FIELD

The present application relates to a method, apparatus, system and computer program and in particular but not exclusively to minimizing the power consumption of an antenna array.

BACKGROUND

A communication system can be seen as a facility that enables communication sessions between two or more entities such as user terminals, base stations and/or other nodes by providing carriers between the various entities involved in the communications path. A communication system can be provided for example by means of a communication network and one or more compatible communication devices. The communication sessions may comprise, for example, communication of data for carrying communications such as voice, video, electronic mail (email), text message, multimedia and/or content data and so on. Nonlimiting examples of services provided comprise two-way or multi-way calls, data communication or multimedia services and access to a data network system, such as the Internet.

In a wireless communication system at least a part of a communication session between at least two stations occurs over a wireless link. Examples of wireless systems comprise public land mobile networks (PLMN), satellite-based communication systems and different wireless local networks, for example wireless local area networks (WLAN). Some wireless systems can be divided into cells, and are therefore often referred to as cellular systems.

A user can access the communication system by means of an appropriate communication device or terminal. A communication device of a user may be referred to as user equipment (UE) or user device. A communication device is provided with an appropriate signal receiving and transmitting apparatus for enabling communications, for example enabling access to a communication network or communications directly with other users. The communication device may access a carrier provided by a station, for example a base station of a cell, and transmit and/or receive communications on the carrier.

The communication system and associated devices typically operate in accordance with a given standard or specification which sets out what the various entities associated with the system are permitted to do and how that should be achieved. Communication protocols and/or parameters which shall be used for the connection are also typically defined. One example of a communications system is UTRAN (3G radio). Other examples of communication systems are the long-term evolution (LTE) of the Universal Mobile Telecommunications System (UMTS) radio-access technology and so-called 5G or New Radio (NR) networks. NR is being standardized by the 3rd Generation Partnership Project (3GPP).

SUMMARY

According to an aspect, there is provided an apparatus comprising means for determining a minimum number of antennas in an antenna array of a base station for using for transmissions between the base station and at least one user equipment, wherein the minimum number of antennas satisfies a quality of service requirement for the at least one user equipment served by the base station; based on the determined minimum number of antennas, determining an antenna configuration pattern of the antenna array; and using the determined antenna activation pattern for the transmissions between the at least one user equipment and the base station.

Determining the minimum number of antennas may comprise: obtaining a first dataset comprising information indicating a number of times a certain number of antennas of an antenna array were sufficient to serve a plurality of user equipments within a quality-of-service threshold; and determining the minimum number of antennas based on the first dataset.

Determining the minimum number of antennas may comprise: determining, based on the first dataset, a most probable number of antennas that is sufficient to serve the at least one user equipment; determining whether the most probable number of antennas will satisfy the quality of service threshold; and in response to determining that the determined most probable number of antennas will satisfy the quality of service threshold, selecting the most probable number of antennas as the minimum number of antennas.

The means may be for: in response to determining that the determined most probable number of antennas will not satisfy the quality of service threshold, providing feedback indicating that the determined most probable number of antennas will not satisfy the quality of service threshold; determining a next most probable number of antennas based on the first dataset and the feedback; determining whether the most next probable number of antennas will satisfy the quality of service threshold; and in response to determining that the determined next most probable number of antennas will satisfy the quality of service threshold, selecting the next most probable number of antennas as the minimum number of antennas. Determining the antenna configuration pattern may comprise: obtaining a second dataset comprising information indicating a number of times a particular antenna activation pattern comprising a subset of antennas in the antenna array is selected for serving the at least one user equipment within the quality-of-service threshold, wherein the number of antennas in the subset of antennas is dependent on the determined minimum number of antennas; and determining the antenna configuration based on the second dataset and the determined minimum number of antennas.

Determining the antenna configuration pattern based on the second dataset and the determined minimum number of antennas may comprise: determining a most probable antenna activation pattern based on second dataset that results in the highest quality of service for the at least one user equipment; determining that the most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold for the at least one user equipment; and in response to determining that the most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold, selecting the determined most probable antenna activation pattern as the antenna configuration pattern of the antenna array.

The means may be for: in response to determining that the most probable antenna activation pattern results in a quality of service that is less than the quality of service threshold, providing feedback indicating that the most probable antenna activation pattern results in a quality of service that is less than the quality of service threshold; determining a next most probable antenna activation pattern based on second dataset and the feedback; determining that the next most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold for the at least one user equipment; and in response to determining that the next most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold, selecting the determined next most probable antenna activation pattern as the antenna configuration pattern of the antenna array.

The first dataset and/or the second dataset may be based on simulations or historical measurement information.

The quality of service threshold may define a minimum data rate for the at least one user equipment. The quality of service threshold may be defined per user equipment.

According to an aspect, there is provided an apparatus comprising means for: receiving training data comprising data relating to network performance associated with different antenna pattern selections for an antenna array; performing machine learning based on the training data to determine an antenna pattern that minimizes energy consumption of the antenna array subject to one or more quality of service requirements; and outputting an indication of the determined antenna pattern.

The data relating to network performance may comprise simulation data and/or historical data.

The data relating to network performance may comprise at least one user equipment channel vector, and wherein the means may be for: averaging the at least one user equipment channel vector to produce at least one averaged user equipment channel vector, wherein the machine learning is performed based on the at least one averaged user equipment channel vector.

The data relating to network performance may comprise at least one beamforming vector, and wherein the means may be for: eigen beamforming at least one input vector to produce the at least one beamforming vector; or zero forcing the at least one input vector to produce the at least one beamforming vector, wherein the machine learning may be performed based on the at least one beamforming vector.

The training data may further comprise one or more labels corresponding to the data relating to network performance, wherein the one or labels indicate a subset of antennas of the antenna array that minimize the power consumption of the antenna array subject to an associated quality of service threshold.

Performing the machine learning may comprise implementing a supervised deep neural network model.

The deep neural network model may implement a loss function quantifying a difference between an expected outcome and the outcome produced by the model.

The loss function may comprise an asymmetric loss function.

The asymmetric loss function may have a first value when the difference between the expected outcome and the outcome produced by the model is within a first range of values and a second value when the difference between the expected outcome and the outcome produced by the model is outside the first range of values.

The loss function may be determined by: where y is the expected outcome of the machine learning model, y" is the outcome of the machine learning model, l _sym (y, y") is a categorical cross-entropy loss function and where max < Vmax otherwise and where (2, a) are tuning parameters and 1 > a > 0 and 2 > 0.

According to an aspect, there is provided an apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: determine a minimum number of antennas in an antenna array of a base station for using for transmissions between the base station and at least one user equipment, wherein the minimum number of antennas satisfies a quality of service requirement for the at least one user equipment served by the base station; based on the determined minimum number of antennas, determine an antenna configuration pattern of the antenna array; and use the determined antenna activation pattern for the transmissions between the at least one user equipment and the base station.

The at least one processor may be configured to cause the apparatus to: obtain a first dataset comprising information indicating a number of times a certain number of antennas of an antenna array were sufficient to serve a plurality of user equipments within a quality-of-service threshold; and determine the minimum number of antennas based on the first dataset.

The at least one processor may be configured to cause the apparatus to: determine, based on the first dataset, a most probable number of antennas that is sufficient to serve the at least one user equipment; determine whether the most probable number of antennas will satisfy the quality of service threshold; and in response to determining that the determined most probable number of antennas will satisfy the quality of service threshold, select the most probable number of antennas as the minimum number of antennas.

The at least one processor may be configured to cause the apparatus to: in response to determining that the determined most probable number of antennas will not satisfy the quality of service threshold, provide feedback indicating that the determined most probable number of antennas will not satisfy the quality of service threshold; determine a next most probable number of antennas based on the first dataset and the feedback; determine whether the most next probable number of antennas will satisfy the quality of service threshold; and in response to determining that the determined next most probable number of antennas will satisfy the quality of service threshold, select the next most probable number of antennas as the minimum number of antennas.

The at least one processor may be configured to cause the apparatus to: obtain a second dataset comprising information indicating a number of times a particular antenna activation pattern comprising a subset of antennas in the antenna array is selected for serving the at least one user equipment within the quality-of-service threshold, wherein the number of antennas in the subset of antennas is dependent on the determined minimum number of antennas; and determine the antenna configuration based on the second dataset and the determined minimum number of antennas.

The at least one processor may be configured to cause the apparatus to: determine a most probable antenna activation pattern based on second dataset that results in the highest quality of service for the at least one user equipment; determine that the most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold for the at least one user equipment; and in response to determining that the most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold, select the determined most probable antenna activation pattern as the antenna configuration pattern of the antenna array.

The at least one processor may be configured to cause the apparatus to: in response to determining that the most probable antenna activation pattern results in a quality of service that is less than the quality of service threshold, provide feedback indicating that the most probable antenna activation pattern results in a quality of service that is less than the quality of service threshold; determine a next most probable antenna activation pattern based on second dataset and the feedback; determine that the next most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold for the at least one user equipment; and in response to determining that the next most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold, select the determined next most probable antenna activation pattern as the antenna configuration pattern of the antenna array. The first dataset and/or the second dataset may be based on simulations or historical measurement information.

The quality of service threshold may define a minimum data rate for the at least one user equipment.

The quality of service threshold may be defined per user equipment.

According to an aspect, there is provided an apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: receive training data comprising data relating to network performance associated with different antenna pattern selections for an antenna array; perform machine learning based on the training data to determine an antenna pattern that minimizes energy consumption of the antenna array subject to one or more quality of service requirements; and output an indication of the determined antenna pattern.

The data relating to network performance may comprise simulation data and/or historical data.

The data relating to network performance may comprise at least one user equipment channel vector, and wherein the at least one processor may be configured to cause the apparatus to average the at least one user equipment channel vector to produce at least one averaged user equipment channel vector, wherein the at least one processor may be further configured to cause the apparatus to perform the machine learning based on the at least one averaged user equipment channel vector.

The data relating to network performance may comprise at least one beamforming vector, and wherein the at least one processor may be configured to cause the apparatus to: eigen beamform at least one input vector to produce the at least one beamforming vector; or zero force the at least one input vector to produce the at least one beamforming vector, wherein the at least one processor may be configured to cause the apparatus to performed the machine learning based on the at least one beamforming vector.

The training data may further comprise one or more labels corresponding to the data relating to network performance, wherein the one or labels indicate a subset of antennas of the antenna array that minimize the power consumption of the antenna array subject to an associated quality of service threshold. The at least one processor may be configured to cause the apparatus to implement a supervised deep neural network model.

The deep neural network model may implement a loss function quantifying a difference between an expected outcome and the outcome produced by the model.

The loss function may comprise an asymmetric loss function.

The asymmetric loss function may have a first value when the difference between the expected outcome and the outcome produced by the model is within a first range of values and a second value when the difference between the expected outcome and the outcome produced by the model is outside the first range of values.

The loss function may be determined by: where y is the expected outcome of the machine learning model, y" is the outcome of the machine learning model, l _sym (y, y") is a categorical cross-entropy loss function and where max < max otherwise and where (2, a) are tuning parameters and 1 > a > 0 and 2 > 0.

According to an aspect, there is provided a method comprising: determining a minimum number of antennas in an antenna array of a base station for using for transmissions between the base station and at least one user equipment, wherein the minimum number of antennas satisfies a quality of service requirement for the at least one user equipment served by the base station; based on the determined minimum number of antennas, determining an antenna configuration pattern of the antenna array; and using the determined antenna activation pattern for the transmissions between the at least one user equipment and the base station.

Determining the minimum number of antennas may comprise: obtaining a first dataset comprising information indicating a number of times a certain number of antennas of an antenna array were sufficient to serve a plurality of user equipments within a quality-of-service threshold; and determining the minimum number of antennas based on the first dataset. Determining the minimum number of antennas may comprise: determining, based on the first dataset, a most probable number of antennas that is sufficient to serve the at least one user equipment; determining whether the most probable number of antennas will satisfy the quality of service threshold; and in response to determining that the determined most probable number of antennas will satisfy the quality of service threshold, selecting the most probable number of antennas as the minimum number of antennas.

The method may comprise: in response to determining that the determined most probable number of antennas will not satisfy the quality of service threshold, providing feedback indicating that the determined most probable number of antennas will not satisfy the quality of service threshold; determining a next most probable number of antennas based on the first dataset and the feedback; determining whether the most next probable number of antennas will satisfy the quality of service threshold; and in response to determining that the determined next most probable number of antennas will satisfy the quality of service threshold, selecting the next most probable number of antennas as the minimum number of antennas.

Determining the antenna configuration pattern may comprise: obtaining a second dataset comprising information indicating a number of times a particular antenna activation pattern comprising a subset of antennas in the antenna array is selected for serving the at least one user equipment within the quality-of-service threshold, wherein the number of antennas in the subset of antennas is dependent on the determined minimum number of antennas; and determining the antenna configuration based on the second dataset and the determined minimum number of antennas.

Determining the antenna configuration pattern based on the second dataset and the determined minimum number of antennas may comprise: determining a most probable antenna activation pattern based on second dataset that results in the highest quality of service for the at least one user equipment; determining that the most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold for the at least one user equipment; and in response to determining that the most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold, selecting the determined most probable antenna activation pattern as the antenna configuration pattern of the antenna array.

The method may comprise: in response to determining that the most probable antenna activation pattern results in a quality of service that is less than the quality of service threshold, providing feedback indicating that the most probable antenna activation pattern results in a quality of service that is less than the quality of service threshold; determining a next most probable antenna activation pattern based on second dataset and the feedback; determining that the next most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold for the at least one user equipment; and in response to determining that the next most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold, selecting the determined next most probable antenna activation pattern as the antenna configuration pattern of the antenna array.

The first dataset and/or the second dataset may be based on simulations or historical measurement information.

The quality of service threshold may define a minimum data rate for the at least one user equipment.

The quality of service threshold may be defined per user equipment.

According to an aspect, there is provided a method comprising: receiving training data comprising data relating to network performance associated with different antenna pattern selections for an antenna array; performing machine learning based on the training data to determine an antenna pattern that minimizes energy consumption of the antenna array subject to one or more quality of service requirements; and outputting an indication of the determined antenna pattern.

The data relating to network performance may comprise simulation data and/or historical data.

The data relating to network performance may comprise at least one user equipment channel vector, and wherein the method may comprise: averaging the at least one user equipment channel vector to produce at least one averaged user equipment channel vector, wherein the machine learning is performed based on the at least one averaged user equipment channel vector.

The data relating to network performance may comprise at least one beamforming vector, and wherein the method may comprise: eigen beamforming at least one input vector to produce the at least one beamforming vector; or zero forcing the at least one input vector to produce the at least one beamforming vector, wherein the machine learning may be performed based on the at least one beamforming vector. The training data may further comprise one or more labels corresponding to the data relating to network performance, wherein the one or labels indicate a subset of antennas of the antenna array that minimize the power consumption of the antenna array subject to an associated quality of service threshold.

Performing the machine learning may comprise implementing a supervised deep neural network model.

The deep neural network model may implement a loss function quantifying a difference between an expected outcome and the outcome produced by the model.

The loss function may comprise an asymmetric loss function.

The asymmetric loss function may have a first value when the difference between the expected outcome and the outcome produced by the model is within a first range of values and a second value when the difference between the expected outcome and the outcome produced by the model is outside the first range of values.

The loss function may be determined by: where y is the expected outcome of the machine learning model, y" is the outcome of the machine learning model, l _sym (y, y") is a categorical cross-entropy loss function and where max < Vmax otherwise and where (2, a) are tuning parameters and 1 > a > 0 and 2 > 0.

According to an aspect, there is provided a computer readable medium comprising instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: determining a minimum number of antennas in an antenna array of a base station for using for transmissions between the base station and at least one user equipment, wherein the minimum number of antennas satisfies a quality of service requirement for the at least one user equipment served by the base station; based on the determined minimum number of antennas, determining an antenna configuration pattern of the antenna array; and using the determined antenna activation pattern for the transmissions between the at least one user equipment and the base station.

Determining the minimum number of antennas may comprise: obtaining a first dataset comprising information indicating a number of times a certain number of antennas of an antenna array were sufficient to serve a plurality of user equipments within a quality-of-service threshold; and determining the minimum number of antennas based on the first dataset.

Determining the minimum number of antennas may comprise: determining, based on the first dataset, a most probable number of antennas that is sufficient to serve the at least one user equipment; determining whether the most probable number of antennas will satisfy the quality of service threshold; and in response to determining that the determined most probable number of antennas will satisfy the quality of service threshold, selecting the most probable number of antennas as the minimum number of antennas.

The instructions, when executed by the apparatus, may cause the apparatus to further perform: in response to determining that the determined most probable number of antennas will not satisfy the quality of service threshold, providing feedback indicating that the determined most probable number of antennas will not satisfy the quality of service threshold; determining a next most probable number of antennas based on the first dataset and the feedback; determining whether the most next probable number of antennas will satisfy the quality of service threshold; and in response to determining that the determined next most probable number of antennas will satisfy the quality of service threshold, selecting the next most probable number of antennas as the minimum number of antennas.

Determining the antenna configuration pattern may comprise: obtaining a second dataset comprising information indicating a number of times a particular antenna activation pattern comprising a subset of antennas in the antenna array is selected for serving the at least one user equipment within the quality-of-service threshold, wherein the number of antennas in the subset of antennas is dependent on the determined minimum number of antennas; and determining the antenna configuration based on the second dataset and the determined minimum number of antennas.

Determining the antenna configuration pattern based on the second dataset and the determined minimum number of antennas may comprise: determining a most probable antenna activation pattern based on second dataset that results in the highest quality of service for the at least one user equipment; determining that the most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold for the at least one user equipment; and in response to determining that the most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold, selecting the determined most probable antenna activation pattern as the antenna configuration pattern of the antenna array.

The instructions, when executed by the apparatus, may cause the apparatus to further perform: in response to determining that the most probable antenna activation pattern results in a quality of service that is less than the quality of service threshold, providing feedback indicating that the most probable antenna activation pattern results in a quality of service that is less than the quality of service threshold; determining a next most probable antenna activation pattern based on second dataset and the feedback; determining that the next most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold for the at least one user equipment; and in response to determining that the next most probable antenna activation pattern results in a quality of service that is greater than or equal to the quality of service threshold, selecting the determined next most probable antenna activation pattern as the antenna configuration pattern of the antenna array.

The first dataset and/or the second dataset may be based on simulations or historical measurement information.

The quality of service threshold may define a minimum data rate for the at least one user equipment.

The quality of service threshold may be defined per user equipment.

According to an aspect, there is provided a computer readable medium comprising instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: receiving training data comprising data relating to network performance associated with different antenna pattern selections for an antenna array; performing machine learning based on the training data to determine an antenna pattern that minimizes energy consumption of the antenna array subject to one or more quality of service requirements; and outputting an indication of the determined antenna pattern.

The data relating to network performance may comprise simulation data and/or historical data. The data relating to network performance may comprise at least one user equipment channel vector, and wherein the instructions, when executed by the apparatus, may cause the apparatus to further perform: averaging the at least one user equipment channel vector to produce at least one averaged user equipment channel vector, wherein the machine learning is performed based on the at least one averaged user equipment channel vector.

The data relating to network performance may comprise at least one beamforming vector, and wherein the instructions, when executed by the apparatus, may cause the apparatus to further perform: eigen beamforming at least one input vector to produce the at least one beamforming vector; or zero forcing the at least one input vector to produce the at least one beamforming vector, wherein the machine learning may be performed based on the at least one beamforming vector.

The training data may further comprise one or more labels corresponding to the data relating to network performance, wherein the one or labels indicate a subset of antennas of the antenna array that minimize the power consumption of the antenna array subject to an associated quality of service threshold.

Performing the machine learning may comprise implementing a supervised deep neural network model.

The deep neural network model may implement a loss function quantifying a difference between an expected outcome and the outcome produced by the model.

The loss function may comprise an asymmetric loss function.

The asymmetric loss function may have a first value when the difference between the expected outcome and the outcome produced by the model is within a first range of values and a second value when the difference between the expected outcome and the outcome produced by the model is outside the first range of values.

The loss function may be determined by: where y is the expected outcome of the machine learning model, y" is the outcome of the machine learning model, l _sym (y, y") is a categorical cross-entropy loss function and where max < Vmax otherwise and where (2, a) are tuning parameters and 1 > a > 0 and 2 > 0.

According to an aspect, there is provided a non-transitory computer readable medium comprising program instructions that, when executed by an apparatus, cause the apparatus to perform at least the method according to any of the preceding aspects.

In the above, many different embodiments have been described. It should be appreciated that further embodiments may be provided by the combination of any two or more of the embodiments described above.

DESCRIPTION OF FIGURES

Embodiments will now be described, by way of example only, with reference to the accompanying Figures in which:

Figure 1 shows a representation of a network system according to some examples;

Figure 2 shows a representation of a control apparatus according to some examples;

Figure 3 shows a representation of an apparatus according to some examples;

Figure 4 shows methods according to some examples;

Figure 5 shows a first dataset according to some examples;

Figure 6 shows a second dataset according to some examples;

Figure 7 shows some example antenna patterns for an antenna array;

Figure 8 shows a method according to some examples;

Figure 9 shows confusion matrices according to some examples;

Figure 10 shows an example comparison of computational complexity for different computational methods;

Figure 11 shows an example comparison of average power consumption of an antenna array for different power minimization schemes; and

Figure 12 shows a method according to some examples.

DETAILED DESCRIPTION

In the following certain examples are explained with reference to mobile communication devices capable of communication via a wireless cellular system and mobile communication systems serving such mobile communication devices. Before explaining in detail the examples, certain general principles of a wireless communication system, access systems thereof, and mobile communication devices are briefly explained with reference to Figures 1 , 2 and 3 to assist in understanding the technology underlying the described examples.

Figure 1 shows a schematic representation of a 5G system (5GS). The 5GS may be comprised by a terminal or user equipment (UE), a 5G radio access network (5GRAN) or next generation radio access network (NG-RAN), a 5G core network (5GC), one or more application function (AF) and one or more data networks (DN).

The 5G-RAN may comprise one or more gNodeB (GNB) or one or more gNodeB (GNB) distributed unit functions connected to one or more gNodeB (GNB) centralized unit functions. The 5GC may comprise the following entities: Network Slice Selection Function (NSSF); Network Exposure Function; Network Repository Function (NRF); Policy Control Function (PCF); Unified Data Management (UDM); Application Function (AF); Authentication Server Function (AUSF); an Access and Mobility Management Function (AMF); and Session Management Function (SMF).

Figure 2 illustrates an example of a control apparatus 200 for controlling a function of the 5GRAN or the 5GC as illustrated on Figure 1. The control apparatus may comprise at least one random access memory (RAM) 211 a, at least on read only memory (ROM) 21 1 b, at least one processor 212, 213 and an input/output interface 214. The at least one processor 212, 213 may be coupled to the RAM 21 1 a and the ROM 21 1 b. The at least one processor 212, 213 may be configured to execute an appropriate software code 215. The software code 215 may for example allow to perform one or more steps to perform one or more of the present aspects. The software code 215 may be stored in the ROM 211 b. The control apparatus 200 may be interconnected with another control apparatus 200 controlling another function of the 5GRAN or the 5GC. In some examples, each function of the 5GRAN or the 5GC comprises a control apparatus 200. In alternative examples, two or more functions of the 5GRAN or the 5GC may share a control apparatus.

Figure 3 illustrates an example of a terminal 300, such as the terminal illustrated on Figure 1 . The terminal 300 may be provided by any device capable of sending and receiving radio signals. Non-limiting examples comprise a user equipment, a mobile station (MS) or mobile device such as a mobile phone or what is known as a ’smart phone’, a computer provided with a wireless interface card or other wireless interface facility (e.g., USB dongle), a personal data assistant (PDA) or a tablet provided with wireless communication capabilities, a machine-type communications (MTC) device, an Internet of things (loT) type communication device or any combinations of these or the like. The terminal 300 may provide, for example, communication of data for carrying communications. The communications may be one or more of voice, electronic mail (email), text message, multimedia, data, machine data and so on.

The terminal 300 may receive signals over an air or radio interface 307 via appropriate apparatus for receiving and may transmit signals via appropriate apparatus for transmitting radio signals. In Figure 3 transceiver apparatus is designated schematically by block 306. The transceiver apparatus 306 may be provided for example by means of a radio part and associated antenna arrangement. The antenna arrangement may be arranged internally or externally to the mobile device.

The terminal 300 may be provided with at least one processor 301 , at least one memory ROM 302a, at least one RAM 302b and other possible components 303 for use in software and hardware aided execution of tasks it is designed to perform, including control of access to and communications with access systems and other communication devices. The at least one processor 301 is coupled to the RAM 302b and the ROM 302a. The at least one processor 301 may be configured to execute an appropriate software code 308. The software code 308 may for example allow to perform one or more of the present aspects. The software code 308 may be stored in the ROM 302a.

The processor, storage and other relevant control apparatus can be provided on an appropriate circuit board and/or in chipsets. This feature is denoted by reference 304. The device may optionally have a user interface such as key pad 305, touch sensitive screen or pad, combinations thereof or the like. Optionally one or more of a display, a speaker and a microphone may be provided depending on the type of the device.

One concern in 5G and beyond is how to achieve a sustainable and low CO2 footprint of future networks by reducing the power consumption of the network. At the wireless front, one of the main causes of high energy consumption is the radio power amplifiers. Therefore if the energy consumption of the radio power amplifiers could be reduced, the sustainability of the network may be improved.

Some studies have shown that in many scenarios in a cell, the number of active antennas of a base station in a network could be reduced, which may help reduce the energy consumption of the network. At each given time instance, a base station (BS) sends data for a list of UE’s. The choice of which set of UEs to be scheduled concurrently may be determined by a scheduler, based on criteria such as the UEs’ channels, service quality, and fairness.

In some cases, the number of RF chains (which include the radio power amplifiers) necessary to achieve a nominal throughput rate may be over dimensioned. In other words, only in certain channel and cell conditions (e.g. terrible channel conditions and fully loaded cells), there may be a need to transmit signals by using all RF chains. In other channel and cell conditions, it may not be necessary to utilize all RF chains.

Therefore there may be optimizations that can be made to try and minimize the number of active antennas in the antenna array, subject to a minimum guaranteed quality of service. Some examples may provide methods, apparatuses, and computer program products to provide such optimizations.

Reference is made to Figure 4, which shows methods according to some examples.

At 400, a method comprises determining a minimum number of antennas in an antenna array of a base station for using for transmissions between the base station and at least one user equipment, wherein the minimum number of antennas satisfies a quality of service requirement for the at least one user equipment served by the base station.

At 402, the method comprises, based on the determined minimum number of antennas, determining an antenna configuration pattern of the antenna array.

At 404, the method comprises using the determined antenna activation pattern for the transmissions between the at least one user equipment and the base station.

At 406, a method comprises receiving training data comprising data relating to network performance associated with different antenna pattern selections for an antenna array.

At 408, the method comprises performing machine learning based on the training data to determine an antenna pattern that minimizes energy consumption of the antenna array subject to one or more quality of service requirements.

At 410, the method comprises outputting an indication of the determined antenna pattern. Some examples may consider an antenna selection method to reduce power consumption of a massive multi-input multi-output (MIMO) system while ensuring minimum throughput requirement. To reduce the complexity of the antenna selection method (which searches over all combinations of active antennas for all scheduled), some examples may “learn” from data, by building prior information.

Some examples may use historical data to find a prior distribution over the solution space to minimize the expected computational complexity. Some examples may comprise an algorithm to use this data to reduce the computational complexity.

Some examples may be adjusted for wide range of limits on the available computational complexity; i.e. the more computation power that is available, the more refined the search over the space of solutions may be.

Some examples may aim to minimize the energy consumption of a massive MIMO antenna array by reducing the number of active antennas in the antenna array while guaranteeing the user QoS requirements.

To this end, in some examples the following optimization problem may be formulated, where the number of active antennas is minimized such that achievable per-user throughputs are higher than a given threshold, which captures (QoS) guarantee.

Here, e = [ei , 62, 6M-I] is a binary vector which denotes the active antenna indices and P(e) is the total number of active antennas. Here emin is the minimum number of active antennas to be active which depends on the number of co-scheduled users, threshold is a threshold quality of service (for example a data rate) for the users of the antenna. which may be huge.

Due to the nature of the integer values of the number of antennas, the optimization problem’s complexity grows as the number of antennas grow. The geometry of the optimization problem, i.e. channels of the UEs, their relative positions, etc. can determine the choice of antennas.

Given that for a given cell, the UEs channel takes up only certain pattern (that is to say, UEs are not uniformly distributed in every possible 3D space around the cell), the choice of antennas is also non-uniform.

As mentioned previously, in some examples two different sets of data (a first data set, referred to as datasetl herein, and a second data set, referred to as dataset 2 herein) may be used to solve the optimization problem for minimizing the number of active antennas within a certain QoS threshold.

Datasetl and dataset2 may be based on information obtained from simulations, or from information obtained from real-life measurements of network performance.

Figure 5 shows an example of datasetl in the form of a histogram showing the number of times a certain number of antennas of a MIMO array were sufficient to serve a UE within a certain threshold. The certain threshold may be a quality of service (QoS) threshold defined by the network operator.

In particular, Figure 5 shows data that has been obtained from simulations over 120 drops of an Urban Micro channel at 3.5GHz, using an AEQB radio unit (192AEs, 64TRX), 273PRB, rate_threshold = 0.3e6, where for each case the best minimum set up of the antennas needed was acquired. A “drop” in this context is an instance of the simulation having a random channel, random scatterer, and random UE positions. In the example of Figure 5, within each drop, the UEs were allowed to move with a speed of 3km/hr in a random direction. By obtaining data from 120 drops, the data obtained may be considered to be generic, and not overly representing a specific environmental situation.

In this setup the minimum allowed number of antenna is 4, which is equal to the minimum number of UE’s served.

The data was obtained using a Monte Carlo simulation and based on exhaustive brute force search, where the occurrence of a certain amount of antennas was recorded and normalized such as to sum up to 1 , to represent a probability of being chosen. For a given number of antennas ek, a pattern of active antennas was selected at random and saved in a list. This list contains M-e _m in number of patterns of antennas. A brute force algorithm was run through this list to find out the frequency of the use of the number of antennas for the optimization problem.

Figure 5 shows that certain combinations of antennas are selected more often that the others.

This histogram may be used when selecting the number of antennas.

Figure 6 shows an example of dataset2, where the most selected antennas are shown for large scale drops of different channels and user equipments being scheduled in the cell within the same threshold as dataset 1 .

More specifically, Figure 6 shows a histogram of a probability mass function over an antenna panel comprising 4x8 antennas. The darker the colour of an antenna represents a higher chance of the antenna being selected. Two different histograms are shown - one according to a brute-force or other similar algorithm (top) and one based on a supervised DNN approach (bottom).

The example dataset2 shown in Figure 6 was obtained using a Monte Carlo simulation and based on exhaustive brute force search, using the same number of drops and simulation parameters as for datasetl described above in relation for Figure 5. For a fixed number of allowed active antenna, the occurrence of the amount of time a specific antenna constellation or antenna pattern being used was be recorded.

For a given number antennas ek, there may be a choice of All the patterns were built and the frequency of the times a certain patterns p_k out of results in higher rates compared to other patterns for the same number of antennas was determined.

For example, for the scenario used to compute the histogram of Figure 6, eight antennas from an 8x4 antenna array were used, and a search was performed through the patterns shown in Figure 7.

In some examples, the channel vectors of all co-scheduled UEs in any given time, H _k (t) may be recorded offline, and may be input through a heuristic algorithm. The heuristic algorithm may be any heuristic algorithm. At each TTI the heuristic algorithm may output a subset of antennas that could be used to satisfy the rate requirements R(e_k) while minimizing the total power consumption.

For example, the unprocessed recorded dataset from PHY layer may appear as follows:

To record this dataset, the output of a channel estimation unit and the number of UEs scheduled may be required, which may be an output of the scheduler unit.

For datasetl , the antenna number (cardinality of subsets P(.)) of the selected antennas may be computed, where P(t) is a subset containing the indexes of the antennas that are selected by the heuristic algorithm.

To build the datasetl , the histogram of the cardinality of the |P(t)| may be computed. An example of this dataset is shown below:

For dataset2, for any given number of antennas in datasetl , a histogram over different configurations (pattern) of the antennas may be computed. For instance, for |P| = 8, different highly likely antenna configurations may be determined, for example as shown in Figure 7.

An example of this dataset is shown below:

In some examples, a method for minimizing the number of active antennas within a certain QoS threshold may comprise an offline phase.

In some examples the datasets may be generated in the offline phase. An algorithm, such as a heuristic algorithm or a brute force algorithm, may be used to produce the datasets as discussed previously. Figure 8 shows an example method, which may also be referred to as an algorithm herein, according to some examples. In some examples, the method may be performed at a base station.

At 800, the method comprises determining a minimum number of antennas in an antenna array of a base station that satisfies a minimum QoS requirement for at least one user equipment served by the base station. In some examples the at least one user equipment may comprise all co-scheduled user equipments served by the base station.

As shown in Figure 8, in some examples the determining step 800 may comprise, at 800a, determining the following inputs:

1 ) one or more channels of the UE’s to be scheduled;

2) a QoS or rate threshold for the UE’s, which in some examples may be per UE;

3) a number of the co-scheduled UE’s per transmission slot;

4) datasetl ; and

5) dataset2.

Datasetl and dataset2 may be determined as discussed previously.

In some examples, the one or more channels, QoS/rate requirement, and number of coscheduled UEs per slot may be determined by the network operator or other functionalities of the network (e.g. scheduler).

At 800b, the determining step 800 may comprise determining a most probable number of antennas ek based on datasetl and the number of co-scheduled UEs per transmission slot.

At 800c, the determining step 800 may comprise determining whether the number of antennas determined at 800b satisfies the QoS/rate threshold for the users.

When the determination at 800c finds that the QoS/rate threshold is not satisfied, at 800d the determining step 800 may comprise providing feedback and repeating the determination step 800c to determine the next most probable number of antennas based on the feedback.

When the determination at 800c finds that the QoS/rate threshold is satisfied, the number of antennas determined at 800b is selected as the minimum number of antennas, and the method proceeds to step 802. At 802 the method comprises determining an antenna configuration pattern of the antenna array based on the determined minimum number of antennas.

At 802a, the determining step 802 may comprise determining, based on dataset2, the most probable pattern of antennas Pk that results in the highest QoS.

At 802b, the determining step 802 may comprise determining whether the pattern determined at 802a satisfies the QoS threshold requirements.

When the determination at 802b finds that the threshold QoS is not satisfied, at 802c the determining step 802 may comprise providing feedback and repeating the determination step 802a to determine the next most probable pattern of antennas based on the feedback.

When the determination at 802b finds that the threshold QoS is satisfied, at 804 the method comprises using the determined pattern of antennas for transmissions to and from the base station.

In some examples, different UEs served by the base station may have different rate/QoS requirements. In some examples, the threshold on the rate/QoS requirements may be defined per user. To increase performance of the system for higher rate/QoS requirements, in some examples a parallel Datasetl may be provided, which may be acquired using the higher rate/QoS requirement.

In some examples, a Neural Network (NN) model may be used to perform antenna selection.

Reference is made to Figure 12, which shows a method according to some examples, where a NN model is used

At 1200, the method comprises receiving a set of training data. The training data may comprise one or more inputs and may further comprise one or more labels.

In some examples, the one or more inputs may comprise data relating to network performance associated with different antenna pattern selections for an antenna array.

For example, the one or more inputs may comprise simulation data (e.g. the data used to produce datasetl and dataset2 described previously) such as averaged channels and beam forming vectors obtained from simulations described previously. In other examples, the input data may comprise historical data obtained from previous antenna array selections and subsequent measurements of network performance (e.g. QoS per UE) resulting from the selection.

In some examples, pre-processing may be performed on the input data before providing the pre-processed input data to the model. Pre-processing may help in convergence of the NN model. Furthermore, in some examples asymmetric loss may be designed into the model to enforce the constraints on the QoS in the main optimization problem for performing antenna selection.

In some examples, input pre-processing may comprise averaging UE channel vectors (input) to produce averaged channel vectors. The averaging may be over PRB and user antenna dimensions. The averaging may be over both polarizations of the vectors. The averaging may produce a matrix H [M x K], where M is the number of full array antennas and K is the number of user equipments.

The channel vectors may be given as H e C ^A {M x K x N_{sub}}, where N_{sub} is the number of subcarriers. To save the complexity of the NN and the input size, an average over the PRBs may be taken.

Therefore, the channel input becomes of the size:

H = [h _1; h ₂ , ... h _K ] e C ^{M x /<}

The pre-processed matrix H may be provided as an input to the model.

In some examples, the model is provided with a further input comprising beamforming vectors. In some examples, these beamforming vectors are Eigen beamforming vectors.

In some examples, eigen beamforming vectors of the co-scheduled users may be performed.

The Eigen beamforming may produce a matrix W [M x K],

The matrix W may be produced by zero-forcing matrix H. For example:

For eigen beamforming the input may be built by finding the eigenvector corresponding to the largest eigenvalue of the channel spatial covariance matrix: Rk = h^h _k

In some examples, real and imaginary components may be fed into the model separately.

In some examples, for each input, a corresponding label may be provided. The label may be the QoS/rate-achieving power minimizing subset of the antennas. The labels may be produced by any algorithm. The labels may be included in the training data.

Thus, in some examples, the model may receive training data comprising the pre-processed channel vectors (H), eigen beamforming vectors (W), and the corresponding labels.

At 1202, the method comprises performing machine learning based on the training data to determine an antenna pattern that minimizes energy consumption subject to QoS/rate threshold requirements. In some examples, the performing may comprise implementing a supervised deep neural network (DNN) model.

In some examples, a loss function may be incorporated into the model when implementing machine learning to achieve faster convergence of the model.

The loss function may quantify a difference between an expected outcome and the outcome produced by the model. Some commonly chosen loss functions, such as categorical crossentropy loss function, may produce errors that might lead to underperformance when it comes to throughput. As a remedy, some examples introduce an additional term in the loss function to increase the achievable throughput.

A loss function (also referred to as the “new loss function” herein) according to some examples may comprise an asymmetric loss function. The asymmetric loss function may have a first value when the difference between the expected outcome and the outcome produced by the model is within a first range of values and a second value when the difference between the expected outcome and the outcome produced by the model is outside the first range of values.

In some examples the new loss function may be defined as follows:

Where y is the expected outcome of the model, y"is the outcome of the model l_sym (y, y") is the original categorical cross-entropy loss function, and where max < Vmax otherwise

This example asymmetric loss function term may enforce the condition on the throughput from the optimization problem implicitly. (2, a) are tuning parameters and in some examples may be chosen to be 1 > a > 0 and 2 > 0. It should be understood that in some examples, the asymmetric loss function may have different values to those explicitly stated above and different values of tuning parameters to those explicitly stated above may also be used.

At 1204, the method comprises outputting an indicating of the antenna pattern selection. The output may approximate the output of the method described previously in relation to Figure 8.

Figure 9 shows example confusion matrices obtained by performing simulations on 1 e6 data points when using a conventional cross-entropy loss function 900 compared to the example loss function 902 described above. The diagonal running from (0,0) to (7,7) represents the correct prediction of the class (antenna pattern) by the model, while the off-diagonals represent mis-classification of a certain class to another class.

In the example of Figure 9the scale is a value between [0,1], where a solution which provides 1 at the diagonal vector (0,0) to (7,7) and 0 elsewhere indicates a perfect solution, i.e. each class is predicted 100% of the times correctly to its value or y = y ^A all the time.

However, in practice there may be some off-diagonal values, i.e. certain classes and instances of y are misclassified as y ^A , where y != y ^A . This may be unavoidable using any classifier for this problem.

Mistakes in classification may be either y<y ^A (number of predicted antennas y ^A is more than the minimum necessary number of antennas y) or y>y ^A (number of predicted antennas y ^A is less than the minimum necessary number of antennas y). In the matrices shown in Figure 9, the “triangle” below the diagonal (0,0) to (7,7) represents incorrect classifications where y>y ^A , and the “triangle” above the diagonal (0,0) to (7,7) represents incorrect classifications where y<y ^A .

In some examples it may be preferable for mistakes to be towards y<y ^A rather than y>y ^A , as providing more antennas than needed will result in a better QoS. That is to say, it may be preferable to aim to reduce the amount of times that the QoS threshold requirement is violated. In Figure 9, matrix 902 shows that designing asymmetric loss function as described above allows for shift of the frequency off diagonal mistakes toward upper triangle (as indicated by the arrow) where y<y ^A as compared to the cross-entropy function shown in matrix 900.

For example, comparing the predictions at (5, 6), which represents an incorrect classification where y>y ^A , and (6,5) which represents an incorrect prediction where y<y ^A , it can be seen that the classifications in matrix 900 for the cross entropy loss function are equally balanced - that is to say that the cross entropy loss function wrongly classifies y<y ^A and y>y ^A equally. However, for the same classifications (5,6) and (6,5) matrix 902 for the asymmetric loss function described above, it can be seen that the incorrect classifications y<y ^A are predicted more than incorrect classifications y>y ^A .

The (alpha, lambda) experimental values for these simulations were, (0.09, 0.2).

The performance of the Neural Network on simulated data, showing the accuracy of achieving the target antenna subset, is shown below:

In this case, the classification defines the percentage of cases where the predicted class (antenna pattern) is correct.

The percentage of points that satisfied a given QoS for each loss function is shown below:

Thus it can be seen that while the accuracy (i.e. the frequency of the predicted pattern of antennas being correct) of retraining the model using the new loss function is decreased compared to the cross-entropy loss function; however, the new loss function also results in a higher percentage of points satisfying a given QoS requirement as compared to the crossentropy loss function. Thus the new loss function provides a trade-off between the accuracy of antenna panel pattern selection being slightly lower than cross-entropy loss function, but results in a higher user QoS.

For example, if the model predicts that 6 antennas is the minimum number of antennas that would provide a sufficient QoS, and in reality 5 antennas would have been sufficient, then the model is considered to have made a classification error, but not a QoS error. However if the model predicts that 4 antennas is the minimum number of antennas that would provide a sufficient QoS, and in reality 5 antennas would have been sufficient, then the model is considered to have made a classification error and a QoS error.

Figure 10 shows a comparison of the computational complexity (in terms of an average FLOP count per second) of the NN model described above (DNN) as compared to other methods including a so called “Greedy transmit antenna selection (TAS)” approach, a reduction in complexity of a factor of over 1000 may be achieved; and when compared to a “Fixed Columns” approach, a reduction in complexity of a factor of 24 may be achieved.

Figure 1 1 shows that, in terms of an average power consumption for a BS analogue RF frontend comparison with different TAS methods, the NN model described above may result in a reduction in power consumption of more than a factor of 3 compared to utilising the full array. While some other approaches, such as the “Greedy TAS” and “Fixed Columns” approach may achieve a higher power consumption reduction, this is balanced by the significant reduction in computational complexity provided by using the NN model described herein. As referenced above, simulations were performed using the following system configurations:

Thus, some examples provide methods for performing antenna selection to reduce the power consumption of the antenna array, subject to minimum QoS/rate requirements. The examples may comprise using a heuristic algorithm or a NN model.

In some examples, there is provided an apparatus comprising determining a minimum number of antennas in an antenna array of a base station for using for transmissions between the base station and at least one user equipment, wherein the minimum number of antennas satisfies a quality of service requirement for the at least one user equipment served by the base station; based on the determined minimum number of antennas, determining an antenna configuration pattern of the antenna array; and using the determined antenna activation pattern for the transmissions between the at least one user equipment and the base station. In some examples, the apparatus may comprise at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: determine a minimum number of antennas in an antenna array of a base station for using for transmissions between the base station and at least one user equipment, wherein the minimum number of antennas satisfies a quality of service requirement for the at least one user equipment served by the base station; based on the determined minimum number of antennas, determine an antenna configuration pattern of the antenna array; and use the determined antenna activation pattern for the transmissions between the at least one user equipment and the base station.

In some examples, there is provided an apparatus comprising means for: receiving training data comprising data relating to network performance associated with different antenna pattern selections for an antenna array; performing machine learning based on the training data to determine an antenna pattern that minimizes energy consumption of the antenna array subject to one or more quality of service requirements; and outputting an indication of the determined antenna pattern.

In some examples the apparatus may comprise at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: receive training data comprising data relating to network performance associated with different antenna pattern selections for an antenna array; perform machine learning based on the training data to determine an antenna pattern that minimizes energy consumption of the antenna array subject to one or more quality of service requirements; and output an indication of the determined antenna pattern.

It should be understood that the apparatuses may comprise or be coupled to other units or modules etc., such as radio parts or radio heads, used in or for transmission and/or reception. Although the apparatuses have been described as one entity, different modules and memory may be implemented in one or more physical or logical entities.

It is noted that whilst some examples have been described in relation to 5G networks, similar principles can be applied in relation to other networks and communication systems. Therefore, although certain examples were described above by way of example with reference to certain example architectures for wireless networks, technologies and standards, examples may be applied to any other suitable forms of communication systems than those illustrated and described herein. It is also noted herein that while the above describes examples, there are several variations and modifications which may be made to the disclosed solution without departing from the scope of the present invention.

As used herein, “at least one of the following: <a list of two or more elements>” and “at least one of <a list of two or more elements>” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

In general, the various embodiments may be implemented in hardware or special purpose circuitry, software, logic or any combination thereof. Some aspects of the disclosure may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the disclosure is not limited thereto. While various aspects of the disclosure may be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

As used in this application, the term “circuitry” may refer to one or more or all of the following:

(a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry) and

(b) combinations of hardware circuits and software, such as (as applicable):

(i) a combination of analog and/or digital hardware circuit(s) with software/fi rmware and

(ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and

(c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.”

This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

The embodiments of this disclosure may be implemented by computer software executable by a data processor of the mobile device, such as in the processor entity, or by hardware, or by a combination of software and hardware. Computer software or program, also called program product, including software routines, applets and/or macros, may be stored in any apparatus- readable data storage medium and they comprise program instructions to perform particular tasks. A computer program product may comprise one or more computer-executable components which, when the program is run, are configured to carry out embodiments. The one or more computer-executable components may be at least one software code or portions of it.

Further in this regard it should be noted that any blocks of the logic flow as in the Figures may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on such physical media as memory chips, or memory blocks implemented within the processor, magnetic media such as hard disk or floppy disks, and optical media such as for example DVD and the data variants thereof, CD. The physical media is a non-transitory media.

The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal ) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

The memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory. The data processors may be of any type suitable to the local technical environment, and may comprise one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASIC), FPGA, gate level circuits and processors based on multi core processor architecture, as non-limiting examples.

Embodiments of the disclosure may be practiced in various components such as integrated circuit modules. The design of integrated circuits is by and large a highly automated process. Complex and powerful software tools are available for converting a logic level design into a semiconductor circuit design ready to be etched and formed on a semiconductor substrate. The scope of protection sought for various embodiments of the disclosure is set out by the independent claims. The embodiments and features, if any, described in this specification that do not fall under the scope of the independent claims are to be interpreted as examples useful for understanding various embodiments of the disclosure.

The foregoing description has provided by way of non-limiting examples a full and informative description of the exemplary embodiment of this disclosure. However, various modifications and adaptations may become apparent to those skilled in the relevant arts in view of the foregoing description, when read in conjunction with the accompanying drawings and the appended claims. However, all such and similar modifications of the teachings of this disclosure will still fall within the scope of this invention as defined in the appended claims. Indeed, there is a further embodiment comprising a combination of one or more embodiments with any of the other embodiments previously discussed.