Technical Field

Embodiments described herein relate to methods and apparatus for characterizing a radio frequency, RF, environment in a telecommunications network, for example by estimating a RF propagation model based on mobility statistics.

Background

Building an exact RF propagation model of a network is a particular exercise that is carried out to perform tasks such as optimizing the performance of a network. Among all of the techniques available to achieve this, one method is whereby operators carry out drive test campaigns to measure the signal level of reference signals used by cells of the network. In this context, drive tests consist of using a motor vehicle containing measurement equipment capable of measuring signal levels in a given geographical area, thus requiring engineers to drive a car along the area that needs to be characterized.

In the case of large areas, this implies that drive test campaigns can take several days to collect the relevant data. The relevant data which is collected is stored together with the associated measurement location, for which the use of a GPS device is required. During these campaigns, engineers receive measurements of network RF conditions which are later used to detect areas suffering from poor RF conditions (either in terms of coverage, interference or both).

However, performing drive tests is an expensive activity, and has a disadvantage of being limited in terms of only RF sampling the drive route. In other words, while drive testing is useful for characterizing the RF environment experienced by UEs under similar conditions, i.e. UEs performing calls in cars, it is less useful for characterizing an RF environment indoors or for pedestrian areas.

Apart from drive testing, other solutions for characterizing RF environments associated with LTE networks are based on the collection and processing of call traces with periodical measurement information. While such techniques can vastly reduce the operational costs and improve the efficiency of RF optimization projects compared to drive tests, using RF propagation information such as call traces as input can also lead to other disadvantages, for example whereby such call traces are not always available for use in determining an RF model of a telecommunications network. For example, call traces are licensed by vendors, and as such operators may not be able to afford their cost. Also, the collection and processing of call traces may require sophisticated software and hardware architectures. Furthermore, operators are often reluctant to activate the collection of periodical measurements and reports due to the extra network load they cause.

Summary

According to an embodiment there is provided a method for characterizing a radio frequency, RF, environment in a telecommunications network comprising a plurality of cells. The method comprises obtaining data comprising at least one of configuration management, CM, data and performance management, PM, data. The method comprises generating a distribution indicative of a reference signal received power, RSRP, distribution per cell of the plurality of cells using the obtained data, and generating a distribution indicative of a carrier over interference ratio, C/I, distribution for pairs of cells of the plurality of cells using the obtained data. The method comprises using the generated distributions indicative of the RSRP distribution and C/I distribution to characterize the RF environment. According to another embodiment there is provided a network node comprising a processor and a memory, said memory containing instructions executable by said processor for characterizing a radio frequency, RF, environment in a telecommunications network comprising a plurality of cells. The network node is operative to obtain data comprising at least one of configuration management, CM, data and performance management, PM, data. The network node is operative to generate a distribution indicative of a reference signal received power, RSRP, distribution per cell of the plurality of cells using the obtained data. The network node is operative to generate a distribution indicative of a carrier over interference ratio, C/I, distribution for pairs of cells of the plurality of cells using the obtained data. The network node is operative to use the generated distributions indicative of the RSRP distribution and C/I distribution to characterize the RF environment.

According to another embodiment there is provided a computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method as described herein, and as defined in the appended claims.

According to another embodiment there is provided a computer program product comprising a computer-readable medium with the computer program as described above.

Brief Description of the Drawings

Figure 1 illustrates an example of a flow chart relating to a method according to an embodiment;

Figure 2 shows an example of a network module according to an embodiment;

Figure 3 shows an example of a flow chart relating to a method according to an embodiment;

Figure 4 shows an example of a flow chart relating to a method according to an embodiment;

Figure 5 shows an example of a Reference Signal Received Power, RSRP, distribution; Figure 6 shows an example of a Carrier over Interference ratio, C/I, distribution;

Figure 7 shows an example of a network node according to an embodiment;

Figure 8 shows an example of a telecommunications system; Figure 9 illustrates a telecommunication network connected via an intermediate network to a host computer in accordance with some embodiments;

Figure 10 illustrates a host computer communicating via a base station with a user equipment over a partially wireless connection in accordance with some embodiments;

Figure 1 1 illustrates methods implemented in a communication system including a host computer, a base station and a user equipment in accordance with some embodiments;

Figure 12 illustrates methods implemented in a communication system including a host computer, a base station and a user equipment in accordance with some embodiments;

Figure 13 illustrates methods implemented in a communication system including a host computer, a base station and a user equipment in accordance with some embodiments; and

Figure 14 illustrates methods implemented in a communication system including a host computer, a base station and a user equipment in accordance with some embodiments.

Detailed Description

The following sets forth specific details, such as particular embodiments for purposes of explanation and not limitation. But it will be appreciated by one skilled in the art that other embodiments may be employed apart from these specific details. In some instances, detailed descriptions of well-known methods, nodes, interfaces, circuits, and devices are omitted so as not obscure the description with unnecessary detail. Those skilled in the art will appreciate that the functions described may be implemented in one or more nodes using hardware circuitry (e.g., analog and/or discrete logic gates interconnected to perform a specialized function, ASICs, PLAs, etc.) and/or using software programs and data in conjunction with one or more digital microprocessors or general purpose computers that are specially adapted to carry out the processing disclosed herein, based on the execution of such programs. Nodes that communicate using the air interface also have suitable radio communications circuitry. Moreover, the technology can additionally be considered to be embodied entirely within any form of computer-readable memory, such as solid-state memory, magnetic disk, or optical disk containing an appropriate set of computer instructions that would cause a processor to carry out the techniques described herein.

Hardware implementation may include or encompass, without limitation, digital signal processor (DSP) hardware, a reduced instruction set processor, hardware (e.g., digital or analog) circuitry including but not limited to application specific integrated circuit(s) (ASIC) and/or field programmable gate array(s) (FPGA(s)), and (where appropriate) state machines capable of performing such functions.

In terms of computer implementation, a computer is generally understood to comprise one or more processors, one or more processing modules or one or more controllers, and the terms computer, processor, processing module and controller may be employed interchangeably. When provided by a computer, processor, or controller, the functions may be provided by a single dedicated computer or processor or controller, by a single shared computer or processor or controller, or by a plurality of individual computers or processors or controllers, some of which may be shared or distributed. Moreover, the term“processor” or“controller” also refers to other hardware capable of performing such functions and/or executing software, such as the example hardware recited above.

Although the description is given for a wireless device, or user equipment (UE), it should be understood by the skilled in the art that“UE” is a non-limiting term comprising any mobile or wireless terminal, device or node equipped with a radio interface allowing for at least one of: transmitting signals in uplink (UL) and receiving and/or measuring signals in downlink (DL). A UE herein may comprise a UE (in its general sense) capable of operating or at least performing measurements in one or more frequencies, carrier frequencies, component carriers or frequency bands. It may be a “UE” operating in single- or multi-radio access technology (RAT) or multi-standard mode. As well as“UE”, the terms“mobile station” (“MS”),“mobile device” and“terminal device” may be used interchangeably in the following description, and it will be appreciated that such a device does not necessarily have to be‘mobile’ in the sense that it is carried by a user. Instead, the term“mobile device” encompasses any device that is capable of communicating with communication networks that operate according to one or more mobile communication standards, such as the Global System for Mobile communications, GSM, UMTS, Long- Term Evolution, LTE, IEEE 802.1 1 or 802.16, etc.

The description involves communication between a UE and a radio access network, which typically includes multiple radio access nodes. In the specific example given, the radio access nodes take the form of eNodeBs (eNBs), as defined by 3GPP, or gNodeBs (gNBs) as utilised in the future standards expected to meet the 5G requirements. However, it will be appreciated that the concepts described herein may involve any radio access nodes. Moreover, where the following description refers to steps taken in or by a radio access node, this also includes the possibility that some or all of the processing and/or decision making steps may be performed in a device that is physically separate from the radio antenna of the radio access node, but is logically connected thereto. Thus, where processing and/or decision making is carried out“in the cloud”, the relevant processing device is considered to be part of the radio access node for these purposes.

The embodiments described herein provide a method and apparatus for characterizing the RF environment of a telecommunications network, for example an LTE network, using first and second statistical outputs relating to mobility statistics at adjacency level coming from any source of data. The first statistical output relates to a Reference Signal Received Power (RSRP) distribution per cell, and the second statistical output relates to a Victim-RSRP-to-interferer-RSRP ratio distribution for every pair of cells, also referred to as carrier-to-interference ratio (C/I) distribution or interference matrix.

While the embodiments described herein are made in the context of an LTE network, it is noted that the apparatus and methods may also be used with other networks, including the 5 ^th Generation network, 5G. Likewise, although the embodiments are described in the context of a C/I distribution, it is noted that this term is intended to cover, as noted above, a Victim-RSRP-to-interferer-RSRP ratio distribution for every pair of cells, or an interference matrix.

While the embodiments will mainly focus on the use of a configuration of a network and Performance Management (PM) counters as a source of data, the embodiments are intended to embrace the use of any data source containing mobility statistics at an adjacency level, as well as network configuration parameters. Using PM counters has an advantage in that they are massively implemented in LTE networks, for example as defined in the 3rd Generation Partnership Project (3GPP) Technical Specification relating to Performance Management, TS 32.425 (V14.1 .0). It is noted that references herein to PM counters is intended to embrace both PM counters and PM sub-counters.

It is noted that the outputs of the embodiments described herein, that is the characterization of the RF environment, or the determination of a propagation model in an RF environment, can be used as the input of a different system, for example for optimizing a telecommunications network, or for modifying a mobile network signal propagation prediction. As such, the characterization of an RF environment, as determined according to the embodiments described herein, can be used for a variety of different applications, all of which are intended to be embraced by the present application.

Prior to discussing the embodiments in greater detail, they will first be placed in context in relation to other systems for characterizing an RF environment.

As mentioned in the background section, one method of characterizing an RF environment is to use drive tests. However, such a technique has the disadvantages mentioned in the background section, for example of being costly to implement, and poor for characterizing within buildings or pedestrian areas.

As an alternative to using drive tests in order to characterize the RF propagation environment, another technique is to use specific information, e.g. call traces, provided by the network elements (eNodeBs, MMEs, OSS, ...) related to call L3 messaging. This information contains protocol messages named measurement reports which are described for example in LTE RRC protocol specifications, such as Technical Specification 3GPP RRC Protocol TS 36.331 (Version 15.0.1 , for example Sections 5.5.4 and 5.5.5).

The main purpose of these messages is to facilitate the mobility of active users. Thus, through measurement reports the User Equipment (UE) notify the LTE network about the radio conditions they are experiencing when some RF criteria are met. Based on this the network can make decisions, for example a decision to command the UE to perform a handover (based on lack of coverage, high interference, load balancing or any other reason). Then, for this purpose, measurement reports can be configured to be triggered based on specific RF conditions (known as event-based measurement reports).

The current standardized triggers are:

Event A1 (Serving becomes better than threshold).

Event A2 (Serving becomes worse than threshold).

Event A3 (Neighbor becomes offset better than Primary Cell/Primary SCell, PCell/PSCell).

Event A4 (Neighbor becomes better than threshold).

Event A5 (PCell/PSCell becomes worse than thresholdl and neighbor becomes )etter than threshold2).

Event A6 (Neighbor becomes offset better than Secondary Cell, SCell).

Event B1 (Inter RAT neighbor becomes better than threshold).

Event B2 (PCell becomes worse than thresholdl and inter RAT neighbor becomes better than threshold2).

In addition to these events, different vendors implement the possibility to configure a UE to periodically report radio measurements to the network. This allows the network to know the UE environment without the need for the UE to meet any RF criteria. The use of periodical measurements allows a more realistic characterization of the whole RF environment, since it is possible to collect statistical information from all the offered traffic area, compared to the event-triggered approach, which is limited to certain zones (typically cell borders or areas with compromising RF conditions).

Co-pending patent application WO2017/202464A1 by the present applicant described a method and system in which it is possible to configure the UEs to report periodical measurements to characterize the RF environment. This characterization consists of the calculation of RSRP distribution per cell and a C/I distribution for every pair of cells (also known as interference matrix). The RSRP distribution per cell is built with the signal levels in which the cell is the serving cell (i.e. the strongest cell), and is a very useful metric of the LTE coverage level. The C/I distribution characterizes the interference. This information has many uses, many of them related to radio network optimization and troubleshooting.

For example, one type of use is the detection of over-shooters (i.e. cells received at faraway areas that could be served by other closer cells). Another type of use is the generation of statistical propagation maps for antenna parameter optimization. Another type of use is planning related to PCI and Physical Random Access Channel (PRACH), aimed at minimizing collisions between cells with higher scores in the interference matrix.

However, periodical measurement functionality is not available for all vendors and, in case it is, not all the operators are licensed to use it. Furthermore, the use of periodical measurements introduces load excess, associated with the additional signalling required. Because of such limitations, the system described in co-pending patent application WO2017/202464A1 describes a system in which periodical measurements are emulated from event based measurement reports. WO2017/202464A1 describes how to configure RRC reconfiguration messages to force a UE to periodically report measurement reports.

The three methods described previously (i.e. drive tests, periodical measurements and emulated periodical measurements) allow the creation of an RF propagation model of the LTE network, i.e. a characterization of the RF environment. This model can be used for a variety of uses, including for example the optimization of a set of physical RF related parameters, such as antenna pattern, electrical tilt, mechanical tilt, azimuths, etc.

However, drive tests suffer from excessive costs and other disadvantages, while the periodical measurements and emulated periodical measurements suffer the disadvantage of requiring access to L3 messages in the network. Vendors offer interfaces to access these messages, but this access is licensed and in some cases restricted. These interfaces are widely known as Call Traces (CTR). In addition to these, to process the amount of data containing these messages is a costly process that requires complex software and high hardware capacity. Moreover, periodical measurement functionality implies a signalling overhead which usually dissuades network operators from activating it. Turning to the embodiments described herein, the embodiments propose a method and apparatus to characterize an RF environment of a telecommunications network, for example an LTE network, based on different input data to that of drive tests or call traces.

According to one embodiment, OSS data is used as an input to generate RSRP distribution per cell and C/I distribution for every pair of cells as outputs. In some examples the input OSS data used by the proposed embodiments consists, for example, of performance management, PM, counters and configuration management, CM, data.

Figure 1 shows a method according to an embodiment, for characterizing a radio frequency, RF, environment in a telecommunications network comprising a plurality of cells.

The method comprises obtaining operation support system, OSS, data, comprising at least one of configuration management, CM, data and performance management, PM, data, step 101. It is noted that the OSS data may be received from any type of source, including for example directly from a network node such as an eNodeB.

In one embodiment the OSS data may be received by the network node performing the operations of the method and in an alternative embodiment the OSS data may be accessed by said network node at a repository and downloaded to said network node.

The method comprises generating a distribution indicative of a reference signal received power, RSRP, distribution per cell of the plurality of cells using the OSS data, step 103. The method comprises generating a distribution indicative of a carrier over interference ratio, C/I, distribution for pairs of cells of the plurality of cells using the OSS data, step 105. The method comprises using the generated distributions indicative of the RSRP distribution and C/I distribution to characterize the RF environment, step 107.

As mentioned earlier, the characterized RF environment may then be used for a variety of uses, including for example optimizing one or more parameters of the telecommunications network. The CM data may comprise, for example, information relating to one or more configurations of radio resource control, RRC, mobility events.

The PM data may comprise, for example, PM counter data relating to mobility events within the telecommunications network.

From the CM data, the system requires information regarding the configuration of RRC mobility events. In LTE networks, according to one example it is possible to configure the UEs to report radio conditions using a RRC Connection Reconfiguration message. This message indicates to the UE which are the different RF criteria it must measure in order to report Measurement Reports to the network.

The main configuration parameters in RRC Connection Reconfiguration are described in 3GPP Technical Specification TS.36.331 (Version 15.0.1 ), and include for example:

• EUTRA measurement object“carrierFreq” that defines the frequency that the UE will measure.

• EUTRA Report Configuration ’’event ID” that indicates the event type (A1 , A2, A3...).

• EUTRA Report Configuration ’’triggerQuantity” that indicates whether RSRP or RSRQ will be used to trigger the UE measurement.

• EUTRA Report Configuration’’reportQuantity” that indicates whether RSRP and RSRQ should be reported in UE measurement or only the’’triggerQuantity”.

• EUTRA Report Configuration’’maxReportCells” that defines the maximum number of neighboring cells that will be reported in the UE measurement (maximum is 8).

• EUTRA Report Configuration thresholds, offsets and hysteresis depending on the event type (A1 , A2, A3...) that defines the“entering” and“leaving” conditions of each event. As an example, if’’triggerQuantity” is RSRP, A2 threshold is set for example to -1 10 dBm and A2 hysteresis is defined, for example, to 1 dB, then:

• “Entering condition" is fulfilled if serving cell RSRP is less than -1 1 1 dBm (A2 threshold minus A2 hysteresis).

• “Leaving condition" is fulfilled if serving cell RSRP is higher than -109 dBm (A2 threshold plus A2 hysteresis). • EUTRA Report Configuration’’timeToT rigger” that defines the time that the UE should be fulfilling the entering condition to report a UE measurement report.

• EUTRA Report Configuration’’reportlnterval” that indicates how often a new UE measurement report must be sent once the first UE measurement report is sent and leaving condition is not fulfilled.

• EUTRA Report Configuration’’reportAmount” that provides how many UE measurement reports will be sent by the UE once the first UE measurement report is sent and leaving condition is not fulfilled.

Among all the previous parameters, according to some embodiments described herein the following parameters are used:

• carrierFreq

• event ID

• triggerQuantity

• thresholds, offsets and hysteresis

It is noted, however, that the apparatus and methods described herein are intended to cover the use of other parameters.

It is also noted that different configurations of the previous parameters may be associated to RAN mobility algorithms, for example by vendors in an LTE network. As an example:

• Event A3 may be configured to trigger intra-frequency handovers

• Event A5 may be configured to trigger inter-frequency handovers

• Event A2 may be configured to trigger blind redirections towards other technologies

• Event B2 may be configured to trigger handovers towards other technologies

With regard to the PM data, according to some embodiments they require information regarding the number of established connections (e.g. the number of UEs turning into active mode) and the number of handovers attempted at adjacency level. This is, given a cell, it is needed to know the number of handovers that were attempted to every of its neighbors.

In one example, the PM data used by the method comprises information relating to the number of established connections by user equipment, and the number of handovers attempted by user equipment at an adjacency level.

In one example, determining the number of established connections comprises determining the number of user equipment that have turned into an active mode of operation. Determining the number of handovers attempted by a user equipment at an adjacency level may comprise, according to one example, for a given cell, determining the number of handovers that have been attempted by the cell to each of the neighbors of the cell.

More generally, according to some embodiments the method comprises determining the number of user equipment that are in a state from which to carry out a mobility action that leads to an increment of a PM mobility counter, and then determining the number of events that lead to an increase of a PM mobility counter.

It is noted, however, that the PM data may comprise other information in other examples, for example relating to idle mode users with some other mobility counters, (e.g. network attachments and cell reselections in idle mode). In such examples, the counters and thresholds in equations (4), (5) and (7), (8) described herein may be replaced by the counters and thresholds related to idle mode, e.g.: UE Context Establishment Successes could be replaced by Attachment Successes, and the thresholds in equations (5) and (8) could be replaced by cell reselection thresholds, and the mobility counters in equations (4) and (7) could be replaced by cell reselections related counters.

Based on the CM and PM information described above, the embodiments are able to determine distributions indicative of both RSRP distribution per cell and C/I distribution for every pair of cells.

Figure 2 shows an example of a network module 200 according to an embodiment, for characterizing the RF environment of a telecommunications network. The network module 200 may be a standalone node, or form part of another network node, or part of a cloud environment, as will be described later in Figure 8.

The network module 200 comprises an RSRP module 201 and a C/I module 202. The network module 200 is configured to receive OSS data, in this example comprising CM data 203 and PM data 204. The RSRP module 201 is configured to generate a distribution 206 indicative of a reference signal received power, RSRP, distribution per cell of the plurality of cells using the OSS data, i.e. the CM data and PM data in this example. The C/I module 202 is configured to generate a distribution 207 indicative of a C/I distribution for pairs of cells of the plurality of cells using the OSS data, i.e. CM data and PM data in this example.

The RSRP module 201 and a C/I module 202 may be configured to build distributions based on the law of large numbers. The law of large numbers states that the sum of a large number of random variables is described by a normal distribution. Therefore, as the radio channel is affected by a huge number of external conditions, this law can be applied to model signal distributions.

Thus, according to one embodiment the network module 200 may be configured to generate the distribution indicative of the RSRP distribution and/or the C/I distribution using a normal distribution approximation, wherein the normal distribution approximation comprises a mean value m and variance value o. In such an embodiment the network module is configured to receive either the mean value m or the variance value o as an input, (in Figure 2 this being the variance value o, also referred to herein as the standard deviation).

It is noted, however, that other distributions characterized by different parameters may be used, for example a scale parameter“o” and non-centrality parameter“v” in a Rice distribution, or a shape parameter“m” and a spread parameter“w” in a Nakagami distribution. The choice of distribution to use can depend, for example, on the RF environment being characterized.

In the example of Figure 2 the RSRP module 201 , as mentioned above, is configured to generate RSRP distributions per cell. In order to do this, the RSRP module 201 may use the configuration of events related to mobility and their associated handover statistics per adjacency (i.e. per pair of cells) available at PM. The relations between event configurations and handover statistics used by this module in this particular example are listed below:

Event A2 Blind redirections

Event A5 Inter-frequency handovers

Event B2 Inter-RAT handovers Apart from the previous, this module uses statistics, for example regarding the number of connections established per cell.

As mentioned before, signal distributions are modelled by normal distributions. Normal distributions are described by two values, mean "m" and variance "o ² ". Then, the probability function can be described according to equation (1 ) below and the cumulative distribution can be described according to equation (2) below:

Thus, once the mean m and variance o are known the RSRP distribution is characterized. An option to calculate these values is to know the values of f(xi) and f(x ₂ ) where xi, x ₂ are two different RSRP values.

However, just based on CM parameters and PM counters it is not possible to obtain two different values of f(x). Then, either the mean value m and variance value o need to be specified as a system input. In the embodiments described herein, the variance value o is the one to be specified as an input, shown as input 205 in Figure 2 above.

In this example the variance value o is a constant value applied to all cell distributions. At this point, it is still pending to calculate m to fully characterize the normal distribution. For this, equations (3), (4) and (5) below may be used: (¾SRP) ⁼ YRSRP (3)

I RATHOS + Blind redirections +interF _H os

^RSRP UE Context Establishment Successes +iRATHOs+ Blindredirections + interFHOs ^ ^J

where:

"IRATHO _S " is the total number of inter-RAT handover attempts.

"Blindredirections" is the total number of blind redirections.

"interF _HOs " is the total number of inter-frequency handover attempts.

"UE Context Establishment Successes" is the total number of established calls.

B2 _threshoidi is the RSRP value below which the serving cell must be measured to trigger event B2.

B2hyst is the RSRP value to be added to B2 _threshoidi for hysteresis purposes.

A2 _threshold is the RSRP value below which the serving cell must be measured to trigger event A2.

A2hyst is the RSRP value to be added to A2 _threshoid for hysteresis purposes.

A5 _threshoidi is the RSRP value below which the source cell must be measured to trigger event A5.

A5hyst is the RSRP value to be added to A5threshoidi for hysteresis purposes.

A5 _0ffset is the RSRP value to be added to A5threshoidi. It is composed of frequency offset (defining an offset for specific source to neighbor frequency relations) and cell individual offset (defining an offset for specific source to neighbor cell relations).

Using equation (3) above, it is possible to calculate the value of F(x), i.e. the cumulative distribution, where x is the maximum RSRP value given by any of the configured B2, A2 and A5 thresholds and their hysteresis and offset values, as stated in equation (5). This is based on the principle that blind redirections, inter-frequency handovers and inter-RAT handovers are triggered when RSRP values are below their configured thresholds.

In addition, according to examples it is considered that all the established sessions initially experience RSRP levels above the configured mobility thresholds. Thus, the numerator in equation (4) represents samples where the RSRP value is below the maximum threshold among B2, A2 and A5 thresholds (as in equation (5)) and the denominator in equation (4) represents the total number of RSRP samples.

In an example where any of B2, A2 and A5 are not configured for mobility purposes, they can be removed from equation (5) as well as their associated mobility statistics from equation (4), i.e. A2 Blind redirections, A5 Inter-frequency handovers, B2 Inter- RAT handovers.

Finally, a mean value of the RSRP distribution, i.e. PRSRP, can be derived from equation (2) by replacing F(x) with YRSRP, which was computed according to equation (3), and using the value of the variance o as input. The variance o represents the standard deviation of the RSRP distribution.

Figure 3 shows is greater detail an example of how the RSRP module 201 of Figure 2 may be configured to function. The function is performed per cell, as illustrated by step 301.

In step 302 it is determined whether the cell is configured for B2 events, i.e. configured for inter radio access technology, inter-RAT, handovers. If so, in step 306 it is determining from received PM data the number of inter-RAT handovers.

In step 303 it is determined whether the cell is configured for A2 events, i.e. configured for blind redirections. If so, in step 308 it is determining from PM data the number of blind redirections.

In step 304 it is determined whether the cell is configured for A5 events, i.e. configured for inter-frequency handovers. If so, in step 310 it is determining from PM data the number of inter-frequency handovers. Thus, according to the particular embodiment of Figure 3, there is provided a method for generating a distribution indicative of a RSRP distribution, per cell, that comprises: determining from first CM data (e.g. Event A2) whether a cell is configured for blind redirections, and if so determining from PM data the number of blind redirections; determining from second CM data (e.g. Event A5) whether a cell is configured for inter-frequency handovers, and if so determining from PM data the number of inter- frequency handovers; and determining from third CM data (e.g. Event B2) whether a cell is configured for inter radio access technology, inter-RAT, handovers, and if so determining from PM data the number of inter-RAT handovers.

It is noted that one or more of the events B2, A2 and A5 may be omitted if a cell is not configured for that particular configuration.

In step 31 1 the cumulative distribution for RSRP, F(XRSRP), is calculated using equations (3), (4) and (5) described above.

Then, using the variance value o as an input, for example which may comprise a constant value across all cells, a distribution indicative of an RSRP distribution, PRSRP, is calculated in step 312 using equation (2).

Returning to Figure 2, the C/I module 202 is configured to generate a distribution indicative of a C/I distribution per pair of victim/interferer cells. In order to do this, the C/I module 202 is configured to use the configuration of events A3 and A5 in the particular example, related to mobility and their associated handover statistics. Apart from the previous, the C/I module 202 also uses statistics, for example regarding the number of connections established per cell.

As with the RSRP module 201 , C/I distributions are modelled according to a normal distribution in this particular example. Therefore, it is necessary to calculate a distribution indicative of a C/I distribution, i.e. p<c _/i) . For this purpose, equations (3), (4) and (5) are converted into equations (6), (7) and (8), respectively, as shown below:

F(X _C/ , ) = Yc/i (6)

HOVICTIM^INTERFERER , _

^C/I UE Context Establishment Successes + HOVICTIM-»INTERFERER ^ ^J where:

HOVICTIM INTERFERER is the total number of intra-frequency handover attempts from source cell (victim) to neighbor cell (interferer).

UE Context Establishment Successes is the total number of established calls.

AS _threshoid is the parameter defining the difference of RSRP values measured from source and neighbor cells in order to trigger A3 event.

A3hyst is the RSRP value to be added to A3threshoid for hysteresis purposes.

A3 _0ffset is the RSRP value to be added to A3threshoid. It is composed of frequency offset (defining an offset for specific source to neighbor frequency relations) and cell individual offset (defining an offset for specific source to neighbor cell relations).

A5threshoidi is the RSRP value below which the source cell must be measured to trigger event A5.

A5 _threshoid2 is the RSRP value above which the neighbor cell must be measured to trigger event A5. AShyst is the RSRP value to be added to A5threshoidi for hysteresis purposes.

A5 ₀ ffset is the RSRP value to be added to A5threshoidi . It is composed of frequency offset (defining an offset for specific source to neighbor frequency relations) and cell individual offset (defining an offset for specific source to neighbor cell relations).

As in the RSRP module 201 , using equation (6) it is possible to calculate the value of cumulative distribution F(x) where x is the maximum difference in C/I values between source and target cell obtained in equation (8) for every particular pair of cells. This is based on the principle that intra-frequency handovers are triggered when C/I values are below their configured thresholds. In addition, it is considered that all the established sessions initially experience C/I levels above the configured mobility thresholds. Thus, the numerator in equation (7) represents samples where the C/I value is below the maximum threshold among A3, and A ₅ threshoid2 - A5threshoidi (as in equation (8)), and the denominator in equation (7) represents the total number of C/I samples.

In a scenario where any of A3 and A5 are not configured for mobility purposes, they can be removed from equation (8).

Finally, pc/i can be derived from equation (2) by replacing F(X) with Yc _/i , which was computed according to equation (6), and using the value of s as input s represents the standard deviation of the distribution of the RSRP difference.

Figure 4 shows in greater detail an example of how the C/I module 202 of Figure 2 may be configured to function. The function is performed per pair of cells, i.e. source-neighbor cells, as illustrated by step 401.

In step 402 it is determined whether the cell is configured for A3 events, i.e. configured for intra-frequency handovers. If so, in step 406 it is determining from received PM data the number of intra-frequency handovers.

In step 403 it is determined whether the cell is configured for A5 events, i.e. configured for intra-frequency handovers. If so, in step 406 it is determining from received PM data the number of intra-frequency handovers. Thus, according to the particular embodiment of Figure 4, there is provided a method for generating a distribution indicative of a C/I distribution, per pair of source and neighbor cells, that comprises:

determining from fourth CM data (e.g. Event A3) whether a cell is configured for intra-frequency handovers, and if so determining from PM data the number of intra- frequency handovers; and

determining from fifth CM data (e.g. Event A5) whether a cell is configured for intra- frequency handovers, and if so determining from PM data the number of intra-frequency handovers.

It is noted that either of the events A3 or A5 may be omitted if a cell is not configured for that particular configuration. In step 408 a cumulative distribution F(Xc _/i ) is calculated using equations (6), (7) and (8) described above.

Then, using the variance value o as an input, for example which may comprise a constant value across all cells, a distribution indicative of a C/I distribution, pc _/i , is calculated in step 409 using equation (2).

In the embodiments described above it is noted that the variance value o may be specified as a constant value applied to cell distributions in the generation of the distribution indicative of a RSRP distribution and a C/I distribution.

In some embodiments the variance value o is specified as a constant value, for example in the range 9 to 10 dB. In some embodiments higher values may be used, for example when modeling large cell range scenarios, and lower values may be used, for example when modelling small cell scenarios.

Figure 5 shows an example of a RSRP distribution, indicating the area where iRAT handovers, inter-frequency handovers and blind redirections are performed. Figure 6 shows an example of a C/I distribution, indicating where intra-frequency handovers are performed.

Figure 7 shows and example of a network node 700 comprising a processor 701 and a memory 703, said memory 703 containing instructions executable by said processor 701 for characterizing a radio frequency, RF, environment in a telecommunications network comprising a plurality of cells. The network node may also comprise an interface 705 for interfacing with other network nodes.

The network node 700 is operative to receive operation support system, OSS, data comprising at least one of configuration management, CM, data and performance management, PM, data. The network node 700 is operative to generate a distribution indicative of a reference signal received power, RSRP, distribution per cell of the plurality of cells using the OSS data, and generate a distribution indicative of a carrier over interference ratio, C/I, distribution for pairs of cells of the plurality of cells using the OSS data. The network node is operative to use the generated distributions indicative of the RSRP distribution and C/I distribution to characterize the RF environment.

For example, the CM data may comprise information relating to one or more configurations of radio resource control, RRC, mobility events.

The network node 700 may be operative to receive a RRC connection reconfiguration message comprising one or more measurement reports performed by a user equipment.

The network node 700 may be further operative to determine the number of user equipment that are in a state from which to carry out a mobility action that leads to an increment of a PM mobility counter, and determine the number of events that lead to an increase of a PM mobility counter.

In some embodiments the PM data may comprise information relating to the number of established connections by user equipment, and the number of handovers attempted by user equipment at an adjacency level. The network node 700 may be operative to determine the number of established connections by determining the number of user equipment that have turned into an active mode of operation, and determine the number of handovers attempted by a user equipment at an adjacency level by, for a given cell, determining the number of handovers that have been attempted by the cell to each of the neighbors of the cell.

In some embodiments, generating a distribution indicative of a RSRP distribution may comprise the network node 700 using information relating to at least one PM mobility counter.

In some embodiments, generating a distribution indicative of a RSRP distribution may comprise the network node being operative to use configuration information relating to one or more mobility events, and associated handover statistics per pair of cells for each of the one or more mobility events, to generate the distribution indicative of the RSRP distribution.

The network node 700, when operative to generate a distribution indicative of a RSRP distribution, per cell, may be operative to: determine from first CM data (Event A2) whether a cell is configured for blind redirections, and if so determining from PM data the number of blind redirections; determine from second CM data (Event A5) whether a cell is configured for inter-frequency handovers, and if so determining from PM data the number of inter-frequency handovers; and determine from third CM data (Event B2) whether a cell is configured for inter radio access technology, inter-RAT, handovers, and if so determining from PM data the number of inter-RAT handovers.

In some embodiments, generating a distribution indicative of a C/I distribution may comprise the network node 700 being operative to use at least one PM mobility counter.

In some embodiments, generating a distribution indicative of a C/I distribution may comprise the network node 700 being operative to use configuration information relating to one or more mobility events, and associated handover statistics per pair of cells for each of the one or more mobility events, to generate the distribution indicative of a C/I distribution. In some embodiments, generating a distribution indicative of a C/I distribution, per pair of source and neighbor cells, comprises the network node 700 being operative to: determine from fourth CM data (Event A3) whether a cell is configured for intra-frequency handovers, and if so determining from PM data the number of intra-frequency handovers; and determine from fifth CM data (Event A5) whether a cell is configured for intra- frequency handovers, and if so determining from PM data the number of intra-frequency handovers.

Generating the distribution indicative of the RSRP distribution and/or the C/I distribution may comprise using a normal distribution approximation, wherein the network node 700 is operative to use a normal distribution approximation comprising a mean value (m) and variance value (o).

Either the mean value m or the variance value o can be specified as a system input in the generation of the distribution indicative of a RSRP distribution and a C/I distribution.

For example, the variance value o can be specified as a constant value applied to cell distributions in the generation of the distribution indicative of a RSRP distribution and a C/I distribution, as described in greater detail in the examples above. For example, the variance value o can be specified as a constant value in the range 9 to 10 dB. However, as explained above, in some embodiments higher values may be used, for example when modelling large cell range scenarios, and lower values may be used, for example when modelling small cell scenarios.

The embodiments described herein have been tested in the field to optimize a network in two different markets. The aim of the field test was to increase the number of connections in the capacity band (2500 MHz). In these networks, event B2 was configured to trigger inter-RAT handovers, event A3 was configured to trigger intra- frequency handovers and event A5 was configured to trigger both inter-frequency and intra-frequency handovers. The variance value o was set to 10 dB, and PRSRP and p<c _/i) calculated accordingly.

Both the previous networks were multiband networks having one of the bands configured as the capacity one (2500 MHz). In both networks, the radiofrequency propagation channel for the capacity band was modeled using the presented invention. Then, the model was provided as an input to a network optimization tool in order to optimize electrical tilts in the capacity band with the aim of increasing the number of users it serves. The objectives were met as illustrated in the following tables:

Table 1 below shows the results from a first network project - as can be seen the number of connections increased almost 30% while Drop Call Rate and HO Failure Rate were improved:

Table 1 - First Network Project

All of the KPI figures in Table 1 were computed averaging daily values during a period of three days before changes, and three days after changes.

Table 2 below shows the results from a second network project - the main objective of this project was to decongest the 800 band by moving users to the upper bands. This goal was achieved and Table 2 below shows how utilization, number of connected users and number of RRC connections increased in a cluster of the highest band:

Table 2 - Second Network Project In order to isolate the effects of electrical tilt changes related to this activity from other changes, all of the KPI figures in Table 2 were computed for the busy hour of the day before the changes and the busy hour for the day after changes.

It can be noted that reducing both the DL throughput and DL tonnage is an expected behavior when increasing the coverage, since averaged cell SINR is decreased. Particularly, new users will be located in the border of the coverage area, which means that they will contribute with lower SINR.

Referring to Figure 8, this shows that the proposed network module, or methods described herein, (referred to in Figure 8 as“RF distributions”) can be implemented as part of the OSS to enhance SON algorithm (as shown in Figure 8a) or as part of the eNodeB (as shown in Figure 8b). They may also be used as a separate entity (as shown in Figure 8c) for example for network design and optimization tasks.

The embodiments described herein provide a system, method and apparatus which implements a methodology based on PM counters collected from a live network to provide the required information, for example, for performing physical parameters optimization. The embodiments can be implemented either as part of the OSS to enhance centralized SON functionalities or as part of the eNodeB to enhance distributed SON functionalities. It may also be used by a separate entity for network optimization tasks. In addition, the embodiments provide a universal solution that can be used for different vendors.

According to another aspect there is provided a computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any one of the embodiments described herein, and as defined in the appended claims.

According to another aspect, there is also provided a computer program product comprising a computer-readable medium with the computer program as described above. It is noted that, according to some embodiments, the apparatus and methods described herein may be implemented as part of an Operation Support System (OSS) to enhance different centralized or distributed Self-Organizing Network (SON) functionalities, or may be used as stand-alone entities for network design and optimization tasks.

In an example where there is an aim to optimize the performance of a network, such as the performance of an LTE network, a proper RF environment characterization is required. Characterizing the RF environment may include, for example, the modelling of the actual signal fingerprint for all the cells the cellular network is composed of. Thus, once modelled, it is possible to locate areas where both coverage and interference key performance indicators (KPIs) can be optimized.

The RF optimization based on outputs of the embodiments described herein may comprise, for example, modifying the physical configuration of a cell, which include for example modifying one or more of:

• Transmission Power

• Antenna elevation

• Antenna azimuth

• Antenna electrical tilt

• Antenna mechanical tilt

• Antenna model

Physical cell ID (PCI)

• Physical Random Access Channel (PRACH) root sequence index

The embodiments described herein have an advantage of enabling an RF environment to be characterized for such applications using standard configuration management (CM) and performance management (PM) data. Since CM and PM data are a basic functionality offered by LTE vendors, the embodiments may be used universally.

Further, the embodiments have an advantage of enabling an RF environment to be characterized without using drive tests nor call traces. Thus, the use of OSS data, rather than drive tests and call traces, provides savings in operational expenditure, OPEX, costs. Avoiding the use of call traces, CTR, also implies savings in hardware costs and software complexity required for their processing, and avoids network load overheads associated with call trace signalling.

In examples where all UEs in the network are considered, this implies the consideration of all areas where traffic is allocated.

The embodiments may be implemented, for example, in network nodes as a SON solution for self RF optimization and troubleshooting.

According to one embodiment there is provided a method for characterizing a radio frequency, RF, environment in a telecommunications network comprising a plurality of cells. The method comprises generating a distribution indicative of a reference signal received power, RSRP, distribution per cell of the plurality of cells using network configuration parameters, generating a distribution indicative of a carrier over interference ratio, C/I, distribution for pairs of cells of the plurality of cells using mobility statistics at an adjacency level; and using the generated distributions indicative of the RSRP distribution and C/I distribution to characterize the RF environment.

Figure 9 illustrates a telecommunication network connected via an intermediate network to a host computer in accordance with some embodiments.

With reference to Figure 9, in accordance with an embodiment, a communication system includes telecommunication network 1310, such as a 3GPP-type cellular network, which comprises access network 131 1 , such as a radio access network, and core network 1314. Access network 131 1 comprises a plurality of base stations 1312a, 1312b, 1312c, such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 1313a, 1313b, 1313c. Each base station 1312a, 1312b, 1312c is connectable to core network 1314 over a wired or wireless connection 1315. A first UE 1391 located in coverage area 1313c is configured to wirelessly connect to, or be paged by, the corresponding base station 1312c. A second UE 1392 in coverage area 1313a is wirelessly connectable to the corresponding base station 1312a. While a plurality of UEs 1391 , 1392 are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole UE is in the coverage area or where a sole UE is connecting to the corresponding base station 1312. Telecommunication network 1310 is itself connected to host computer 1330, which may be embodied in the hardware and/or software of a standalone server, a cloud- implemented server, a distributed server or as processing resources in a server farm. Host computer 1330 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider. Connections 1321 and 1322 between telecommunication network 1310 and host computer 1330 may extend directly from core network 1314 to host computer 1330 or may go via an optional intermediate network 1320. Intermediate network 1320 may be one of, or a combination of more than one of, a public, private or hosted network; intermediate network 1320, if any, may be a backbone network or the Internet; in particular, intermediate network 1320 may comprise two or more sub-networks (not shown).

The communication system of Figure 9 as a whole enables connectivity between the connected UEs 1391 , 1392 and host computer 1330. In this embodiment the connectivity may be described as an over-the-top (OTT) connection 1350. Host computer 1330 and the connected UEs 1391 , 1392 are configured to communicate data and/or signaling via OTT connection 1350, using access network 131 1 , core network 1314, any intermediate network 1320 and possible further infrastructure (not shown) as intermediaries. OTT connection 1350 may be transparent in the sense that the participating communication devices through which OTT connection 1350 passes are unaware of routing of uplink and downlink communications. For example, base station 1312 may not or need not be informed about the past routing of an incoming downlink communication with data originating from host computer 1330 to be forwarded (e.g., handed over) to a connected UE 1391. Similarly, base station 1312 need not be aware of the future routing of an outgoing uplink communication originating from the UE 1391 towards the host computer 1330.

Figure 10 illustrates a host computer communicating via a base station with a user equipment over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with an embodiment, of the UE, base station and host computer discussed in the preceding paragraphs will now be described with reference to Figure 10. In communication system 1400, host computer 1410 comprises hardware 1415 including communication interface 1416 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of communication system 1400. Host computer 1410 further comprises processing circuitry 1418, which may have storage and/or processing capabilities. In particular, processing circuitry 1418 may comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions. Host computer 1410 further comprises software 141 1 , which is stored in or accessible by host computer 1410 and executable by processing circuitry 1418. Software 141 1 includes host application 1412. Host application 1412 may be operable to provide a service to a remote user, such as UE 1430 connecting via OTT connection 1450 terminating at UE 1430 and host computer 1410. In providing the service to the remote user, host application 1412 may provide user data which is transmitted using OTT connection 1450.

Communication system 1400 further includes base station 1420 provided in a telecommunication system and comprising hardware 1425 enabling it to communicate with host computer 1410 and with UE 1430. Hardware 1425 may include communication interface 1426 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of communication system 1400, as well as radio interface 1427 for setting up and maintaining at least wireless connection 1470 with UE 1430 located in a coverage area (not shown in Figure 10) served by base station 1420. Communication interface 1426 may be configured to facilitate connection 1460 to host computer 1410. Connection 1460 may be direct or it may pass through a core network (not shown in Figure 10) of the telecommunication system and/or through one or more intermediate networks outside the telecommunication system. In the embodiment shown, hardware 1425 of base station 1420 further includes processing circuitry 1428, which may comprise one or more programmable processors, application- specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions. Base station 1420 further has software 1421 stored internally or accessible via an external connection.

Communication system 1400 further includes UE 1430 already referred to. Its hardware 1435 may include radio interface 1437 configured to set up and maintain wireless connection 1470 with a base station serving a coverage area in which UE 1430 is currently located. Hardware 1435 of UE 1430 further includes processing circuitry 1438, which may comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions. UE 1430 further comprises software 1431 , which is stored in or accessible by UE 1430 and executable by processing circuitry 1438. Software 1431 includes client application 1432. Client application 1432 may be operable to provide a service to a human or non-human user via UE 1430, with the support of host computer 1410. In host computer 1410, an executing host application 1412 may communicate with the executing client application 1432 via OTT connection 1450 terminating at UE 1430 and host computer 1410. In providing the service to the user, client application 1432 may receive request data from host application 1412 and provide user data in response to the request data. OTT connection 1450 may transfer both the request data and the user data. Client application 1432 may interact with the user to generate the user data that it provides.

It is noted that host computer 1410, base station 1420 and UE 1430 illustrated in Figure 10 may be similar or identical to host computer 1330, one of base stations 1312a, 1312b, 1312c and one of UEs 1391 , 1392 of Figure 9, respectively. This is to say, the inner workings of these entities may be as shown in Figure 10 and independently, the surrounding network topology may be that of Figure 9.

In Figure 10, OTT connection 1450 has been drawn abstractly to illustrate the communication between host computer 1410 and UE 1430 via base station 1420, without explicit reference to any intermediary devices and the precise routing of messages via these devices. Network infrastructure may determine the routing, which it may be configured to hide from UE 1430 or from the service provider operating host computer 1410, or both. While OTT connection 1450 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).

Wireless connection 1470 between UE 1430 and base station 1420 is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to UE 1430 using OTT connection 1450, in which wireless connection 1470 forms the last segment. A measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring OTT connection 1450 between host computer 1410 and UE 1430, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring OTT connection 1450 may be implemented in software 141 1 and hardware 1415 of host computer 1410 or in software 1431 and hardware 1435 of UE 1430, or both. In embodiments, sensors (not shown) may be deployed in or in association with communication devices through which OTT connection 1450 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 141 1 , 1431 may compute or estimate the monitored quantities. The reconfiguring of OTT connection 1450 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect base station 1420, and it may be unknown or imperceptible to base station 1420. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling facilitating host computer 1410’s measurements of throughput, propagation times, latency and the like. The measurements may be implemented in that software 141 1 and 1431 causes messages to be transmitted, in particular empty or‘dummy’ messages, using OTT connection 1450 while it monitors propagation times, errors etc.

Figure 1 1 illustrates methods implemented in a communication system including a host computer, a base station and a user equipment in accordance with some embodiments.

Figure 11 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which may be those described with reference to Figures 11 and 12. For simplicity of the present disclosure, only drawing references to Figure 11 will be included in this section. In step 1510, the host computer provides user data. In substep 1511 (which may be optional) of step 1510, the host computer provides the user data by executing a host application. In step 1520, the host computer initiates a transmission carrying the user data to the UE. In step 1530 (which may be optional), the base station transmits to the UE the user data which was carried in the transmission that the host computer initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1540 (which may also be optional), the UE executes a client application associated with the host application executed by the host computer.

Figure 12 illustrates methods implemented in a communication system including a host computer, a base station and a user equipment in accordance with some embodiments.

Figure 12 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which may be those described with reference to Figures 1 1 and 12. For simplicity of the present disclosure, only drawing references to Figure 12 will be included in this section. In step 1610 of the method, the host computer provides user data. In an optional substep (not shown) the host computer provides the user data by executing a host application. In step 1620, the host computer initiates a transmission carrying the user data to the UE. The transmission may pass via the base station, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1630 (which may be optional), the UE receives the user data carried in the transmission.

Figure 13 illustrates methods implemented in a communication system including a host computer, a base station and a user equipment in accordance with some embodiments.

Figure 13 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which may be those described with reference to Figures 1 1 and 12. For simplicity of the present disclosure, only drawing references to Figure 13 will be included in this section. In step 1710 (which may be optional), the UE receives input data provided by the host computer. Additionally or alternatively, in step 1720, the UE provides user data. In substep 1721 (which may be optional) of step 1720, the UE provides the user data by executing a client application. In substep 171 1 (which may be optional) of step 1710, the UE executes a client application which provides the user data in reaction to the received input data provided by the host computer. In providing the user data, the executed client application may further consider user input received from the user. Regardless of the specific manner in which the user data was provided, the UE initiates, in substep 1730 (which may be optional), transmission of the user data to the host computer. In step 1740 of the method, the host computer receives the user data transmitted from the UE, in accordance with the teachings of the embodiments described throughout this disclosure.

Figure 14 illustrates methods implemented in a communication system including a host computer, a base station and a user equipment in accordance with some embodiments.

Figure 14 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment. The communication system includes a host computer, a base station and a UE which may be those described with reference to Figures 1 1 and 12. For simplicity of the present disclosure, only drawing references to Figure 14 will be included in this section. In step 1810 (which may be optional), in accordance with the teachings of the embodiments described throughout this disclosure, the base station receives user data from the UE. In step 1820 (which may be optional), the base station initiates transmission of the received user data to the host computer. In step 1830 (which may be optional), the host computer receives the user data carried in the transmission initiated by the base station.

Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include digital signal processors (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as read-only memory (ROM), random-access memory (RAM), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. The word “comprising” does not exclude the presence of elements or steps other than those listed in a claim,“a” or“an” does not exclude a plurality, and a single processor or other unit may fulfil the functions of several units recited in the claims. Any reference signs in the claims shall not be construed so as to limit their scope.