may act as the ML inference host. In particular examples of this option D, the continuous operation/model management/data preparation/ML training host 1001 may be managed by the second node 1 12, while the ML inference host 1002 may be run by the first node 11 1.

As a summarized overview of the foregoing, embodiments herein may be understood to enable a fine-grained method to control the close loop management on the periodicity of transmission of the one or more reference signals, e.g., SSB, keeping the real network demand in consideration. Embodiments herein may thereby enable to free up physical resources but also reduce the energy transmission , hence improving the spectral and energy efficiency by enabling to dynamically adjust a delayed periodicity of the one or more reference signals, e.g., SSB. This may be understood to be since, for example, in 5G, always on signal may be enabled to be reduced, and an SSB block in 5G may be understood to occupy more resource blocks, which are 20 Physical Resource Blocks (PRBs), than in LTE which are 6 PRBs. Longer periodicities of transmission of the one or more reference signals, such as SSB, may help in reducing the ping-pong phenomenon where high and low RSRP values may be repeatedly measured, while shorter periodicities may facilitate faster cell search for the devices, but with an increased signaling load. Enabling a dynamic adjustment of longer and shorter transmission periodicity may be understood to have its tradeoffs for RSRP stability, HO success rate and attach times hence a periodicity of transmission of the one or more reference signals, e.g., SSB aware of the activity of the devices and the environment may ensure the optimized performance for signaling. Embodiments herein may be particularly relevant for network performance when taken in reference to mMTC and/or the loT scenario. In such scenarios, the mobility of the devices may be understood to be minimal, and with low activity time. Hence, frequent transmission of the one or more reference signals, e.g., SSB, may be enabled to be controlled or modified in accordance with optimum resource utilization. On the side of the devices, embodiments herein may result in better battery life by enabling to defer transmission of the one or more reference signals, e.g., SSB for a delayed duration, thereby also suspending the measurement at side of the devices for the same duration.

Figure 11 depicts two different examples in panels a) and b), respectively, of the arrangement that the first node 1 11 may comprise to perform the method actions described above in relation to Figure 4 and/or any of Figures 5-10. In some embodiments, the first node 11 1 may comprise the following arrangement depicted in Figure 11a. The first node 1 11 may be understood to be for handling the periodicity of transmission of the one or more reference signals. The first node 11 1 is configured to operate in the communications system 100.

Several embodiments are comprised herein. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. In Figure 11 , optional boxes are indicated by dashed lines. The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the first node 1 11 and will thus not be repeated here. For example, the one or more reference signals may be configured to be SSBs.

The first node 1 11 is configured to, e.g., by means of a determining unit 1101 within the first node 1 11 configured to, determine the periodicity of transmission of the one or more reference signals by the first radio network node 141 . The periodicity of transmission may be configured to be based on the one or more radio conditions and the one or more indicators of the mobility of the one or more devices 130 configured to be served by the first radio network node 141 .

In some embodiments, the first node 11 1 is further configured to, e.g., by means of a providing unit 1102 within the first node 11 1 configured to, provide the indication of the periodicity configured to be determined, to the first radio network node 141 , or to the another node 113 configured to operate in the communications system 100.

The first node 11 1 may be further configured to, e.g., by means of an obtaining unit 1103 within the first node 1 11 configured to, obtain the first information configured to indicate the one or more first measurements from the one or more devices 130.

In some embodiments, the first node 1 11 may be further configured to, e.g., by means of the determining unit 1101 within the first node 111 configured to, determine, based on the first information configured to be obtained, the one or more first indications of the radio condition for the one or more devices 130. The periodicity may be configured to be determined based on the one or more first indications configured to be determined.

The one or more first measurements may be configured to be of the first type. The one or more first measurements may be configured to indicate the occurrence of one or more of: the Event A1 , the Event A2, the Event A3 and the Event A5.

In some embodiments, the one of the one or more first indications may be configured to indicate whether or not: A1 Event UE Ratio> K*A2 Event UE Ratio+A3 Event UE Ratio+A5 Event UE Ratio. In such embodiments, K may be configured to be a constant value and UE Ratio may be configured to indicate the respective subset of the one or more devices 130 configured to be experiencing the respective Event. In some embodiments, the first node 11 1 may be further configured to, e.g., by means of the obtaining unit 1103 within the first node 11 1 configured to, obtain the second information configured to indicate the one or more second measurements from the one or more devices 130 configured to be served by the first radio network node 141 . In some of such embodiments, the first node 11 1 may be further configured to one of the following two options.

According to a first option, in some embodiments, the first node 1 11 may be further configured to, e.g., by means of the determining unit 1101 within the first node 11 1 configured to, determine, based on the second information configured to be obtained, one or more second indications, the one or more second indications being configured to indicate a mobility state of the one or more devices 130, and wherein the periodicity is configured to be determined based on the one or more second indications configured to be determined.

According to a second option, in some embodiments, the first node 1 11 may be further configured to, e.g., by means of the determining unit 1101 within the first node 11 1 configured to, determine, based on the obtained one or more second indications, the one or more third indications. The one or more third indications may be configured to be of the radio condition for the one or more devices 130. The periodicity may be configured to be determined based on the one or more third indications configured to be determined.

In some embodiments, the one or more second measurements may be configured to be of the second type. The one or more second measurements may be configured to indicate periodic measurements, and at least one of the following may apply. In some embodiments, a) at least one of the one or more second indications may be configured to indicate whether or not the number of handovers in time t is smaller than the first threshold. In some embodiments, b) at least one of the one or more second indications may be configured to indicate velocity information. In some embodiments, c) at least one of the one or more third indications may be configured to indicate whether or not the percentage of the one or more devices 130 meeting the state in which the serving cell may have better or equal radio conditions than the best neighbor by the configured margin during the period of time exceeds the second threshold.

In some embodiments, the one or more third indications may be configured to be determined with the proviso the one or more second indications may indicate the number of the one or more devices 130 configured to be determined to be static or have mobility below the third threshold may be above the fourth threshold.

In some embodiments, the first node 1 11 may be further configured to, e.g., by means of the determining unit 1 101 within the first node 1 11 configured to, determine, based on the one or more third indications configured to be determined, the set of the one or more devices 130 having the risk of experiencing RLF. The periodicity may be configured to be determined based on the set configured to be determined. In some embodiments, the first node 1 11 may be further configured to, e.g., by means of the determining unit 1 101 within the first node 111 configured to, determine, based on the set of the one or more devices 130 configured to be determined as having the risk of experiencing RLF, the predicted number of requests, by the one or more devices 130 to establish the connection with the respective radio network node 140. The periodicity may be configured to be determined based on the number of requests configured to be predicted.

In some embodiments, the first node 1 11 may be configured to be repeated periodically to dynamically adjust the periodicity configured to be determined.

In some embodiments, at least one of the one or more first indications, the one or more second indications, the one or more third indications, the set of the one or more devices 130, the predicted number of requests, and the periodicity may be configured to be determined at least one of: a) using machine learning, based on the respective predictive model, and b) in real time.

In some embodiments, the determining of the respective predictive model may be further configured to comprise, for the same, at least partially the same or other one or more devices 130, one of: a) autonomously obtaining the respective predictive model by: i) training the respective predictive model during the training phase by providing input to the respective predictive model, ii) testing the respective accuracy of the trained respective predictive model during the testing phase, and iii) iterating the training phase and the testing phase until the respective accuracy may exceed the respective fifth threshold, and b) receiving the one or more of the respective predictive models from the second node 1 12 configured to operate in the communications system 100.

The embodiments herein may be implemented through one or more processors, such as a processor 1104 in the first node 1 11 depicted in Figure 1 1 , together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the in the first node 11 1. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the first node 11 1.

The first node 1 11 may further comprise a memory 1105 comprising one or more memory units. The memory 1 105 is arranged to be used to store obtained information, store data, configurations, schedulings, and applications etc. to perform the methods herein when being executed in the first node 1 11. In some embodiments, the first node 11 1 may receive information from, e.g., the second node 112, the another node 113, the respective radio network node 140, the first radio network node 141 , the second radio network node 142, the one or more devices 130, the one or more first devices 131 , and/or another node or device, through a receiving port 1106. In some examples, the receiving port 1106 may be, for example, connected to one or more antennas in the first node 1 11. In other embodiments, the first node 1 11 may receive information from another structure in the communications system 100 through the receiving port 1 106. Since the receiving port 1 106 may be in communication with the processor 1 104, the receiving port 1 106 may then send the received information to the processor 1104. The receiving port 1106 may also be configured to receive other information.

The processor 1104 in the first node 11 1 may be further configured to transmit or send information to e.g., the second node 1 12, the another node 1 13, the respective radio network node 140, the first radio network node 141 , the second radio network node 142, the one or more devices 130, the one or more first devices 131 , another node or device and/or another structure in the communications system 100, through a sending port 1107, which may be in communication with the processor 1104, and the memory 1 105.

Those skilled in the art will also appreciate that the units 1101 -1103 described above may refer to a combination of analog and digital circuits, and/or one or more processors configured with software and/or firmware, e.g., stored in memory, that, when executed by the one or more processors such as the processor 1104, perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application- Specific Integrated Circuit (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC).

The units 1 101 -1 103 described above may be the processor 1104 of the first node 1 11 , or an application running on such processor.

Thus, the methods according to the embodiments described herein for the first node 1 11 may be respectively implemented by means of a computer program 1108 product, comprising instructions, i.e., software code portions, which, when executed on at least one processor 1104, cause the at least one processor 1 104 to carry out the actions described herein, as performed by the first node 111. The computer program 1108 product may be stored on a computer- readable storage medium 1109. The computer-readable storage medium 1 109, having stored thereon the computer program 1108, may comprise instructions which, when executed on at least one processor 1 104, cause the at least one processor 1104 to carry out the actions described herein, as performed by the first node 11 1. In some embodiments, the computer- readable storage medium 1109 may be a non-transitory computer-readable storage medium, such as a CD ROM disc, a memory stick, or stored in the cloud space. In other embodiments, the computer program 1 108 product may be stored on a carrier containing the computer program, wherein the carrier is one of an electronic signal, optical signal, radio signal, or the computer-readable storage medium 1 109, as described above.

The first node 11 1 may comprise an interface unit to facilitate communications between the first node 11 1 and other nodes or devices, e.g., the second node 112, the another node 1 13, the respective radio network node 140, the first radio network node 141 , the second radio network node 142, the one or more devices 130, the one or more first devices 131 , another node or device and/or another structure in the communications system 100. In some particular examples, the interface may, for example, include a transceiver configured to transmit and receive radio signals over an air interface in accordance with a suitable standard.

In other embodiments, the first node 11 1 may comprise the following arrangement depicted in Figure 11 b. The first node 1 11 may comprise a processing circuitry 1104, e.g., one or more processors such as the processor 1104, in the first node 1 11 and the memory 1105. The first node 1 11 may also comprise a radio circuitry 1110, which may comprise e.g., the receiving port 1 106 and the sending port 1 107. The processing circuitry 1 104 may be configured to, or operable to, perform the method actions according to Figure 4 and/or any of Figures 5-10, in a similar manner as that described in relation to Figure 1 1 a. The radio circuitry 1 110 may be configured to set up and maintain at least a wireless connection with the second node 1 12, the another node 1 13, the respective radio network node 140, the first radio network node 141 , the second radio network node 142, the one or more devices 130, the one or more first devices 131 , another node or device and/or another structure in the communications system 100.

Hence, embodiments herein also relate to the first node 1 11 , e.g., operative for handling the periodicity of transmission of the one or more reference signals, the first node 11 1 being operative to operate in the communications system 100. The first node 111 may comprise the processing circuitry 1104 and the memory 1 105, said memory 1 105 containing instructions executable by said processing circuitry 1104, whereby the first node 11 1 is further operative to perform the actions described herein in relation to the first node 1 11 , e.g., in Figure 4 and/or any of Figures 5-10.

Figure 12 depicts two different examples in panels a) and b), respectively, of the arrangement that the communications system 100 may comprise to perform the method actions described above in relation to Figure 5 and/or any of Figures 6-10. In some embodiments, the communications system 100 may comprise the following arrangement depicted in Figure 12a. The communications system 100 may be understood to be for handling the periodicity of transmission of the one or more reference signals. The communications system 100 is configured to comprise the first node 1 11 and the second node 112.

Several embodiments are comprised herein. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. In Figure 12, optional boxes are indicated by dashed lines. The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the communications system 100 and will thus not be repeated here. For example, the one or more reference signals may be configured to be SSBs.

The communications system 100 is configured to, e.g. by means of an obtaining unit 1201 within the second node 1 12 configured to, obtain, by the second node 112, the first set of first information configured to indicate the first set of the one or more first measurements from the one or more first devices 131 configured to be served by the second radio network node 142.

The communications system 100 is also configured to, e.g. by means of the obtaining unit 1201 within the second node 112 configured to, obtain, by the second node 1 12 and using machine learning, based on the first set of first information configured to be obtained, the first respective predictive model of the one or more first indications of the radio condition for the one or more first devices 131 .

The communications system 100 is also configured to, e.g. by means of the obtaining unit 1201 within the second node 1 12 configured to, obtain, by the second node 1 12, the first set of second information configured to indicate the first set of the one or more second measurements from the one or more first devices 131 configured to be served by the second radio network node 142.

The communications system 100 is also configured to, e.g. by means of the obtaining unit 1201 within the second node 112 configured to, obtain, by the second node 112 and using machine learning, based on the first set of second information configured to be obtained, the second respective predictive model of the one or more second indications. The one or more second indications may be configured to indicate the mobility state of the one or more first devices 131.

The communications system 100 is also configured to, e.g. by means of the obtaining unit 1201 within the second node 112 configured to, obtain, by the second node 1 12 and using machine learning, based on the first set of one or more second indications configured to be obtained, the third respective predictive model of the one or more third indications. The one or more third indications may be configured to be of the radio condition for the one or more first devices 131. The communications system 100 is further configured to, e.g. by means of a providing unit 1202 within the second node 1 12 configured to, provide, by the second node 112, the respective predictive models to the first node 11 1.

The communications system 100 is further configured to, e.g. by means of the determining unit 1101 within the first node 1 11 configured to, determine, by the first node 11 1 , and based on the respective predictive models, the periodicity of transmission of the one or more reference signals by the first radio network node 141 . The periodicity of transmission may be configured to be based on the one or more radio conditions and the one or more indicators of the mobility of the one or more devices 130 configured to be served by the first radio network node 141 .

The communications system 100 is further configured to, e.g. by means of the providing unit 1102 within the first node 1 11 configured to, provide, by the first node 1 11 , the indication of the periodicity configured to be determined, to the first radio network node 141 , or to the another node 1 13 configured to operate in the communications system 100.

The communications system 100 may be further configured to, e.g. by means of the obtaining unit 1103 within the first node 1 11 configured to, obtain, by the first node 1 11 , the second set of the first information configured to indicate the second set of the one or more first measurements from the one or more devices 130 configured to be served by the first radio network node 141 .

The communications system 100 may be further configured to, e.g. by means of the determining unit 1101 within the first node 11 1 configured to, determine, by the first node 1 11 , based on the second set of the first information configured to be obtained and using the first respective predictive model, the one or more first indications of the radio condition for the one or more devices 130. The periodicity may be configured to be determined based on the one or more first indications configured to be determined.

In some embodiments, the one or more first measurements may be configured to be of the first type. The one or more first measurements may be configured to indicate the occurrence of one or more of: the Event A1 , the Event A2, the Event A3 and the Event A5.

In some embodiments, the one of the one or more first indications may be configured to indicate whether or not: A1 Event UE Ratio> K*A2 Event UE Ratio+A3 Event UE Ratio+A5 Event UE Ratio. In such embodiments, K may be configured to be the constant value and UE Ratio may be configured to indicate the respective subset of the one or more first devices 131 , or the respective subset of the one or more devices 130, configured to be experiencing the respective Event.

The communications system 100 may be further configured to, e.g. by means of the obtaining unit 1 103 within the first node 11 1 configured to, obtain, by the first node 111 , the second set of the second information configured to indicate the second set of the one or more second measurements from the one or more devices 130 configured to be served by the first radio network node 141. In some of such embodiments, the communications system 100 may be further configured to one of the following two options.

According to a first option, in some embodiments, the communications system 100 may be further configured to, e.g. by means of the determining unit 1 101 within the first node 111 configured to, determine, by the first node 1 11 , based on the second set of second information configured to be obtained, and using the second respective predictive model, the one or more second indications. The one or more second indications may be configured to indicate the mobility state of the one or more devices 130. The periodicity may be configured to be determined based on the one or more second indications configured to be determined.

According to a second option, in some embodiments, the communications system 100 may be further configured to, e.g. by means of the determining unit 1101 within the first node 11 1 configured to, determine, by the first node 11 1 , based on the second set of one or more second indications configured to be obtained and using the third respective predictive model, the one or more third indications. The one or more third indications may be configured to be of the radio condition for the one or more devices 130. The periodicity may be configured to be determined based on the one or more third indications configured to be determined.

In some embodiments, the second set of the one or more second measurements may be configured to be of the second type. The second set of one or more second measurements may be configured to indicate periodic measurements, and at least one of the following three options may apply. In some embodiments, a) at least one of the one or more second indications may be configured to indicate whether or not the number of handovers in time t is smaller than the first threshold. In some embodiments, b) at least one of the one or more second indications may be configured to indicate velocity information. In some embodiments, c) at least one of the one or more third indications may be configured to indicate whether or not the percentage of the one or more devices 130 meeting the state in which the serving cell may have better or equal radio conditions than the best neighbor by the configured margin during the period of time exceeds the second threshold.

In some embodiments, the one or more third indications may be configured to be determined with the proviso the one or more second indications may indicate the number of the one or more devices 130 configured to be determined to be static or have mobility below the third threshold may be above the fourth threshold.

The communications system 100 may be further configured to, e.g. by means of the determining unit 1101 within the first node 1 11 configured to, determine, by the first node 11 1 , based on the one or more third indications configured to be determined, the set of the one or more devices 130 having the risk of experiencing RLF. The periodicity may be configured to be determined based on the set configured to be determined.

The communications system 100 may be further configured to, e.g. by means of the determining unit 1101 within the first node 1 11 configured to, determine, by the first node 1 11 , based on the set of the one or more devices 130 configured to be determined as having the risk of experiencing RLF, the predicted number of requests, by the one or more devices 130 to establish the connection with the respective radio network node 140. The periodicity may be configured to be determined based on the number of requests configured to be predicted.

In some embodiments, at least one of the set of the one or more devices 130 and the predicted number of requests may be configured to be determined at least using machine learning, based on a respective predictive model.

In some embodiments, the actions configured to be performed by the communications system 100 in claims 41 -50 may be configured to be repeated periodically to dynamically adjust the determined periodicity.

In some embodiments, at least one of the one or more first indications, the one or more second indications, the one or more third indications, the set of the one or more devices 130, the predicted number of requests, and the periodicity may be configured to be determined in real time.

In some embodiments, the obtaining of the respective predictive model further is configured to comprise, for the one or more first devices 131 autonomously obtaining the respective predictive model by: i) training the respective predictive model during the training phase by providing input to the respective predictive model, ii) testing the respective accuracy of the trained respective predictive model during the testing phase, and iii) iterating the training phase and the testing phase until the respective accuracy may exceed the respective fifth threshold.

The embodiments herein may be implemented through one or more processors, such as the processor 1 104 in the first node 11 1 and a processor 1203 in the second node 1 12, respectively, depicted in Figure 12, together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the in the communications system 100. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the communications system 100. The communications system 100 may further comprise the memory in the first node 1 11 and a memory 1204 in the second node 1 12, respectively, comprising one or more memory units. The memory 1204 is arranged to be used to store obtained information, store data, configurations, schedulings, and applications etc. to perform the methods herein when being executed in the communications system 100.

The first node 1 11 comprised in the communications system 100 may be understood to comprise the receiving port 1 106, the sending port 1 107, the program 1 108, the computer readable medium 1 109 and optionally, the processing circuitry 1104 and/or the radio circuitry 11 10, as described in relation to Figure 11 , configured to implement the methods according to the embodiments described herein for the first node 1 11 comprised in the communications system 100.

In some embodiments, the second node 112 may receive information from, e.g., the first node 11 1 , the another node 113, the respective radio network node 140, the first radio network node 141 , the second radio network node 142, the one or more devices 130, the one or more first devices 131 , and/or another node or device through a receiving port 1205 in the first second node 112. In some examples, the receiving port 1205 may be, for example, connected to one or more antennas in the second node 112. In other embodiments, the second node 1 12 may receive information from another structure in the communications system 100 through the receiving port 1107. Since the receiving port 1205 may be in communication with the processor 1203 of the second node 1 12, the receiving port 1205 may then send the received information to the respective processor 1203. The receiving port 1205 may also be configured to receive other information.

The processor 1203 in the second node 1 12 may be further configured to transmit or send information to e.g., the first node 111 , the another node 113, the respective radio network node 140, the first radio network node 141 , the second radio network node 142, the one or more devices 130, the one or more first devices 131 , another node or device and/or another structure in the communications system 100, through a sending port 1206 in the second node 112, which may be in communication with the processor 1203, and the memory 1204.

Those skilled in the art will also appreciate that any of the units 1201 -1202 described above may refer to a combination of analog and digital circuits, and/or one or more processors configured with software and/or firmware, e.g., stored in memory, that, when executed, respectively, by the one or more processors such as the processor 1203, perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuit (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC). Any of the units 1201 -1202 described above may be the processor 1203 of the second node 112, or an application running on such processor.

Thus, the methods according to the embodiments described herein for the communications system 100 may be respectively implemented by means of a computer program 1207 product in the second node 1 12, comprising instructions, i.e., software code portions, which, when executed on at least one processor 1203, cause the at least one processor 1203 to carry out the actions described herein, as performed by the in the second node 1 12. The computer program 1207 product may be stored on a computer-readable storage medium 1208 in the second node 112. The computer-readable storage medium 1208, having respectively stored thereon the computer program 1207, may comprise instructions which, when respectively executed on at least one processor 1203, cause the at least one processor 1203 to carry out the actions described herein, as performed by the second node 1 12. In some embodiments, the computer-readable storage medium 1208 may be a non-transitory computer-readable storage medium, such as a CD ROM disc, a memory stick, or stored in the cloud space. In other embodiments, the computer program 1207 product may be stored on a carrier containing the computer program, wherein the carrier is one of an electronic signal, optical signal, radio signal, or the computer-readable storage medium 1208, as described above.

The second node 112 may comprise an interface unit to facilitate communications between the second node 1 12 and other nodes or devices, e.g., the first node 11 1 , the another node 1 13, the respective radio network node 140, the first radio network node 141 , the second radio network node 142, the one or more devices 130, the one or more first devices 131 , another node or device and/or another structure in the communications system 100. In some particular examples, the interface may, for example, include a transceiver configured to transmit and receive radio signals over an air interface in accordance with a suitable standard.

In other embodiments, the communications system 100 may comprise the following arrangement depicted in Figure 12b. The communications system 100 may comprise a processing circuitry 1203 in the second node 112, e.g., one or more processors such as the processor 1203, in the communications system 100 and the memory 1204. The communications system 100 may also comprise a radio circuitry 1209 in the second node 1 12, which may comprise e.g., the respective receiving port 1205 and the respective sending port 1206. The processing circuitry 1203 may be configured to, or operable to, perform the method actions according to Figure 5 and/or any of Figures 6-10, in a similar manner as that described in relation to Figure 12a. The radio circuitry 1209 may be configured to set up and maintain at least a wireless connection with the first node 111 , the another node 1 13, the respective radio network node 140, the first radio network node 141 , the second radio network node 142, the one or more devices 130, the one or more first devices 131 , another node or device and/or another structure in the communications system 100.

Hence, embodiments herein also relate to the communications system 100 operative to handle security, the communications system 100 being operative to comprise the one or more nodes 110. The communications system 100 may comprise the processing circuitry 1203 and the memory 1204, said memory 1204 containing instructions executable by said processing circuitry 1203, whereby the communications system 100 is further operative to perform the actions described herein in relation to the communications system 100, e.g., in Figure 5 and/or any of Figures 6-10.

When using the word "comprise" or “comprising”, it shall be interpreted as non- limiting, i.e. meaning "consist at least of".

The embodiments herein are not limited to the above described preferred embodiments. Various alternatives, modifications and equivalents may be used. Therefore, the above embodiments should not be taken as limiting the scope of the invention.

Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

As used herein, the expression “at least one of:” followed by a list of alternatives separated by commas, and wherein the last alternative is preceded by the “and” term, may be understood to mean that only one of the list of alternatives may apply, more than one of the list of alternatives may apply or all of the list of alternatives may apply. This expression may be understood to be equivalent to the expression “at least one of:” followed by a list of alternatives separated by commas, and wherein the last alternative is preceded by the “or” term.

Any of the terms processor and circuitry may be understood herein as a hardware component.

As used herein, the expression “in some embodiments” has been used to indicate that the features of the embodiment described may be combined with any other embodiment or example disclosed herein. As used herein, the expression “in some examples” has been used to indicate that the features of the example described may be combined with any other embodiment or example disclosed herein.