Dynamic adaptation of time windows for beam prediction

Abstract

A wireless communication device (121) and a method performed by a wireless communications device (121) for reporting beam predictions to a wireless node (111, 122), where the method comprises obtaining (701), a first configuration of a prediction time window within which beam prediction for the wireless node (111, 122) is to be 5 determined, transmitting (703) an indication of a proposed prediction time window to the wireless node, within which beam prediction for the wireless node (111, 122) is to be determined, receiving (704), from the wireless node (111, 122), a second updated configuration of the prediction time window and reporting (705) a beam prediction to the wireless node, wherein the beam prediction comprises an indication of at least one 10 predicted beam to be transmitted from the wireless node (111, 122) within the prediction time window in the second updated configuration. A network node (111, 122) and a method performed by the network node for reporting beam predictions to the wireless node (111, 122) is also disclosed.

Description

[0001] DYNAMIC ADAPTATION OF TIME WINDOWS FOR BEAM PREDICTION

[0002] TECHNICAL FIELD

[0003] The present solution is related to a method performed by wireless communication device and a wireless communication device for reporting beam predictions to a wireless node. Also, the present solution is related to a method performed by a wireless node and a wireless node for handling beam prediction for the wireless node.

[0004] Moreover, the present solution is related to a computer program which performs the method when executed on the wireless communication device or the wireless node.

[0005] BACKGROUND

[0006] In a typical wireless communication network, wireless devices, also known as wireless communication devices, mobile stations, stations (STA) and / or User Equipments (UE), communicate via a Local Area Network such as a Wi-Fi network or a Radio Access Network (RAN) with one or more core networks (CN). The RAN covers a geographical area which is divided into service areas or cell areas, which may also be referred to as a beam or a beam group, with each service area or cell area being served by a radio access node such as a radio access node e.g., a Wi-Fi access point or a radio base station (RBS), which in some networks may also be denoted, for example, a NodeB, eNodeB (eNB), or gNB as denoted in 5G. A service area or cell area is a geographical area where radio coverage is provided by the radio access node. The radio access node communicates over an air interface operating on radio frequencies with the wireless device within range of the radio access node.

[0007] Specifications for the Evolved Packet System (EPS), also called a Fourth Generation (4G) network, have been completed within the 3rd Generation Partnership Project (3GPP) and this work continues in the coming 3GPP releases, for example to specify a Fifth Generation (5G) network also referred to as 5G New Radio (NR). The EPS comprises the Evolved Universal Terrestrial Radio Access Network (E-UTRAN), also known as the Long Term Evolution (LTE) radio access network, and the Evolved Packet Core (EPC), also known as System Architecture Evolution (SAE) core network. E-UTRAN / LTE is a variant of a 3GPP radio access network wherein the radio access nodes are directly connected to the EPC core network rather than to RNCs used in 3G networks. In general, in E-UTRAN / LTE the functions of a 3G RNC are distributed between the radio access nodes, e.g. eNodeBs in LTE, and the core network. As such, the RAN of an EPS has an essentially “flat” architecture comprising radio access nodes connected directly to one or more core networks, i.e. they are not connected to RNCs. To compensate for that, the E-UTRAN specification defines a direct interface between the radio access nodes, this interface being denoted the X2 interface.

[0008] Wireless communication systems in 3GPP

[0009] Figure 1 illustrates a simplified wireless communication system with a UE 12, which communicates with one or multiple access nodes 103-104, which in turn is connected to a network node 106. The access nodes 103-104 are part of a radio access network 10.

[0010] For wireless communication systems pursuant to 3GPP Evolved Packet System, (EPS), also referred to as Long Term Evolution, LTE, or 4G, standard specifications, such as specified in 3GPP TS 36.300 and related specifications, the access nodes 103-104 correspond typically to an Evolved NodeBs (eNBs) and the network node 106 corresponds typically to either a Mobility Management Entity (MME) and / or a Serving Gateway (SGW). The eNB is part of the radio access network 10, which in this case is the E-UTRAN (Evolved Universal Terrestrial Radio Access Network), while the MME and SGW are both part of the EPC (Evolved Packet Core network). The eNBs are interconnected via the X2 interface, and connected to EPC via the S1 interface, more specifically via S1-C to the MME and S1-U to the SGW.

[0011] For wireless communication systems pursuant to 3GPP 5G System, 5GS (also referred to as New Radio, NR, or 5G) standard specifications, such as specified in 3GPP TS 38.300 and related specifications, on the other hand, the access nodes 103-104 corresponds typically to an 5G NodeB (gNB) and the network node 106 corresponds typically to either an Access and Mobility Management Function (AMF) and / or a User Plane Function (UPF). The gNB is part of the radio access network 10, which in this case is the NG-RAN (Next Generation Radio Access Network), while the AMF and UPF are both part of the 5G Core Network (5GC). The gNBs are inter-connected via the Xn interface, and connected to 5GC via the NG interface, more specifically via NG-C to the AMF and NG-U to the UPF.

[0012] To support fast mobility between NR and LTE and avoid change of core network, LTE eNBs may also be connected to the 5G-CN via NG-U / NG-C and support the Xn interface. An eNB connected to 5GC is called a next generation eNB (ng-eNB) and is considered part of the NG-RAN. LTE connected to 5GC will not be discussed further in this document; however, it should be noted that most of the solutions / features described for LTE and NR in this document also apply to LTE connected to 5GC. In this document, when the term LTE is used without further specification it refers to LTE-EPC.

[0013] Beam management procedure

[0014] In a high frequency range, such as 3gpp Frequency Range 2 (FR2), multiple RF beams may be used to transmit and receive signals at a gNB and a UE. For each downlink (DL) beam of the gNB, there is typically an associated best UE receiving (Rx) beam for receiving signals on the DL beam. The DL beam and the associated UE Rx beam form a beam pair. The beam pair can be identified through a so-called beam management (BM) process in NR.

[0015] A DL beam may identified by an associated DL reference signal (RS) transmitted on the beam, either periodically, semi-persistently, or aperiodically. According to some 3gpp standardisations DL RS used for that purpose may be a Synchronization Signal (SS) and Physical Broadcast Channel (PBCH) block (SSB) or a Channel State Information RS (CSI-RS). By measuring all the DL RSs, the UE may determine and report to the gNB the best DL beam to use for DL transmissions. The gNB may then transmit a burst of DL-RS using the reported best DL beam to let the UE evaluate candidate UE RX beams.

[0016] Although not explicitly stated in the NR 3GPP specification, beam management has been divided into three procedures, schematically illustrated in Figure 2 and described below.

[0017] P-1: In this procedure the purpose is to find a coarse direction for the UE using wide gNB transmission (Tx) beam covering the whole angular sector.

[0018] P-2: In this procedure the purpose is to refine the gNB TX beam by doing a new beam search around the coarse direction found in P1.

[0019] P-3: This procedure is used for a UE that has analog beamforming to let the UE find a suitable UE RX beam.

[0020] P-1 is expected to use beams with rather large beamwidths and where the beam reference signals are transmitted periodically and are shared between all UEs of the cell served by the base station transmitting the beams to the UEs. Some reference signals that may be used for P-1 are periodic CSI-RS or SSB. The UE then reports N best beams to the gNB and their corresponding Reference Signal Received Power (RSRP) values.

[0021] P-2 is expected to use aperiodic / or semi-persistent CSI-RS transmitted in narrow beams around the coarse direction found in P-1. P-3 is expected to use aperiodic or semi-persistent CSI-RSs repeatedly transmitted in one narrow gNB beam. One alternative way is to let the UE determine a suitable UE RX beam based on periodic SSB transmissions. Since each SSB consists of four OFDM symbols, a maximum of four UE RX beams may be evaluated during each SSB burst transmission. One benefit of using SSB instead of CSI-RS is that no extra overhead of CSI-RS transmission is needed.

[0022] Reference signal configurations

[0023] CSI-RS:

[0024] A CSI-RS is transmitted over each transmit (Tx) antenna port at the network node and for different antenna ports. The CSI-RSs are multiplexed in time, frequency, and code domain such that the channel between each Tx antenna port at the network node and each receive antenna port at the UE may be measured by the UE. The time-frequency resource used for transmitting CSI-RS is referred to as a CSI-RS resource.

[0025] In NR, the CSI-RS for beam management is defined as a 1- or 2-port CSI-RS resource in a CSI-RS resource set where a field repetition is present. The following three types of CSI-RS transmissions are supported:

[0026] • Periodic CSI-RS: CSI-RS is transmitted periodically in certain slots. This CSI-RS transmission is semi-statically configured using Radio Resource Control (RRC) signaling with parameters such as CSI-RS resource, periodicity, and slot offset.

[0027] • Semi-Persistent CSI-RS: Similar to periodic CSI-RS, resources for semi- persistent CSI-RS transmissions are semi-statically configured using RRC signaling with parameters such as periodicity and slot offset. However, unlike periodic CSI-RS, dynamic signaling is needed to activate and deactivate the CSI- RS transmission.

[0028] • Aperiodic CSI-RS: This is a one-shot CSI-RS transmission that may happen in any slot. Here, one-shot means that CSI-RS transmission only happens once per trigger. The CSI-RS resources (i.e. , the RE locations which consist of subcarrier locations and OFDM symbol locations) for aperiodic CSI-RS are semi-statically configured. The transmission of aperiodic CSI-RS is triggered by dynamic signaling through PDCCH using the CSI request field in UL Downlink Control Information (DCI), in the same DCI where the UL resources for the measurement report are scheduled. Multiple aperiodic CSI-RS resources may be included in a CSI-RS resource set and the triggering of aperiodic CSI-RS is on a resource set basis. SSB:

[0029] In NR, an SSB consists of a pair of synchronization signals (SSs), a physical broadcast channel (PBCH), and a Demodulation Reference Signal (DMRS) for the PBCH. An SSB is mapped to four consecutive OFDM symbols in the time domain and 240 contiguous subcarriers (20 RBs) in the frequency domain.

[0030] To support beamforming and beam-sweeping for SSB transmission in NR, a cell may transmit multiple SSBs in different narrow-beams in a time multiplexed fashion. The transmission of these SSBs is confined to a half frame time interval (5 ms). It is also possible to configure a cell to transmit multiple SSBs in a single wide-beam with multiple repetitions. The design of beamforming parameters for each of the SSBs within a half frame is up to network implementation. The SSBs within a half frame are broadcasted periodically from each cell. The periodicity of the half frames with SS / PBCH blocks is referred to as SSB periodicity, which is indicated by SIB1.

[0031] The maximum number of SSBs within a half frame, denoted by L, depends on the frequency band, and the time locations for these L candidate SSBs within a half frame depends on the SCS of the SSBs. The L candidate SSBs within a half frame are indexed in an ascending order in time from 0 to L-1. By successfully detecting PBCH and its associated DMRS, a UE knows the SSB index. A cell does not necessarily transmit SS / PBCH blocks in all L candidate locations in a half frame, and the resource of the unused candidate positions may be used for the transmission of data or control signaling instead. It is up to network implementation to decide which candidate time locations to select for SSB transmission within a half frame, and which beam to use for each SSB transmission.

[0032] AI / ML based spatial beam prediction in NR

[0033] During the 3GPP meeting RAN1#109-e it was agreed to study AI / ML-based spatial beam prediction (BM Case 1) for a set A of beams based on measurement results of Set B of beams. The Set B of beams could either be a subset of the Set A of beams, or the set A of beams could consist of different beams compared to the Set B of beams (for example Set A may consist of narrow beams and Set B may consist of wide beams). The spatial beam prediction could either be made at the gNB side or at the UE side.

[0034] During the 3GPP meeting RAN1#109-e it was also agreed to study AI / ML-based temporal (BM case 2) beam prediction for a Set A of beams based on measurement results of Set B of beams, where the Set A of beams and Set B of beams can be the same set of beams or different set of beams. For AI / ML-based temporal beam prediction, it was also agreed that the measurement results of K (K>=1) latest measurement instances during a time window T1 of the Set B beams are used for AI / ML model input. Furthermore, it was agreed that one or more beams from the Set A beams will be used as AI / ML model output, where the AI / ML model output should be F predictions for F future time instances, where all F future time instances are located within a time window T2.

[0035] During the 3GPP meeting RAN1#115, it was also agreed to capture the following description of the two sub-use cases for providing a description of the Beam Management (BM) use case as part of the 3GPP technical report 38.843:

[0036] Figure 3 provides an example for the inference procedure for beam management for BM-Case1 (spatial prediction) and BM-Case2 (temporal prediction). Measurements based on Set B of beams are used as model input. In addition, beam ID information may also be provided as input to the AI / ML model. Based on model output (e.g., probability of each beam in Set A to be the Top-1 beam, predicted L1-RSRPs), Top-1 / N beam(s) among Set A of beams may be predicted and / or potentially with predicted L1-RSRPs (depending on the labeling). In the evaluation, for BM-Case 1, the measurements of Set B (otherwise stated) are used as model input to predict Top-1 / N beams from Set A, and for BM-Case2, the measurements from historic time instance(s) are used as model input for temporal DL beam prediction of beams from Set A (that is, predictions of beams from Set A with respect to time). In the evaluation, the cases that Set A and Set B are different (Set B is NOT a subset of Set A), and Set B is a subset of Set A for both BM-Case1 and BM-Case2, and the case that Set A and Set B are the same for BM-Case2 are considered. The performance of DL Tx beam prediction and DL Tx-Rx beam pair prediction is evaluated.

[0037] For both BM-Case1 and BM-Case2, the UE may report the prediction result to the gNB based on the output of a UE-side model, or the gNB may predict the Top-1 / N beam(s) based on the reported measurements of Set B for a NW-side model.

[0038] It is noted that since a beam is something that is formed on the NW side the UE may only measure the result of this. In an example scenario the narrow beams are measured by CSI-RS resources and the wide beams are measured by SSBs at the UE side. This is how the UE could see the beams from the NW. Set B is different from Set A

[0039] Figure 4a illustrates a schematic example of the Set A of beams and the Set B of beams. The top illustration shows all the narrow gNB beams, which constitutes the Set A of beams, and the lower illustrations shows all the wide gNB beams, which constitutes the Set B of beams.

[0040] Set B is a subset of Set A

[0041] Figure 4b illustrates another example of the Set A of beams and the Set B of beams, wherein Set A comprises narrow gNB beams and set B is subset of Set A comprising some of the narrow beams from the gNB.

[0042] Simplified procedure of BM-Case2

[0043] The BM-Case2, i.e. , temporal domain DL Tx Beam Prediction, is the temporal DL Tx beam prediction for Set A of beams based on the historic measurement results of Set B of beams.

[0044] It was agreed in 3GPP RAN1#117 meeting to support the UE to provide predictions of N future time instances:

[0045] Agreement

[0046] For UE-sided model for BM-Case 2, for inference results report, support to configure UE with N future time instance(s) for inference by NW when applicable

[0047] • FFS: how to determinate reference time for the time instance(s) • FFS: duration values of the N time instance(s) that can be predicted.

[0048] It is to be noted that the temporal predictions made by an AI / ML model become less reliable, the farther in time they are especially in a rapidly changing environment.

[0049] Similarly, the predictions closer to the time of obtaining AI / ML inputs (i.e., Set B beam measurements) are expected to be more accurate.

[0050] For example, for a static or slow speed moving UE, the report containing information (e.g., beam ID and predicted RSRP) of a larger number of future time instances is beneficial as the temporal predictions are likely to be valid for a longer duration.

[0051] Conversely, when a UE is moving at high speed, it is more beneficial to report for a smaller number of future time instances since predictions farther in time are less likely to be valid. Therefore, it is beneficial to have a control on the number of future time instances and the number of reported beams for each future time instance. According to the RAN1 agreement, the NW will have control in how to configure the future time instances for UE to predict. Note that regarding the for further study (FFS) in the above agreement regarding reference time, the following were agreed during 3GPPRAN1#118bis meeting:

[0052] Agreement

[0053] For BM-Case 2 of UE-side model, for the reference time of the earliest time instance for the predicted results, consider at least the following alternatives for potential downselection:

[0054] • Option 1 : Based on the uplink slot for the report

[0055] • Option 2: Based on the CSI reference resource corresponding to the report Option 3: Based on the latest transmission occasion of the CSI-RS / SSB resource in Set B for measurement for the report, wherein the transmission occasion is no later than the CSI reference resource.

[0056] The UE predicts a time window during which the NW (e.g. the base station) may use predictions of a first set of beams (BS Tx beams), denoted set A in this disclosure, performed by the UE based on a second set of BS beams, denoted set B in this disclosure.

[0057] A “prediction time window” may be defined as a time window from an earliest of the NW-configured time instances for the UE to predict, to a last NW-configured time instance for the UE to predict.

[0058] One general example of such prediction time window control is presented in Figure 6a and may be represented as follows.

[0059] Taking prediction report time as a reference time which is at time slot n, the prediction time starts from n+b, i.e. , the predicted beam indexl is validly predicted starting from n+b; the predicted beam index 2 is validly predicetd starting from n+b+1*d; the predicted beam index 3 is validly predicted starting from n+b+2*d,... , until t3 the predicted beam index N is validly predicted starting from n+b+N*d.

[0060] Where,

[0061] • b is a delay of applying the prediction, which normally is reserved for a delay required by the UE to receive the beams from the NW which are based on the prediction report by the UE,

[0062] • d is a time granularity of each prediction, it may be one or an integral multiple of the periodicity of a reference signal.

[0063] • N is a number of future time instances. According to the above definition of the prediction time window, the prediction time window is the time window from “n+ 5” to “n+ 5 + (N-1)*d)”.

[0064] The prediction time window is configured by the network (NW) according to the 3GPP RAN1 agreements (the NW configures the N future time instances). In this case, the UE receiving the information reports the predictions that lie within the indicated prediction time window. In an alternative solution, the prediction time window may be informed by the UE, e.g., contained in the prediction report.

[0065]

[0066] SUMMARY

[0067] Given that 3GPP RAN1 has agreed upon the above scenarios, for BM-case 2, it may be possible to transmit set B in periodical, and semi-persistent resource types. The UE may in this case be providing predictions of the future time instances in a periodic or semi-persistent way. Note that once the prediction reporting starts, the NW and the UE may keep the prediction time window constant.

[0068] However, such methods limit the prediction flexibility. In one example, the UE may be able to predict the same, more, or fewer number of beams with enough accuracy even after the prediction time window. In another example, the prediction accuracy in the future may have different requirements depending on the purpose of the beams, e.g., the beam for beam failure detection, the beam for beam recovery or the beam for beam management, which may have different requirements, and hence they may have different prediction time windows.

[0069] Given the drawbacks mentioned above, it would be desirable with a more flexible prediction time window. Embodiments disclosed herein provide a mechanism for the UE operating an AI / ML model to conditionally or adaptively adjust a prediction time window based on criteria and / or rules presented herein. According to a first aspect, the object is achieved by method, performed by a wireless communications device, for reporting beam predictions for a wireless node. The method comprises obtaining a first configuration of a prediction time window within which beam prediction for the wireless node is to be determined, transmitting an indication of a proposed prediction time window to the wireless node, within which beam prediction for the wireless node is to be determined, receiving from the wireless node a second updated configuration of the prediction time window and reporting a beam prediction to the wireless node. The beam prediction comprises an indication of at least one predicted beam to be transmitted from the wireless node within the prediction time window in the second updated configuration

[0070] According to a second aspect, the object is achieved by a wireless communications device, such as a UE, for reporting beam predictions for a wireless node. The wireless communications devicecomprises an input and output interface and a processor. The processor is configured to obtain a first configuration of a prediction time window within which beam prediction for the wireless node is to be determined, transmit an indication of a proposed prediction time window to the wireless node within which beam prediction for the wireless node is to be determined, receive from the wireless node a second updated configuration of the prediction time window and report a beam prediction to the wireless node. The beam prediction comprises an indication of at least one predicted beam to be transmitted from the wireless node within the prediction time window in the second updated configuration.

[0071] According to a third aspect, the object is achieved by method, performed by a wireless node, for handling beam predictions for the wireless node. The method comprises transmitting to a wireless communications device a first configuration of a prediction time window within which beam prediction for the wireless node is to be determined, receiving from the wireless communications device an indication of a proposed prediction time window within which beam prediction for the wireless node is to be determined, transmitting to the wireless communications device a second updated configuration of the prediction time window and receiving from the wireless communications device a beam prediction. The beam prediction comprises an indication of at least one predicted beam to be transmitted from the wireless node within the prediction time window in the second updated configuration. According to a fourth aspect, the object is achieved by a wireless node for reporting beam predictions for the wireless node. The wireless node comprses an input and output interface and a processor configured to transmit to a wireless communications device a first configuration of a prediction time window within which beam prediction for the wireless node is to be determined, receive from the wireless communications device an indication of a proposed prediction time window within which beam prediction for the wireless node is to be determined, transmit to the wireless communications device a second updated configuration of the prediction time window and receive from the wireless communications device a beam prediction. The beam prediction comprises an indication of at least one predicted beam to be transmitted from the wireless node within the prediction time window in the second updated configuration.

[0072] According to a further aspect, the object is achieved by a computer program comprising instructions, which when executed by a processor, causes the processor to perform actions according to any of the aspects above.

[0073] According to a further aspect, the object is achieved by a carrier comprising the computer program of the aspect above, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

[0074] Embodiments herein enable flexible adaptation of the prediction time window for the UE performing beam predictions as a function of time. This reduces the overall signaling overhead since the network configures the UE with a new time window after each time window has ended. Longer time windows reduce the signalling. Embodiments further enable the predictions that are received by the network from the wireless communications device to be accurate. Embodiments enable the UE to adapt the window during inference, leading to NW getting more information of the UE beam information in the future, which may enable energy savings and bitrate improvements. The UE reports the valid time window. By this, the NW may directly know if the configured time window is proper or not. However, the NW may also derive the prediction performance and the UE conditions for prediction based on the valid time widow. Such UE conditions may e.g. be UE mobility (stationary or not), channel quality etc. For example, a long valid time window at the least may be interpreted such that the predicted beam is valid for a long time period. BRIEF DESCRIPTION OF THE FIGURES

[0075] Fig. 1 illustrates a simplified wireless communication according to known technology. Fig. 2 schematically illustrates beam management divided into three procedures according to known technology.

[0076] Fig. 3 provides an example for the inference procedure for beam management for two prediction cases.

[0077] Fig. 4a illustrates a schematic example of two sets of beams, Set A and Set B beams. Fig. 4b illustrates another example of the Set A of beams and the Set B of beams.

[0078] Fig. 5 is a schematic overview depicting a wireless communications network in which embodiments of the present invention may be implemented.

[0079] Fig. 6a illustrates a general example of a prediction time window control according to some embodiments herein.

[0080] Fig. 6b illustrates an example of an association between a valid prediction time window and different AI / ML-related parameters.

[0081] Fig. 6c illustrates a combined signaling diagram and flow chart according to some embodiments herein.

[0082] Fig. 7 illustrates a flow chart with actions performed by a UE according to some embodiments disclosed herein.

[0083] Fig. 8 illustrates a flow chart with actions performed by a

[0084] Fig. 9 and Fig. 10 illustrate further optional details of a UE and a wireless node wireless node according to some embodiments disclosed herein.

[0085] Figure 11 shows an example of a communication system in accordance with some embodiments.

[0086] Figure 12 is another example of a communication system 1200 according to some embodiments.

[0087] Figure 13 shows a wireless device which may be configured to operate in communication system of Figure 11 or in communication system Figure 12.

[0088] Figure 14 shows a network node, in accordance with some embodiments

[0089] Figure 15 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[0090] DETAILED DESCRIPTION

[0091] Embodiments herein relate to wireless communication networks in general. Figure 5 is a schematic overview depicting a wireless communications network 100 wherein embodiments herein may be implemented. The wireless communications network 100 comprises one or more RANs and one or more CNs. The wireless communications network 100 may use a number of different technologies, such as Wi-Fi, Long Term Evolution (LTE), LTE-Advanced, 5G, New Radio (NR), Wideband Code Division Multiple Access (WCDMA), Global System for Mobile communications / enhanced Data rate for GSM Evolution (GSM / EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations.

[0092] Embodiments herein relate to recent technology trends that are of particular interest in a 5G context, however, embodiments are also applicable in further development of the existing wireless communication systems such as e.g. WCDMA and LTE and to future 6G wireless communication systems.

[0093] Network nodes operate in the wireless communications network 100. The network nodes may for example be access nodes such as a first radio access node 111. The first radio access node 111 provides radio coverage over a geographical area, a service area referred to as a cell 115, which may also be referred to as a beam or a beam group of a first radio access technology (RAT), such as 5G, LTE, Wi-Fi or similar. There may also be further cells, such as a second cell 116.

[0094] The first radio access node 111 may be a NR-RAN node, transmission and reception point e.g. a base station, a radio access node such as a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), an access controller, a base station, e.g. a radio base station such as a NodeB, an evolved Node B (eNB, eNode B), a gNB, a base transceiver station, a radio remote unit, an Access Point Base Station, a base station router, a transmission arrangement of a radio base station, a stand-alone access point or any other network unit capable of communicating with a wireless device within the service area depending e.g. on the radio access technology and terminology used. The first radio access node 111 may be referred to as a serving radio access node and communicates with a UE with Downlink (DL) transmissions to the UE and Uplink (UL) transmissions from the UE.

[0095] A number of wireless communications devices operate in the wireless communication network 100, such as a wireless communications device 121 and a second wireless communications device 122. The wireless communications devices 121, 122 may each be a UE. The wireless communications devices 121, 122 may further each be an FWA node, or nodes with similar functionality. The wireless communications devices 121 , 122 may further each be a mobile station, a non-access point (non-AP) STA, a STA, a user equipment and / or a wireless terminals, that communicate via one or more Access Networks (AN), e.g. RAN, e.g. via the first radio access node 111 to one or more core networks (CN) e.g. comprising a CN node 130, for example comprising an Access Management Function (AMF). It should be understood by the skilled in the art that “UE” is a non-limiting term which means any terminal, wireless communication terminal, user equipment, Machine Type Communication (MTC) device, Device to Device (D2D) terminal, or node e.g. smart phone, laptop, mobile phone, sensor, relay, mobile tablets or even a small base station communicating within a cell.

[0096] Methods herein may in a first aspect be performed by the wireless communications device 121 and in in a second aspect by a wireless communications node, such as the first radio access node 111 or the second wireless communications device 122. As an alternative, a Distributed Node (DN) and functionality, e.g. comprised in a cloud 140 as shown in Figure 5, may be used for performing or partly performing the methods.

[0097] Embodiments herein will now be described in more detail. Embodiments herein disclose solutions, such as UE implementations, for providing a mechanism for the UE operating an AI / ML model to conditionally or adaptively adjust a prediction time window based on criteria and / or rules presented herein.

[0098] In some embodiments disclosed herein the UE determines one or more valid prediction time windows which may differ compared to the pre-defined prediction time instants N by determining whether certain criteria have been fulfilled or not.

[0099] In some embodiments disclosed herein the UE reports a new prediction time window to the network node, e.g. by reporting N_valid. N_valid are the time instants derived by the UE, which may be different from the pre-defined time instants N.

[0100] Alternatively, the UE requests the NW to configure the extension of the prediction time window by a simple indication indicating prediction extension (e.g., more predictions are available) or not.

[0101] The NW may reconfigure the UE to update the prediction time window based on the report or request from the UE.

[0102] In embodiments disclosed herein, the “prediction time window” or pre-defined prediction time window is defined as the time window from the earliest of the NW- configured time instances for the UE to predict, to the last configured time instances for the UE to predict. Other terminology for the same definition may comprise prediction horizon, forecast window, beam prediction window, pre-defined time instants, etc. The “valid prediction time window” may be a time window where the UE has determined that the prediction accuracy is above a certain threshold value.

[0103] In embodiments disclosed herein, the term “beam” may correspond to a spatial direction in which a signal is transmitted (e.g. by a network node) or received (e.g. by the UE), or a spatial filter applied to a signal which is transmitted or received. Thus, transmitting signals with different beams may correspond to transmitting signals in different spatial directions. When the text refers to a “beam which is selected” it may refer to a beam index and / or a Reference Signal (RS) index or identifier, such as a Synchronization Signal block (SSB) index, or a CSI-RS resource identifier. Thus, selecting a beam may correspond to selecting an SSB, associated to an SSB index. Or, selecting a beam may correspond to selecting a CSI-RS, associated to a CSI-RS resource identifier, or another identifier such as the “associated ID”, or “consistency ID” defined in 3GPP Rel-19.

[0104] UE-centric embodiments

[0105] In some embodiments, the UE 121 operating an AI / ML model manages (e.g., evaluates, determines, maintains and / or updates) one or more valid prediction time windows which may differ from the pre-defined time instants N of the pre-defined prediction time window. The UE 121 may determine the one or more valid prediction time windows by determining whether predictions within a prediction time window fulfils certain criteria or not.

[0106] In some cases the valid prediction time window may be longer than the pre-defined prediction time window comprising the pre-defined time instants N, but in some other cases the valid prediction time window may be shorter than or equal to the pre-defined prediction time window.

[0107] The UE 121 may manage one or more valid prediction time windows comprising time instances N_valid for the predicted beams or RSs which are used for different purposes or types, e.g. as configured in a NW report configuration (which configures how the UE reports the prediction results including the prediction time window), as one or more of the below examples. The time window duration in the below examples may be same or different. For example, the first valid prediction time window N1 may equal the second valid prediction time window N2 or it may be different.

[0108] • In one example, the UE manages a first valid prediction time window N1 for beams used for beam management, e.g., the RSs for L1-RSRP measurement.

[0109] • In one example, the UE manages a second valid prediction time window N2 for beams used for TCI states, e.g., the RS in target TCI state or QCLed to the target TCI state, or the RSs in the activated TCI state list.

[0110] • In one example, the UE manages a third valid prediction time window N3 for beams used as BFD (beam failure detection) RS.

[0111] • In one example, the UE manages a fourth valid prediction time window N4 for beams used as BFR (beam failure recovery) RS.

[0112] • In one example, the UE manages a fifth valid prediction time window N5 for beams used as RLM (Radio link monitoring) RS.

[0113] • In one example, the UE manages a sixth valid prediction time window N6 for beams used for AI / ML model performance monitoring.

[0114] • In one example, the UE manages a seventh valid prediction time window N7 for beams used as CSI-RS for PDSCH.

[0115] • In one example, the UE manages an eighth valid prediction time window N8 for a specific beam determined by the NW or by the UE.

[0116] • The valid prediction time window in the above examples may be chosen out of a candidate time instant list, in which the candidate time instants are defined along with at least a minimal granularity of the prediction time window and a maximal duration of the prediction time window.

[0117] • The valid prediction time window in the above examples may be the same as or different from the pre-defined prediction window N.

[0118] • To be compatible with the above examples, N_valid may be implented as a list associated with beams or beam purposes, which comprises a valid prediction time window per beam or beam purpose. The valid prediction time may be the same as or different from the pre-defined prediction time window.

[0119] In some embodiments, one or more valid prediction time windows may be associated with different AI / ML model input / output sizes, e.g., sizes of Set A and Set B. The AI / ML model complexity is dependent on the input / output size (more complex model with higher input / output dimension) • In one example, for the one or more beams or RSs in the above embodiment, with the same input Set B, the UE is capable of predicting a Ka1 number of beams in Set A during the valid prediction time window Na1 , and predicting a Kb1 number of beams in Set A during the valid prediction time window Nb1 , etc.

[0120] • In another example, for the one or more beams or RSs in the above embodiment, with same output Set A, the UE uses a Ka2 number of beams in the input Set B to predict the valid prediction time window Na, and requests a Kb2 number of beams in Set B to predict the valid prediction time window Nb2, etc.

[0121] In some embodiments, one or more valid prediction time windows may be associated with the AI / ML model performances, e.g. the predicted RSRP accuracy (i.e. accuracy of the prediction of the RSRP of a beam) or predicted beam accuracy (i.e. accuracy of the beam prediction, e.g. a percentage of predicted best beams (e.g. Top-1) that actually turn out to be best beams (e.g. Top-1)). Thus, the valid prediction time window may be determined based on the AI / ML model performance with respect to time.

[0122] • In one example, for the one or more beams or RSs in the above embodiment, the UE may predict the AI / ML model performance K11 (e.g., K11 fulfills a threshold or a criteria for the predicted RSRP accuracy or predicted beam accuracy) during the valid prediction time window NA1, and may predict the AI / ML model performance K12 (e.g., K12 fulfills another threshold or criteria for the predicted RSRP accuracy or prediction beam accuracy) during the valid prediction time window NB1, etc. • In one example, for the one or more beams or RSs in the above embodiment, the UE may monitor and ensure the AI / ML model performance K21 (e.g., K21 fulfills one threshold or criteria for monitoring metrics) during the valid prediction time window NA2, and monitor and ensure the AI / ML model performance K22 (e.g., K22 fulfills one threshold or criteria for monitoring metrics) during the valid prediction time window NB2, etc. In this way the UE is able to guarantee the Al model performance by monitoring within a valid prediction window.

[0123] Examples of predicted RSRP accuracy

[0124] • Option 1: Difference between the predicted RSRP (of the predicted beam) and the measured RSRP of the same Tx beam

[0125] • Option 2: Difference between the predicted RSRP (of the predicted beam) and the ideal RSRP of the same Tx beam Example of predicted beam accuracy

[0126] • Top-1 (%): the percentage of "the Top-1 strongest beam is Top-1 predicted beam" • Top-K / 1 (%): the percentage of "the Top-1 strongest beam is one of the Top-K predicted beams"

[0127] • Top-1 / K (%): the percentage of "the Top-1 predicted beam is one of the Top-K strongest beams"

[0128] In some embodiments, taking the above embodiments into account, the valid prediction time window is associated with at least one of the below AI / ML related aspects.

[0129] • The NW CSI report configuration (inference related parameters)

[0130] o The purposes or types of beams / RSs by prediction as described in the text above. For example, if the beams are predicted to mitigate RLF; or SSBs, or CSI-RS for PDSCH

[0131] o Beam information comprising Set A-related and Set B-related information.

[0132] ■ Beam index order: the order of resources (e.g., resource index consistency) for Set B beams and Set A beams, across training and inference.

[0133] • In one example, a resource index (Rl) associated with SetA / Set B, e.g., SetA-RI / SetB-RI =m to determine input value with respect to the m:th input feature across training & inference. The index order on spatial domination (the order may be 1-dimension or 2-dimension or 3-dimension) shall be kept across training & inference.

[0134] ■ Beam shape: relative pointing direction (e.g., to the antenna boresight direction) and beamwidth difference between physical beams with respect to Set A and Set B resources across training and inference should be under predefined tolerances.

[0135] ■ QCL relationship of beams in Set B with beams in Set A, during both training and inference.

[0136] ■ QCL relationship of beams in Set A during inference to beams in Set A during training.

[0137] ■ QCL relationship of beams in Set B during inference to beams in Set B during training.

[0138] ■ TX beam number / panel / chain for AI / ML model training / inference.

[0139] ■ TX beam width, TX beam gain for AI / ML model training / inference. Output power for AI / ML model training / inference.

[0140] o content-related information of the report of the valid prediction time window o Information related to time instances for measurements

[0141] o Information related to time instances for prediction, within the prediction window

[0142] o The associated ID(s), where the UE assumption regarding the associated ID is that the UE may assume similar properties of a DL Tx beam or beam set / list associated with the same associated ID. If 2 NWs provide same ID to the UE, then the UE may assume that the 2 NWs have the same beam information (listed in the above content).

[0143] • The AI / ML model performance-related information, e.g. different criteria or thresholds based on the predicted RSRP accuracy or the predicted beam accuracy.

[0144] • The AI / ML model computational-related information, e.g. the number of CSI processing units required for a certain prediction time window.

[0145] o One or more valid prediction time window may be associated with the AI / ML model input / output sizes, e.g., sizes of Set A and Set B. As mentioned above the AI / ML model complexity is dependent on the input / output size (more complex model with higher input / output dimension)

[0146] Figure 6b demonstrates an example of an association between the valid prediction time window and different parameters such as AI / ML-related parameters. It is possible that the association only comprises a subset of all AI / ML-related parameters in Figure 6b. For example, a first valid prediction window 1 may be based on a report configuration (which may include purpose / types of the beams) for the reporting of the predicted beams from the UE 121. The first valid prediction window 1 may further be based on AI / ML model computational complexity or AI / ML model prediction performance or both. A second valid prediction window 2 may also be based on one or more of the three parameter categories mentioned above. A third valid prediction window 3 may also be based on one or more of the three parameter categories mentioned above.

[0147] Embodiments related to the UE signaling a request for flexible prediction time window Some embodiments disclosed herein enable the UE to request an extended or reduced prediction time window as part of the inference report.

[0148] In some embodiments disclosed herein the NW includes a field in the UE inference report of the N prediction time instances comprising an indication on whether the UE would like to extend or reduce the prediction time window. This may be introduced via a new reporting quantity in the CSI reporting framework. The UE may as part of the inference report (when reporting the N time instances) on the UCI report for example include a number of bits, such as 2 bits, with the following meaning:

[0149] - 00 => keep the same prediction window

[0150] - 01 => extend the prediction window

[0151] 10 => reduce the prediction window

[0152] Other examples may include more bits where the UE may also include by how many slots the UE may reduce or extend the prediction time window. For example, in case a 3-bit information element (IE) is introduced, the UE may indicate whether to reduce or extend the prediction window by 8 slots (or any time unit in the NR framework). In another example the IE may indicate that the NW should remove the last K time instances in the prediction window or add K time instances (assuming the same periodicity as the other ones). In another example, the UE also reports the AI / ML-related aspects. This may be used by the NW to for example also adjust the beams to predict within the window.

[0153] Some embodiments disclosed herein enable the UE to request extended or reduced prediction time window as part of a dedicated report.

[0154] In some embodiments, the UE may determine the valid prediction time window and the associated AI / ML-related aspects before or upon expiring of the pre-defined time instants N, and the UE may then report to the NW the valid prediction time window and / or request an extended or reduced prediction time window with respect to the pre-defined prediction time window before or upon expiring of the pre-defined time instants N. If the UE is able to report before the end of the valid prediction time window then the NW has enough time to acknowledge the new time valid prediction time window and the UE will get a new configuration before a next prediction.

[0155] In one option, the UE reports the valid prediction time window and / or requests extended or reduced prediction time window and the associated AI / ML-related aspects during the inference phase, e.g., during the pre-defined time instants N, which may at least be earlier than K slots before end of the inference phase, e.g., before end of the pre- defined time instants N. The purpose reporting the valid prediction time window with a margin of K time slots is to ensure that the NW is able to reconfigure the prediction window before expiry of the current predefined window, with enough margin.

[0156] The NW may for example configure certain uplink resources, which the UE may use to indicate an extension or reduction of the prediction window reusing the signaling example above. In other embodiments, the valid prediction time window reported by the UE may have other representation formats.

[0157] • According to one option, the UE reports the valid prediction time window N_valid and / or requests extended or reduced prediction time window and the AI / ML- related aspects.

[0158] • According to one other option, the UE reports / requests an offset to the pre-defined time instants N, e.g., M, where M= N_valid -N, and the associated factors.

[0159] • According to another option, the U Erequests to extend the prediction window. • According to yet another option, the UE requests to reduce the prediction window.

[0160] The UE may provide a report of the valid prediction time window and / or request to update the prediction time window, for example in UCI or RRC. Another method may comprise the UE to indicate in the UE Assistance Information element (UAI). The UE may also include a request to update the A I / ML- related aspects.

[0161] Embodiments related to the wireless node for updating the prediction time window based on the UE report of a valid prediction time window

[0162] In some embodiments, the NW (re)configures the UE with the valid prediction time window (e.g. an extension of the pre-defined prediction time window) and the associated AI / ML-related aspects based on the report by the UE. The UE is able to apply the prediction based on the determined valid prediction time window and the associated AI / ML-related aspects provided the UE receives the NW indication of applying it.

[0163] Otherwise, the UE won’t apply the prediction based on the determined valid prediction time window. Instead, the UE may use the old pre-configured prediction time. The (re)configuration may include one or more of the below:

[0164] • In one option, the NW indication is requested per report of the valid prediction time window. In other words, the NW indication is valid for one prediction. In other words, the NW indication is valid for one prediction. • In one option, the NW indication is requested per request (i.e. , the request requesting extended or reduced prediction time window) by the UE.

[0165] • In one option, the NW indication is requested for one or more life cycle of AI / ML model, e.g., by life cycle management. In other words, the NW indication is valid for all predictions in the life cycle(s).

[0166] The format of the NW indication may be one of the below:

[0167] • In one option, the NW indication is a simple indication, e.g., ‘T or ‘O’, acknowledging or not acknowledging of the valid prediction time window or reduced / extended predict time window requested / recommended by the UE. • In one option, the NW indication is a signaling comprising the update to the predefined prediction time window and the associated AI / ML related aspects which may be same or different from the ones reported by the UE.

[0168] • In one option, the NW activates a new CSI report configuration based on the new prediction time window

[0169] o For example, the NW configures a new report quantity based on the new prediction time window

[0170] • In one option, the NW activates another reporting quantity based on the new prediction time window

[0171] o For example, the MAC CE further supports that the NW may activate certain reporting quantities, where each reporting quantity is a different number, or configuration of N future time instances

[0172] The NW provides one or more configurations to the UE via one of the signaling alternatives:

[0173] • Dedicated RRC signaling

[0174] • MAC CE

[0175] o in this option the UE may be (pre)configured with a list of configurations, the gNB then further uses a MAC CE to indicate to the UE which configurations shall be applied by the UE.

[0176] • L1 signaling (e.g., PDCCH)

[0177] o in this option the UE may be (pre)configured with a list of configurations, the gNB then further uses a DCI to indicate to the UE which configurations shall be applied by the UE. Figure 6c illustrates a combined signaling diagram and flow chart according to some embodiments herein.

[0178] Action 600: The UE trains with an AI / ML model to predict beams from the wireless node during a prediction time window. The UE reports the results of the training to the wireless node, such as the first radio access node 111. The report comprises a valid prediction time window. This enables AI / ML model inference with a pre-defined prediction time window after training and prediction reporting.

[0179] Action 601: The UE evaluates the prediction time window and determines a valid prediction time window.

[0180] Action 602: The UE reports the valid prediction time window and / or requests to update the prediction time window.

[0181] Action 603: The NW reconfigures the UE with the valid prediction time window. Action 604: The UE continues inference until the end of the reconfigured prediction time window.

[0182] Flowcharts

[0183] Figure 7 illustrates a flow chart with the actions performed by the UE 121 according to some embodiments disclosed herein.

[0184] Referring to Figure 7, the actions performed by the UE 121 in this example are as follows. The actions may be performed in any suitable order.

[0185] Action 701. The UE 121 obtains a first configuration of a prediction time window within which beam prediction for the wireless node 111, 122 is to be determined. The first configuration of the prediction time window configure the UE 121 with a first prediction time window, such as the pre-defined prediction time window mentioned above.

[0186] Action 702. The UE 121 determines a proposed prediction time window. The proposed prediction time window may be the valid prediction time window above.

[0187] Determining the proposed prediction time window may be based on AI / ML applied to one or more measured beams (which may be regarded as input beams, i.e. input data to the AI / ML model). The AI / ML model may be used for the beam prediction within the prediction time window.

[0188] Determining the proposed prediction time window may be based on one or more of: AI / ML model computational complexity and AI / ML model prediction performance within the prediction time window. AI / ML model computational complexity may e.g. be input / output sizes, i.e. sizes of the input or output data or both. AI / ML model prediction performance may be based on an accuracy of the predicted RSRP or an accuracy of the beam prediction.

[0189] Action 703: The UE 121 transmits an indication of the proposed prediction time window to the wireless node, within which beam prediction for the wireless node 111, 122 is to be determined.

[0190] Action 704: The UE 121 receives, from the wireless node 111, 122, a second updated configuration of the prediction time window. The second updated configuration of the prediction time window may comprise an updated configured value of the prediction time window which equals the proposed prediction time window.

[0191] Figure 8 illustrates a flow chart with the actions performed by the wireless node according to some embodiments disclosed herein. The wireless node may be one of a generic NW node, gNB, base station, unit within the base station to handle at least some ML operation, relay node, core network node, a core network node that handle at least some ML operations, a device supporting D2D communication.

[0192] Referring to Figure 8, the actions performed by the wireless node in this example are as follows. The actions may be performed in any suitable order.

[0193] Action 801. The wireless node transmits, to the wireless communications device 121 , the first configuration of the prediction time window within which beam prediction for the wireless node is to be determined.

[0194] Action 802. The wireless node receives, from the wireless communications device 121, an indication of the proposed prediction time window within which beam prediction for the wireless node is to be determined.

[0195] Action 803. The wireless node transmits, to the wireless communications device 121, the second updated configuration of the prediction time window.

[0196] Action 804. The wireless node receives from the wireless communications device 121 , beam predictions comprising the indication of the at least one predicted beam to be transmitted from the wireless node within the updated prediction time window.

[0197] Action 804. The wireless node may transmit information to the wireless communications device 121 on the at least one predicted beam.

[0198] Figure 9 and Figure 10 illustrate further optional details of the UE 121 and the wireless node 111, 122 respectively. The UE 121 is configured to perform the method actions of Figure 7 as well as some of the actions of Figure 6c above. The wireless node 111 , 122 is configured to perform actions described above, for example in relation to Figure 8 and Figure 6c.

[0199] The embodiments herein may be implemented through a processor or one or more processors, such as the processor 904, 1004, of a processing circuitry in the UE 121 and the wireless node 111, 122, and depicted in Figures 9 and 10 together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the UE 121 and the wireless node 111, 122. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the UE 121 and the wireless node 111, 122.

[0200] The UE 121 and the wireless node 111, 122 may further comprise a memory 902, 1002 comprising one or more memory units. The memory comprises instructions executable by the processor in the UE 121 and the wireless node 111, 122.

[0201] The respective memory 902, 1002 is arranged to be used to store e.g. information, data, configurations, and applications to perform the methods herein when being executed in the UE 121 and the wireless node 111, 122.

[0202] In some embodiments, a computer program 903, 1003 comprises instructions, which when executed by the at least one processor, cause the at least one processor of the UE 121 and each of the wireless node 111, 122 to perform the actions above.

[0203] In some embodiments, a carrier 905, 1005 comprises the computer program, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

[0204] The UE 121 and the wireless node 111, 122 may further comprise an input and output interface, I / O, 906, 1006 configured to communicate with other devices. The input and output interface 906, 1006 may comprise a wireless transceiver. Those skilled in the art will also appreciate that the units described above may refer to a combination of analog and digital circuits, and / or one or more processors configured with software and / or firmware, e.g. stored in the UE 121 and the wireless node 111, 122, that when executed by the respective one or more processors such as the processors described above causes the UE 121 and the wireless node 111, 122 to perform the method actions above.

[0205] One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuitry (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a system-on-a-chip (SoC).

[0206] When using the word "comprise" or “comprising” it shall be interpreted as nonlimiting, i.e. meaning "consist at least of".

[0207] The embodiments herein are not limited to the above-described preferred embodiments. Various alternatives, modifications and equivalents may be used. Figure 11 shows an example of a communication system 1100 in accordance with some embodiments.

[0208] In the example, the communication system 1100 includes a telecommunications network 1102 that includes an access network 1104, such as a radio access network (RAN), and a core network 1106, which includes one or more core network nodes 1108. The access network 1104 includes one or more access network nodes or base stations of various types, access network nodes 1110A and 1110B are depicted (which may be collectively referred to as network nodes 1110), or any other similar 3rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 1104 may include more than one access network technology. The network nodes 1110 of access network 1104 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 1112A, 1112B, 1112C, and 1112D (one or more of which may be generally referred to as UEs 1112) to the core network 1106 over one or more wireless connections.

[0209] Moreover, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunications network 1102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 1102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in the telecommunications network 1102, including one or more access network nodes 1110 and / or core network nodes 1108.

[0210] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1, F1, W1, E1, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies.

[0211] The network nodes 1110 facilitate direct or indirect connection of one or more UEs 1112 to the core network 1106 over one or more wireless connections. Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 1100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 1100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0212] The UEs 1112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 1110 and other communication devices. Similarly, the network nodes 1108, 1110 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 1102) with the UEs 1112 and / or with other network nodes or equipment in the telecommunications network 1102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunications network 11102. More specifically, UEs 1112 may send messages, data, and / or other signals to network nodes 1108, 1110 or other elements of the telecommunications network 1102 by transmitting such signals to the relevant device directly without the signals passing through any intervening devices or by transmitting such signals to the relevant device indirectly through an intervening device (or multiple intervening devices) that then transmit the signal to the relevant device. Similarly, network nodes 1108, 1110 may send messages, data, and other signals to UEs 1122, other network nodes 1108, 1110, and other devices in telecommunications network 1102 directly or indirectly. As one specific example, a core network node 1108 may transmit a particular message to a UE 1112 by transmitting the message to an access network node 1110 that will then transmit the message to the intended UE 1112. Similarly, a core network node 1108 may receive a particular message from a UE 1112 by receiving the message from an access network node 1110 that itself received the message from the UE 1112.

[0213] In the depicted example, the core network 1106 connects elements of the access network 1104 (e.g., one or more of the network nodes 1110) to one or more host computing systems, such as host 1116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 1106 includes one or more core network nodes (e.g., core network node 1108) of various types, one or more of which may be generally referred to as network nodes 1108. Network nodes 1108 are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 11108. Example core network nodes provide functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (ALISF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0214] The host 1116 may be under the ownership or control of a service provider other than an operator or provider of the access network 1104 and / or the telecommunications network 1102. The host 1116 may be operated by the service provider or on behalf of the service provider. The host 1116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0215] As a whole, the communication system 1100 of Figure 11 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1100 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (Wi-Max), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, Li-Fi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. Moreover, the communication system 1100 may be configured to support multiple different standards, protocols, or other rule sets, with individual components supporting all of the relevant rule sets or with different components or sub-systems within the communication system 1100 supporting different standards, protocols, or rule sets.

[0216] As one example, in certain embodiments, access network 1104 may contain some access network nodes 1110 that support 3GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 1110 support (or the same access network nodes 1110 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 1102 may support multiple generations of related communication standards (e.g., 4G and 5G 3GPP communication standards) and, as a result, may include an access network 104 and / or a core network 106 that supports multiple different standard generations or may include multiple access networks 104 and / or multiple core networks 106 with individual networks 1104, 1106 supporting different standard generations.

[0217] Telecommunications network 1102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 1102. For example, the telecommunications network 1102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0218] In some examples, one or more of the UEs 1112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC). In the example, the hub 1114 communicates with the access network 1104 to facilitate indirect communication between one or more UEs (e.g., UE 1112C and / or 1112D) and network nodes (e.g., network node 1110B). In some examples, the hub 1114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1114 may be a broadband router enabling access to the core network 1106 for the UEs. As another example, the hub 1114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 1110, or by executable code, script, process, or other instructions in the hub 1114.

[0219] As another example, the hub 1114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 1114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0220] The hub 1114 may have a constant / persistent or intermittent connection to the network node 1110B. The hub 1114 may also allow for a different communication scheme and / or schedule between the hub 1114 and UEs (e.g., UE 1112C and / or 1112D), and between the hub 1114 and the core network 1106. In other examples, the hub 1114 is connected to the core network 1106 and / or one or more UEs via a wired connection. Moreover, the hub 1114 may be configured to connect to an M2M service provider over the access network 1104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1110 while still connected via the hub 1114 via a wired or wireless connection. In some embodiments, the hub 1114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 1110B. In other embodiments, the hub 1114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1110B, but which is additionally capable of operating as a communication start and / or end point for certain data channels. Figure 12 is another example of a communication system 1200 according to some embodiments. As used herein, the communication system 1200 includes multiple access points (APs) 1210 (with four exemplary APs 1210A, 1210B, 1210C, and 1210D being depicted) and multiple wireless devices, referred to in the context of communication system 1200 as stations (STAs) 1212 (referred to individually as STA 1212A, STA 1212B, STA 1212C, STA 1212D, and STA 1212E). STA 1212A is served by AP 1210A in a first basic service set (BSS) 1220A. STA 1210B and STA 1210C are served by AP 1210B in a second BSS, BSS 1220B. STA 1212D is served by AP 1210C in a third BSS, BSS 1220C. STA 1212E is served by AP 1210D in a fourth BSS, BSS 1220D. Stations 1212 may be non-AP STAs and correspond to various kinds of wireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, head-mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, stations 1212 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0221] Each of STAs 1212 may connect through a radio link to one of APs 1210. For example, depending on location or channel conditions experienced by a given STA 1212, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from a frequency spectrum that is shared on the basis of a contention-based mechanism, e.g., an unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0222] Each AP 1210 may provide data connectivity to STAs 1212 connected to a particular AP 1210. As illustrated, APs 1210 may be connected to a data network 1230. In this way, APs 1210 may also provide data connectivity between STAs 1212 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like. Accordingly, the radio link established between a given STA 1212 and its serving AP 1210 may be used for providing various kinds of services to STA 1212, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 1212 and / or on a device linked to STA 1212. Byway of example, Figure 12 illustrates an application service platform 1232 provided in data network 1230. The application(s) executed on STA 1212 and / or on one or more other devices linked to STA 1212 may use the radio link for data communication with one or more other STA 1212 and / or the application service platform 1232, thereby enabling utilization of the corresponding service(s) at STA 1212. Figure 13 shows a wireless device 1300, which may be configured to operate in communication system 1100 of Figure 11 or in communication system 1200 of Figure 12. The wireless device 1300 may be alternatively referred to as a UE 1300, like a UE 1112 within the context of communication system 1100, or as a station (STA) 1300 or as a non-access-point station (non-AP STA) 1300, like a STA 1212 within the context of the communication system 1200, in accordance with respective embodiments. As used herein, a wireless device refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other wireless devices. Examples of a wireless device include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, and wireless terminal. Other examples include any type of UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0223] A wireless device 1300 may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, wireless device 1300 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 1300 may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, wireless device 1300 may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0224] In particular embodiments, wireless device 1300 includes processing circuitry 1302 that is operatively coupled via a bus 1304 to an input / output interface 1306, a power source 1308, a memory 1310, a communication interface 1312, and / or any other component, or any combination thereof. Certain embodiments of wireless device 1300 may include all or a subset of the components shown in Figure 13. The level of integration between the components may vary from one embodiment of wireless device 1300 to another. In general, in a particular embodiment of wireless device 1300, processing circuitry 1302, input / output interface 1306, power source 1308, memory 1310, and communication interface 1312 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of wireless device 1300. Further, certain embodiments of wireless devices 1300 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0225] The processing circuitry 1302 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 1310. The processing circuitry 1302 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1302 may include multiple central processing units (CPUs).

[0226] In the example, the input / output interface 1306 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into wireless device 1300. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0227] In some embodiments, the power source 1308 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used to supply power to circuitry or to charge an associated battery. The power source 1308 may further include power circuitry for delivering power from the power source 1308 itself, and / or an external power source, to the various parts of wireless device 1300 via input circuitry or an interface such as an electrical power cable. Power source 1308 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 1300 to which power is supplied.

[0228] The memory 1310 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1310 includes one or more programs 1314, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1316. The memory 1310 may store, for use by wireless device 1300, any of a variety of various operating systems or combinations of operating systems.

[0229] The memory 1310 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1310 may allow wireless device 1300 to access instructions, programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1310, which may be or comprise a device-readable storage medium.

[0230] The processing circuitry 1302 may be configured to communicate with an access network or other network via or using the communication interface 1312. The communication interface 1312 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1322. The communication interface 1312 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another wireless device or a network node in an access network). Each transceiver may include a transmitter 1318 and / or a receiver 1320 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1318 and receiver 1320 may be coupled to one or more antennas (e.g., antenna 1322) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0231] In the illustrated embodiment, communication functions of the communication interface 1312 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0232] In particular embodiments, wireless device 1300 may provide an output of data captured via a sensor, through its communication interface 1312, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 1300 can be communicated through a wireless connection to a network node via another wireless device 1300. In particular embodiments, such output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0233] As another example, wireless device 1300 comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, wireless device 1300 may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0234] Wireless device 1300, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, wearable technology, extended industrial application and healthcare. Nonlimiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smartwatch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. In particular embodiments, wireless device 1300 represents an loT device that comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the example embodiment of wireless device 1300 shown in Figure 13.

[0235] As yet another specific example, in an loT scenario, wireless device 1300 may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another wireless device and / or a network node. Wireless device 1300 may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, wireless device 1300 may implement the 3GPP NB-loT standard. In other scenarios, wireless device 1300 may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0236] In practice, any number of wireless devices 1300 may be used together with respect to a single use case. For example, a first wireless device 1300 might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second wireless device 1300 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 1300 may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second wireless device 1300 can also include more than one of the functionalities described above. For example, wireless device 1300 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0237] Figure 14 shows a network node 1400 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunications network. In accordance with respective embodiments, network node 1400 may be configured to operate in communication system 1100 of Figure 11, like network nodes 1108 or 1110, or in communication system 1200 of Figure 12, like an AP 1210 or a station 1212. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., 0-Rll, 0-Dll, O-CU).

[0238] Network nodes 1400 may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. Network node 1400 may be a relay node or a relay donor node controlling a relay. Network nodes 1400 may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0239] Other examples of network nodes 1400 include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cel l / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0240] In particular embodiments, network node 1400 includes a processing circuitry 1402, a memory 1404, a communication interface 1406, and a power source 1408. In general, in a particular embodiment of network node 1400, processing circuitry 1402, memory 1404, communication interface 1406, and power source 1408 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of network node 1400.

[0241] The network node 1400 may be composed of multiple distinct network entities (e.g., a NodeB entity and a RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 1400 comprises multiple such entities (e.g., BTS and BSC), one or more of the separate entities may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1400 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 1404 or portions of memory 1404 for different RATs) and some components may be reused (e.g., a same antenna 1410 may be shared by different RATs). The network node 1400 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1400, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1400.

[0242] The processing circuitry 1402 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other components, such as the memory 1404, to provide network node 1400 functionality.

[0243] In some embodiments, the processing circuitry 1402 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1402 includes one or more of radio frequency (RF) transceiver circuitry 1412 and baseband processing circuitry 1414. In some embodiments, the RF transceiver circuitry 1412 and the baseband processing circuitry 1414 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1412 and baseband processing circuitry 1414 may be on the same chip or set of chips, boards, or units. The memory 1404 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1402. The memory 1404 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 1402 and utilized by the network node 1400. The memory 1404 may be used to store any calculations made by the processing circuitry 1402 and / or any data received via the communication interface 1406. In some embodiments, the processing circuitry 1402 and memory 1404 is integrated.

[0244] The communication interface 1406 is used in wired or wireless communication of signaling and / or data with UEs, other network nodes, and / or any other network equipment. In the illustrated embodiment, communication interface 1406 comprises port(s) / terminal(s) 1416 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 1300 may be capable of wireless communication and communication interface 1406 may also include radio frontend circuitry 1418 that may be coupled to, or in certain embodiments a part of, an antenna 1410. Particular embodiments of radio front-end circuitry 1418 include filter(s) 1420 and amplifier(s) 1422. The radio front-end circuitry 1418 may be connected to an antenna 1410 and processing circuitry 1402. The radio front-end circuitry may be configured to condition signals communicated between antenna 1410 and processing circuitry 1402. The radio front-end circuitry 1418 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 1418 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 1420 and / or amplifiers 1422. The radio signal(s) may then be transmitted via the antenna 1410. Similarly, when receiving data, the antenna 1410 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1418. The digital data may be passed to the processing circuitry 1402. In other embodiments, the communication interface may comprise different components and / or different combinations of components. In certain alternative embodiments, network node 1400 may be capable of wireless communication but does not include separate radio front-end circuitry 1418, instead, the processing circuitry 1402 includes radio front-end circuitry and is connected to the antenna 1410. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1412 is part of the communication interface 1406. In still other embodiments, the communication interface 1406 includes one or more ports or terminals 1416, the radio front-end circuitry 1418, and the RF transceiver circuitry 1412, as part of a radio unit (not shown), and the communication interface 1406 communicates with the baseband processing circuitry 1414, which is part of a digital unit (not shown).

[0245] The antenna 1410 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1410 may be coupled to the radio front-end circuitry 1418 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1410 is separate from the network node 1400 and connectable to the network node 1400 through one or more interfaces or ports.

[0246] The antenna 1410, communication interface 1406, and / or the processing circuitry 1402 may be configured to perform some or all of the receiving operations and / or obtaining operations described herein as being performed by the network node 1400. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 1410, the communication interface 1406, and / or the processing circuitry 1402 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 1400. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0247] The power source 1408 provides power to the various components of network node 1400 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1408 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1400 with power for performing the functionality described herein. For example, the network node 1400 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1408. As a further example, the power source 1408 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail. Embodiments of the network node 1400 may include additional components beyond those shown in Figure 14 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 1400 may include user interface equipment to allow input of information into the network node 1400 and to allow output of information from the network node 1400. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1400.

[0248] Figure 15 is a block diagram illustrating a virtualization environment 1500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, UE, core network node, or host. Further, in embodiments in which a virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1500 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0249] Applications 1502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0250] Hardware 1504 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 1508A and VM 1508B (which may be collectively referred to as VMs 1508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1506 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 1508.

[0251] The VMs 1508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 1506. Different embodiments of the instance of a virtual appliance 1502 may be implemented on one or more of VMs 1508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0252] In the context of NFV, each of the VMs 1508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, nonvirtualized machine. Each of the VMs 1508, and that part of hardware 1504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more of the VMs 1508 on top of the hardware 1504 and corresponds to an application 1502.

[0253] Hardware 1504 may be implemented in a standalone network node with generic or specific components. Hardware 1504 may implement some functions via virtualization. Alternatively, hardware 1504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1510, which, among others, oversees lifecycle management of applications 1502. In some embodiments, hardware 1504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1512 which may alternatively be used for communication between hardware nodes and radio units.

[0254] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0255] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0256]

[0257]

Claims

1. CLAIMS1. A method, performed by a wireless communications device (121), for reporting beam predictions to a wireless node (111, 122), the method comprising:3.obtaining (701), a first configuration of a prediction time window within which beam prediction for the wireless node (111, 122) is to be determined;4.transmitting (703) an indication of a proposed prediction time window to the wireless node, within which beam prediction for the wireless node (111, 122) is to be determined;5.receiving (704), from the wireless node (111, 122), a second updated configuration of the prediction time window; and6.reporting (705) a beam prediction to the wireless node, wherein the beam prediction comprises an indication of at least one predicted beam to be transmitted from the wireless node (111, 122) within the prediction time window in the second updated configuration.

2. The method according to claim 1, further comprising:8.determining (702) the proposed prediction time window, such that it is determined based on AI / ML applied to one or more measured beams, the one or more measure beams being input data to the AI / ML model.

3. The method according to claim 2 , wherein determining (702) the proposed prediction time window is further based on a type of the at least one predicted beam.

4. The method according to claim 2 or 3, wherein determining (702) the proposed prediction time window is based on one or more of: AI / ML model computational complexity; AI / ML model prediction performance within the prediction time window.

5. The method according to claim 4, wherein the AI / ML model computational complexity comprises sizes of input or output data or both.

6. The method according to claim 4 or 5, wherein the AI / ML model prediction performance is based on an accuracy of a predicted Reference Signal Received Power (RSRP) or an accuracy of the beam prediction.

7. The method according to any of the claims 1-6, wherein the second updated configuration of the prediction time window comprises an updated configured value of the prediction time window which equals the proposed prediction time window.

8. A wireless communications device (121) for reporting beam predictions for a wireless node (111, 122), comprising:14.- an input and output interface (906);15.- a processor (904) configured to :16.obtain (701), a first configuration of a prediction time window within which beam prediction for the wireless node (111, 122) is to be determined;17.transmit (703) an indication of a proposed prediction time window to the wireless node, within which beam prediction for the wireless node (111, 122) is to be determined;18.receive (704), from the wireless node (111, 122), a second updated configuration of the prediction time window; and19.report (705) a beam prediction to the wireless node, wherein the beam prediction comprises an indication of at least one predicted beam to be transmitted from the wireless node (111, 122) within the prediction time window in the second updated configuration.

9. A method, performed by a wireless node (111, 122), to handle beam predictions for the wireless node (111, 122), the method comprising:21.transmitting (801), to a wireless communications device (121), a first configuration of a prediction time window within which beam prediction for the wireless node (111, 122) is to be determined;22.receiving (802), from the wireless communications device (121), an indication of a proposed prediction time window within which beam prediction for the wireless node (111, 122) is to be determined;23.transmitting (803), to the wireless communications device (121), a second updated configuration of the prediction time window; and24.receiving (804), from the wireless communications device (121), a beam prediction comprising an indication of at least one predicted beam to be transmitted from the wireless node (111, 122) within the prediction time window in the second updated configuration.

10. The method according to claim 9, wherein the second updated configuration of the prediction time window is based on the received indication of the proposed prediction time window.

11. The method according to claim 9 or 10, further comprising: transmitting (805) information to the wireless communications device (121) on the at least one predicted beam.

12. A wireless node (111, 122) configured to handle beam predictions for the wireless node (111, 122), comprising:27.- an input and output interface (1006);28.- a processor (1004) configured to:29.transmit (801), to a wireless communications device (121), a first configuration of a prediction time window within which beam prediction for the wireless node (111, 122) is to be determined;30.receive (802), from the wireless communications device (121), an indication of a proposed prediction time window within which beam prediction for the wireless node (111, 122) is to be determined;31.transmit (803), to the wireless communications device (121), a second updated configuration of the prediction time window; and32.receive (804), from the wireless communications device (121), a beam prediction comprising an indication of at least one predicted beam to be transmitted from the wireless node (111, 122) within the prediction time window in the second updated configuration.

13. A computer program, comprising computer readable code units which when executed on a processor of a wireless communications device causes the wireless communications device to perform the method according to any of the claims 1-7.

14. A computer program, comprising computer readable code units which when executed on a processor of a wireless node causes the wireless node to perform the method according to any of the claims 9-11.

15. A carrier comprising the computer program according to any of claims 13 or 14, wherein the carrier is one of an electronic signal, an optical signal, a radio signal and a computer readable medium.

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