Beam reporting

Abstract

Examples of the present disclosure relate to beam reporting in cases where prediction has been used to determine the best beam. A UE can receive a beam reporting configuration for a set of reference signal (RS) resources and determine whether to perform beam measurements or beam prediction for the set of RS resources to determine a best beam in the set of RS resources. The UE can then determine the best beam in the set of RS resources using at least one of the beam measurements or the beam prediction and provide a beam report indicating a best beam index for the set of RS resources, where the beam report indicates whether the UE has determined the best beam using beam prediction.

Description

Technical Field

[0001] Examples in this disclosure relate to beam reporting. Some involve beam reporting in cases where predictions have been used to determine the optimal beam. Background Technology

[0002] Communication networks can utilize models, such as AI / ML, to perform tasks like beam measurement and beam reporting. The use of such models can serve as an alternative to, or an extension of, model-based implementations. Summary of the Invention

[0003] Based on various (but not all) examples of this disclosure, a user equipment (UE) is provided, including: At least one processor; and At least one memory storing instructions, which, when executed by the at least one processor, cause the UE to perform at least the following: Receive beam report configuration for reference signal (RS) resource set; Determine whether to perform beam measurement or beam prediction for the RS resource set to determine the optimal beam in the RS resource set; The optimal beam in the RS resource set is determined using at least one of beam measurement or beam prediction; and A beam report is provided that indicates the optimal beam index for the RS resource set, wherein the beam report indicates whether the UE has used beam prediction to determine the optimal beam.

[0004] The beam report may include an index indicating whether the UE has used beam prediction to determine the optimal beam.

[0005] Information indicating the index can be received in the beam report configuration.

[0006] The UE can determine the first subset of usage predictions for the RS resource set, and the second subset of usage measurements for the RS resource set.

[0007] The at least one processor and the at least one memory can also enable the UE to perform instructions on which beams or subsets of beams have been predicted.

[0008] Indications regarding which beams or subsets of beams have been predicted may be provided in at least one of the following: beam reports; and additional reports.

[0009] Machine learning models can be used to perform beam prediction.

[0010] Beam reports may include a best beam index. This index may be quantified based on the size of the RS resource set. Beam reports may include an L1-RSRP for the best beam index used in the report.

[0011] The resource set may include one or more of the following: Synchronization Signal Block (SSB) beam; Channel State Information-Reference Signal (CSI-RS) beam.

[0012] Beam reports can be at least one of the following: periodic; semi-persistent; non-periodic; or UE-initiated.

[0013] RS resource sets can be configured as at least one of the following: periodic; semi-persistent; and non-periodic CSI-RS resources.

[0014] Based on various, but not necessarily all, examples of this disclosure, a method may be provided, including: Receive beam report configuration for reference signal (RS) resource set; Determine whether to perform beam measurement or beam prediction for the RS resource set to determine the optimal beam in the RS resource set; The optimal beam in the RS resource set is determined using at least one of beam measurement or beam prediction; and A beam report is provided that indicates the optimal beam index for the RS resource set, wherein the beam report indicates whether the UE has used beam prediction to determine the optimal beam.

[0015] According to various, but not necessarily all, examples of this disclosure, a computer program may be provided, including computer program instructions for causing a UE to perform at least the following operations or for performing at least the following operations: Receive beam report configuration for reference signal (RS) resource set; Determine whether to perform beam measurement or beam prediction for the RS resource set to determine the optimal beam in the RS resource set; The optimal beam in the RS resource set is determined using at least one of beam measurement or beam prediction; and A beam report is provided that indicates the optimal beam index for the RS resource set, wherein the beam report indicates whether the UE has used beam prediction to determine the optimal beam.

[0016] Based on various, but not necessarily all, examples of this disclosure, a network node may be provided, including: At least one processor; and At least one memory storing instructions that, when executed by the at least one processor, cause the network node to execute at least the following: Configuration for sending beam reports for the Reference Signal (RS) resource set; Send RS resources; and Receive a beam report indicating the optimal beam index for the RS resource set, wherein the beam report indicates whether beam prediction has been used to determine the optimal beam.

[0017] The at least one processor and the at least one memory can also enable the network node to perform receiving instructions about which beams or subsets of beams have been predicted.

[0018] Indications regarding which beams or subsets of beams have been predicted can be received from at least one of the following: Beam report; and Additional report.

[0019] The at least one processor and the at least one memory can also enable the network node to perform actions based on received instructions to determine which beams or subsets of beams were used for prediction, and to update the RS resources.

[0020] Updating RS resources can include removing one or more beams from the resource set that have used predictions.

[0021] The at least one processor and the at least one memory can also enable the network node to perform performance monitoring of beam prediction operations.

[0022] The beam report includes an index indicating whether the UE has used beam prediction to determine the optimal beam.

[0023] The index can be sent in this configuration.

[0024] Prediction can be used for a first subset of the RS resource set, and measurement can be used for a second subset of the RS resource set.

[0025] Machine learning models can be used to perform beam prediction.

[0026] Based on various, but not necessarily all, examples of this disclosure, a method may be provided, including: Configuration for sending beam reports for the Reference Signal (RS) resource set; Send RS resources; and Receive a beam report indicating the optimal beam index of the RS resource set, wherein the beam report indicates whether beam prediction has been used to determine the optimal beam.

[0027] According to various, but not necessarily all, examples of this disclosure, a computer program including computer program instructions may be provided, said computer program instructions being used to cause a network node to perform at least the following operations or to perform at least the following operations: Configuration for sending beam reports for the Reference Signal (RS) resource set; Send RS resources; and Receive a beam report indicating the optimal beam index of the RS resource set, wherein the beam report indicates whether beam prediction has been used to determine the optimal beam.

[0028] According to various (but not all) embodiments, an apparatus is provided, comprising: At least one processor; and At least one memory; The at least one memory stores instructions that, when executed by the at least one processor, cause the device to perform at least a portion of one or more methods described herein.

[0029] According to various (but not all) embodiments, an apparatus is provided that includes components for performing at least a portion of one or more methods described herein. The description of functions and / or actions should also be considered as disclosing any components suitable for performing those functions and / or actions. The functions and / or actions described herein can be performed using any suitable method and in any suitable manner.

[0030] Examples are provided according to various (but not all) embodiments, as claimed in the appended claims.

[0031] Although the examples and optional features of this disclosure are described separately, it should be understood that this disclosure includes all possible combinations and permutations of them. It should be understood that the various examples of this disclosure may include any or all of the features described with respect to other examples of this disclosure, and vice versa. Furthermore, it should be understood that any one or more features in any combination may be implemented / included / performed by any means, method, and / or instructions as needed and appropriate. The description of a function should also be considered as disclosing any components suitable for performing that function. Attached Figure Description

[0032] Some examples will now be described with reference to the accompanying drawings, in which:

[0033] Figure 1 An example network is shown;

[0034] Figure 2A and 2B Example methods are shown;

[0035] Figure 3An exemplary signaling process is shown; and

[0036] Figure 4 An example controller is shown.

[0037] The accompanying drawings are not necessarily to scale. For clarity and simplicity, some features and views in the drawings may be shown schematically or enlarged to scale. For example, the dimensions of some elements in the drawings may be enlarged relative to other elements to aid illustration. Appropriate reference numerals are used in the drawings to indicate corresponding features. For clarity, not all reference numerals need to be shown in all drawings.

[0038] definition AI (Artificial Intelligence) CSI Channel State Information CSI-RS Channel State Information - Reference Signal ML Machine Learning RS reference signal RSRP reference signal received power SSB Synchronization Signal Block UE User Equipment Detailed Implementation

[0039] Figure 1 An example of a communication network 100 is shown, and the examples of this disclosure can be applied to the communication network 100. The communication network 100 is a cellular communication network. The communication network 100 includes a network access node 102. The network access node 102 provides one or more cells 104. The cell 104 can define the coverage area or service area of ​​the corresponding network access node 102.

[0040] Network access node 102 can provide radio access to a communication network to one or more user equipment (UE) devices 106. Radio access may include downlink (DL) communication from network access node 102 to UE 106 and uplink (UL) communication from UE 106 to network access node 102. Examples of uplink channels include a physical uplink control channel (PUCCH) for sending control information to network access node 102 and a physical uplink shared channel (PUSCH) for sending data. Examples of downlink channels include a physical downlink control channel (PDCCH) for sending control information to UE 106 and a physical downlink shared channel (PDSCH) for sending data.

[0041] Multiple UEs 106 can exist in network 100. Each UE 106 can be served by the same or different network access nodes 102.

[0042] If the communication network 100 includes multiple network access nodes 102, the network access nodes 102 can be connected to each other via an interface. The LTE specification refers to this interface as the X2 interface. The interface between an LTE node and a 5G node, or between two 5G nodes, can be referred to as the Xn interface.

[0043] Network access node 102 can also be connected to the core network 108 of communication network 100 via another interface. Core network 108 may include core network nodes. The LTE specification designates the core network as an Evolved Packet Core (EPC), and the core network may include, for example, a Mobility Management Entity (MME) and gateway nodes. The MME can handle the mobility of terminal devices in a tracking area containing multiple cells, and handle signaling connections between terminal devices, such as UE 106, and core network 108. Gateway nodes can handle data routing in core network 108 and data routing to / from terminal devices, such as UE 106. The 5G specification designates the core network as a 5G core (5GC). The 5G core may include, for example, Access and Mobility Management Functions (AMF) and User Plane Functions / Gateways (UPF), as well as other functions. The AMF can handle non-access stratum (NAS) signaling termination, NAS encryption and integrity protection, registration management, connection management, mobility management, access authentication and authorization, and security context management. UPF nodes can support, for example, packet routing and forwarding, packet inspection, and Quality of Service (QoS) processing. In other types of networks, other types of entities can be provided within the core network 108.

[0044] For example Figure 1 The network 100, as shown, can utilize models such as artificial intelligence (AI) / machine learning (ML) to perform tasks such as beam measurement and reporting. For example, UE 106 can perform measurements on a set of reference signal (RS) resources and report beam information to network node 102. UE 106 can perform this task or any other suitable task using a non-model implementation or a model-based implementation. Whether UE 106 uses a model may depend on factors such as the accuracy or validity of the model or any other suitable factor.

[0045] Using models to perform these tasks can serve as an alternative to, or an extension of, non-model-based implementations. Signaling between entities in network 100 should take into account whether or not models are used, depending on the use case. The signaling used should reflect this, allowing the same signaling to be used regardless of whether UE 106 is using a model to perform the task.

[0046] Figure 2A and 2BExample methods that can be used in the implementation of this disclosure are shown. Figure 2A The example method can be implemented by UE106 or any other suitable device. Figure 2B An example can be implemented by network node 102. Network node 102 can be, for example, an access node of a gNB or any other suitable type of network node. Figure 2B The method corresponds to Figure 2A The method, because of execution Figure 2B The network node 102 of the method can be used for execution Figure 2A The method of UE 106 provides access to network 100.

[0047] The method implemented by UE 106 includes, at block 200, receiving a beam report configuration for an RS resource set. The RS resource set may include a synchronization signal block (SSB) beam, a channel state information-reference signal (CSI-RS) beam, or any other suitable resource that may be used to determine the optimal beam.

[0048] RS resource sets can be configured as periodic, semi-persistent, or aperiodic CSI-RS resources. In some examples, RS resource sets can be configured as on-demand CSI-RS resources or UE-initiated CSI-RS resources, where the UE can request the transmission of CSI-RS resources.

[0049] At block 202, UE 106 determines whether to perform beam measurement or beam prediction on the RS resource set to determine the optimal beam in the RS resource set. If UE 106 determines to perform prediction, UE 106 can use a model such as an AI / ML model to perform the prediction.

[0050] In some examples, UE 106 can determine whether it is usage prediction for all usage within the RS resource set or usage measurement for all usage within the RS resource set. In other examples, UE 106 can determine some usage predictions for the RS resource set and usage measurements for other portions of the RS resource set. For example, UE 106 can determine usage predictions for a first subset of the RS resource set and usage measurements for a second subset of the RS resource set. This second subset may be different from the first subset.

[0051] At block 204, the UE determines the optimal beam in the RS resource set. Based on the decision made in block 202, the UE 106 can use beam measurement and / or beam prediction to determine the optimal beam.

[0052] At block 206, UE 106 provides a beam report indicating the optimal beam index for the RS resource set. UE 106 may send this beam report to network node 102. The beam report may include any appropriate information, such as the optimal beam index. The index may be measured based on the size of the RS resource set. The beam report may include the L1 reference signal received power (RSRP) or any other appropriate information for the reported optimal beam index. If UE 106 uses measurement, the beam report may include the optimal beam index and corresponding L1-RSRP based on direct measurements for each beam. If UE 106 uses prediction, the beam report may include the optimal beam index and corresponding L1-RSRP based on beam prediction without performing direct measurements for each beam.

[0053] Beam reports can be periodic, semi-persistent, or aperiodic. In some examples, beam reports can be initiated by the UE.

[0054] In the example disclosed herein, the beam report also indicates whether UE 106 has used beam prediction to determine the optimal beam. That is, the beam report can indicate whether UE 106 has used a model. If beam prediction has already been used for one or more beams, it is not necessary to perform measurements on those beams as well.

[0055] The use of prediction can be indicated in the beam report in any appropriate manner. In some examples, the beam report may include an index indicating whether the UE 106 has used beam prediction to determine the optimal beam. In such examples, information indicating the index can be received in the beam report configuration or via any other appropriate signaling. The UE 106 can then use this information to select the appropriate index for use in the beam report.

[0056] If UE 106 has already used prediction, in some examples, UE 106 can also indicate which beams or subsets of beams have been predicted. In such examples, UE 106 can specify whether prediction has been used for each beam or subset of beams, or it can indicate only that prediction has been used for the entire RS resource set.

[0057] In some examples, the beam report provided at block 206 indicates which beams or subsets of beams have been predicted. In other examples, this indication may be provided in a separate supplementary report from the beam report. Separate reports may be used to report longer time ranges. In some examples, separate reports may provide usage information of measurements at the model input for beam prediction. This usage information may indicate which beams transmitted by network node 102 are actually useful for performing inference operations with good accuracy at UE 106. In some examples, separate reports may be determined based on long-term statistics derived from the inputs at the beam prediction model. In some examples, UE 106 may consider more than one number of inference instances and determine the most frequent or highest-importance beam measurements required at the model input for generating accurate beam predictions. In some examples, separate reports may be associated with performance monitoring reports used to monitor the performance level of beam prediction at UE 106. Network node 102 can use indications about which beams or subsets of beams have been predicted to determine which beams do not need to be transmitted in the RS resource set.

[0058] The method implemented by network node 102 includes, at block 210, sending a configuration for beam reporting of the RS resource set. This configuration is received by UE 106 at block 200.

[0059] At block 212, network node 102 transmits RS resources. The RS resource set may include SSB beams, CSI-RS beams, or any other suitable resources that can be used to determine the optimal beam. The RS resource set may be configured as periodic, semi-persistent, or aperiodic CSI-RS resources.

[0060] In block 214, network node 102 receives a beam report. This beam report may be received from UE 106. The beam report indicates the optimal beam index for the RS resource set. The beam report also indicates whether beam prediction has been used to determine the optimal beam.

[0061] The use of prediction can be indicated in the beam report in any appropriate manner. In some examples, the beam report may include an index indicating whether UE 106 has used beam prediction to determine the optimal beam. In such examples, information indicating this index can be sent to UE 106 in the beam report configuration or via any other appropriate signaling.

[0062] In some examples, usage predictions can be made for all usage within the RS resource set, or usage measurements can be made for all usage within the RS resource set. In other examples, usage predictions can be made for some usage within the RS resource set, and usage measurements can be made for other portions of the RS resource set. For example, UE 106 can determine usage predictions for a first subset of the RS resource set and usage measurements for a second subset of the RS resource set. This second subset may be different from the first subset. In this case, network node 102 can also receive an indication of which beams or beam subsets have been predicted. This indication of which beams or beam subsets have been predicted can be received in the beam report received at block 214. In other examples, this indication of which beams or beam subsets have been predicted can be received in a supplementary report separate from the beam report.

[0063] When network node 102 has received an indication of which beams or subsets of beams have been predicted, network node 102 can use this information to determine which beams or subsets of beams to use prediction for and update the RS resources. Network node 102 can determine that RS resources do not need to be sent for beams for which prediction has already been used. Therefore, network node 102 can update the RS resources. The RS resources can be updated by removing one or more beams for which prediction has already been used from the resource set.

[0064] Network node 102 can also perform performance monitoring of beam prediction operations. Performance monitoring can begin after network node 102 has received an indication that beam prediction has been used to determine the optimal beam. If the performance monitoring indicates that the prediction is below an acceptable level of accuracy, network node 102 will not update the RS resource set or take actions such as beam refinement, channel state information (CSI) acquisition, or beam activation / indication.

[0065] In the examples disclosed herein, a machine learning model can be used to perform beam prediction. The machine learning model may include multiple defined processing steps and can resemble processing instructions associated with regular program code. The difference between regular program code and a machine learning model is that the instructions of regular program code are more explicitly defined at programming time. The instructions of a machine learning model are defined by combining a set of predefined processing blocks (e.g., convolution, data normalization, other operators), where the model's weights are unknown at the time of model definition. The weights of the machine learning model are optimized by providing it with a large amount of input and reference data, and the model weights then converge, allowing the machine learning model to be trained to solve a given task. In this case, the task is to predict the optimal beam for a set of RS resources. In the examples disclosed herein, when the machine learning model is used, the machine learning model can be fixed and can correspond to a set of processing instructions.

[0066] In the example disclosed herein, the machine learning model can reside in UE 106. The machine learning model can be trained to provide a prediction of the optimal beam as output. This prediction can be provided in any suitable format.

[0067] Figure 3 An example signaling procedure between UE 106 and network node 102, which can be used in some implementations of this disclosure, is shown.

[0068] At block 300, network node 102 sends beam reporting configuration for the RS resource set to UE 106. Network node 102 uses the beam reporting configuration to configure parameters for beam reporting for UE 106. The beam reporting configuration may include an indication of the RS resource set, the number of reports (the number of beams to be reported), and any other appropriate information. The beam reporting configuration may be a CSI reporting configuration or any other suitable type of configuration.

[0069] At block 302, network node 102 sends a downlink (DL) reference signal within the RS resource set so that UE 106 can determine the beam quality.

[0070] In block 304, UE 106 can determine whether to use prediction or measurement to determine the optimal beam. If UE 106 determines to use prediction, then UE 106 can determine whether the prediction should be used for all RS resources or only for a subset of resources.

[0071] UE 106 may determine whether to use prediction based on any suitable factors, such as model availability, model accuracy, and / or any other suitable factors.

[0072] In this example, UE 106 uses measurements for a first subset of the RS resources and predictions for a second subset of the RS resources. At block 306, UE 106 performs measurements for the first subset of the RS resources. At block 308, UE 106 performs predictions for the second subset of the RS resources. The predictions can be based on a finite number of measurements and historical measurements. UE 106 uses these measurements and the predictions to determine the optimal beams. For example, UE 106 can determine the top K beams.

[0073] In block 310, UE 106 provides a beam report to network node 102. The beam report can indicate the top K beams or any other suitable information. The beam report can also indicate which beams have been predicted to determine the optimal beam.

[0074] At block 312, network node 102 repeats the transmission of the DL reference signal. At block 314, UE 106 repeats the measurement for the first subset of the RS resources, and at block 316, UE 106 repeats the prediction for the second subset of the RS resources. At block 318, UE 106 repeats sending beam reports to network node 102.

[0075] After some time has passed, in block 320, UE 106 sends a usage report to network node 102. The usage report may include information that enables network node 102 to determine which beams are necessary and which are not. For example, the usage report may indicate which beams or subsets of beams have been predicted. Network node 102 may determine that the predicted beams or subsets of beams are not necessary, but the measured beams or subsets of beams are necessary.

[0076] At block 322, network node 102 determines RS overhead reduction. Network node 102 can determine which beams are unnecessary and can adjust the RS resource set so that unnecessary beams are not transmitted. Network node 102 can use information received in the usage report to determine which beams are unnecessary.

[0077] At block 324, the network node sends updated RS resources. The updated resources do not include unnecessary beams. The updated RS resources may include beams used for measurement, but do not need to include predicted beams.

[0078] At block 326, UE 106 performs measurements on a first subset of the updated RS resources, and at block 328, UE 106 performs predictions on a second subset of the updated RS resources. At block 330, UE 106 repeatedly sends a beam report to network node 102, which is based on the predictions and measurements performed using the updated resource set.

[0079] The examples disclosed herein can reduce the measurement overhead of UE 106. UE 106 can use predictions for at least some beams, thus eliminating the need to measure all configured beams. This significantly reduces processing complexity and power consumption. The use of predictions enables efficient selection of beam subsets, making the process more resource-efficient.

[0080] The examples disclosed herein also enable early optimization of AI / ML models. AI / ML operations for UE 106 can be used immediately after beam measurement configuration. This allows for faster tuning and better beam selection without exhaustive measurements.

[0081] Using reports or other indications regarding necessary and unnecessary beams can improve network efficiency. Reports enable network node 102 to adjust RS resource transmission and reduce unnecessary RS overhead. Network node 102 can reduce RS overhead by identifying and disabling unnecessary beam transmissions based on reports from UE 106. This results in better spectral efficiency and improved network performance.

[0082] The examples disclosed also provide seamless support for beam prediction and measurement. There are no segments in the specification support, ensuring that the mechanism works regardless of whether the UE 106 supports beam prediction, beam measurement, or both. This guarantees broad compatibility across different UE 106 capabilities and network deployments.

[0083] Figure 4 An exemplary controller 400 is shown. Controller 400 may be provided within UE 106, network node 102, or any other suitable entity. Controller 400 may be implemented as controller circuitry. Controller 400 may be implemented solely in hardware, may have only certain aspects of software including firmware, or may be a combination of hardware and software (including firmware). Controller 400 may be provided as means for implementing this disclosure, or may be provided as part of means for implementing this disclosure.

[0084] like Figure 4 As shown, the controller 400 can be implemented using instructions that implement hardware functions, for example, by using executable instructions of a computer program 406 in a general-purpose or special-purpose processor 402, which can be stored on a machine-readable storage medium (disk, memory, etc.) for execution by such processor 402.

[0085] Processor 402 is configured to read from and write to memory 404. Processor 402 may also include an output interface and an input interface, through which processor 402 outputs data and / or commands, and through which data and / or commands are input to processor 402.

[0086] Memory 404 stores instructions, programs 406, or code that control the operation of the device when loaded into processor 402. The instructions, programs 406, or code provide logic and routines that enable the device to perform the methods shown in the figures. Processor 402 can load and execute the instructions, programs 406, or code by reading memory 404.

[0087] In some examples where controller 400 is provided within UE 106, the controller therefore includes means for the following operations: Receive 200 beam report configurations for the reference signal (RS) resource set; Determine whether to perform beam measurement or beam prediction for the RS resource set to determine the optimal beam in the RS resource set; Use at least one of beam measurement or beam prediction to determine the optimal beam in the 204 RS resource set; and Provides a beam report 206 indicating the optimal beam index for the RS resource set, wherein the beam report indicates whether the UE 106 has used beam prediction to determine the optimal beam.

[0088] In some examples where controller 400 is provided within network node 102, the controller therefore includes means for: Configuration for sending beam reports for the Reference Signal (RS) resource set; Send 212 RS resources; and Receive 214 a beam report indicating the optimal beam index for the RS resource set, wherein the beam report indicates whether beam prediction has been used to determine the optimal beam.

[0089] Instructions, program 406, or code can reach the device via any suitable delivery mechanism 408. Delivery mechanism 408 can be, for example, a machine-readable medium, a computer-readable medium, a non-transitory computer-readable storage medium, a computer program product, a memory device, such as a recording medium like a CD-ROM or DVD, or a solid-state storage device, or an article of art that includes or tangibly embodies the computer program 406. The delivery mechanism can be a signal configured to reliably transmit the computer program 406. The device can propagate or transmit the computer program 406 as a computer data signal.

[0090] As used herein, the term “non-transitory” refers to a limitation of the medium itself (which is tangible, not signal-based), as opposed to a limitation on the persistence of data storage (e.g., RAM versus ROM).

[0091] Computer program 406 may include computer program instructions for causing UE 106 to perform at least the following operations or for performing at least the following operations: Receive 200 beam report configurations for the reference signal (RS) resource set; Determine whether to perform beam measurement or beam prediction for the RS resource set to determine the optimal beam in the RS resource set; Use at least one of beam measurement or beam prediction to determine the optimal beam in the 204 RS resource set; and Provides a beam report 206 indicating the optimal beam index for the RS resource set, wherein the beam report indicates whether the UE 106 has used beam prediction to determine the optimal beam.

[0092] Computer program 406 may include computer program instructions for causing network node 102 to perform at least the following operations or for performing at least the following operations: Configuration for sending beam reports for the Reference Signal (RS) resource set; Send 212 RS resources; and Receive 214 a beam report indicating the optimal beam index for the RS resource set, wherein the beam report indicates whether beam prediction has been used to determine the optimal beam.

[0093] Computer program instructions may be included in a computer program, a non-transitory computer-readable medium, a computer program product, or a machine-readable medium. In some, but not all, examples, computer program instructions may be distributed across more than one computer program.

[0094] Although memory 404 is illustrated as a single component / circuit, it can be implemented as one or more separate components / circuits, some or all of which can be integrated / removable and / or can provide permanent / semi-permanent / dynamic buffer storage.

[0095] Although processor 402 is shown as a single component / circuit, it can be implemented as one or more separate components / circuits, some or all of which may be integrated / removable. Processor 402 can be a single-core or multi-core processor.

[0096] References to “computer-readable storage medium,” “computer program product,” “computer program tangibly embodied,” or “controller,” “computer,” “processor,” etc., should be understood to encompass not only computers with different architectures (e.g., single / multiprocessor architectures and sequential (von Neumann) / parallel architectures) but also special-purpose circuits such as field-programmable gate arrays (FPGAs), special-purpose circuits (ASICs), signal processing devices, and other processing circuits including quantum processing circuits. References to computer programs, instructions, code, etc., should be understood to encompass software or firmware for programmable processors, such as the programmable content of hardware devices, whether instructions for processors or configuration settings for fixed-function devices, gate arrays, or programmable logic devices, etc.

[0097] As used in this application, the term "circuit" may refer to one or more of the following: (a) Hardware circuit implementation only (e.g., implementation using only analog and / or digital circuits) and (b) A combination of hardware circuitry and software, such as (if applicable): (i) A combination of analog, digital, and / or quantum hardware circuitry with software / firmware, and (ii) Any or all portions of a hardware processor (including digital and / or quantum processors) with software, and memory, which work together to enable a device, such as a mobile device, computing device, or server, to perform various functions, and (c) Any or all parts of hardware circuitry (e.g., one or more microprocessors and / or one or more quantum processors) that require software (e.g. firmware) to operate, but which may not exist when no software is required to operate.

[0098] This definition of "circuit" applies to all uses of the term in this application, including in any claim. As a further example, as used in this application, the term "circuit" also covers only hardware circuitry or a processor (or multiple processors), or a portion of hardware circuitry or a processor and its accompanying software and / or firmware implementation. For example, and if applicable to the elements of the claims, the term "circuit" also covers baseband integrated circuits or processor integrated circuits for mobile devices, or similar integrated circuits in servers, cellular network devices, or other computing or network devices.

[0099] The blocks shown in the figure may represent steps in a method and / or code segments in computer program 406. The description of a specific order of blocks does not necessarily imply a required or preferred order, and the order and arrangement of blocks can be changed. Furthermore, some blocks may be omitted.

[0100] In the case where a structural feature has been described, whether the function or those functions are explicitly or implicitly described, the structural feature can be replaced by a means for performing one or more functions of the structural feature.

[0101] According to the examples of this disclosure, the device can be provided in an electronic device, such as a mobile terminal. However, it should be understood that a mobile terminal is merely an illustration of an electronic device that will benefit from examples of implementations of this disclosure, and therefore should not be construed as limiting the scope of this disclosure to the same extent. In some implementation examples, the device can be provided in mobile terminals, other types of electronic devices, such as, but not limited to: mobile communication devices, handheld portable electronic devices, wearable computing devices, portable digital assistants (PDAs), pagers, mobile computers, desktop computers, televisions, gaming devices, laptop computers, cameras, video recorders, GPS devices, and other types of electronic systems that can readily adopt the examples of this disclosure. Furthermore, devices can readily adopt the examples of this disclosure regardless of their intention to provide mobility.

[0102] The term "includes" is used in this document in an inclusive rather than exclusive sense. That is, any reference to X including Y means that X may include only one Y, or may include more than one Y. If you intend to use "includes" in an exclusive sense, then make it clear in the context by referring to "includes only one" or by using "consisting of".

[0103] In this specification, the terms “connection,” “coupling,” and “communication,” and their derivatives, mean operational connection / coupling / communication. It should be understood that any number of intermediate components or combinations of intermediate components (including no intermediate components) may exist, i.e., providing direct or indirect connection / coupling / communication. Any such intermediate component may include hardware and / or software components.

[0104] As used herein, the term "determine / identify" (and its grammatical variations) may include, but is not limited to: calculation, operation, processing, derivation, measurement, investigation, identification, lookup (e.g., searching in a table, database, or other data structure), ascertainment, etc. Furthermore, "determine" may include receiving (e.g., receiving information), accessing (e.g., accessing data in memory), obtaining, etc. Moreover, "determine / identify" may include parsing, selecting, picking, building, etc.

[0105] Various examples have been referenced in this specification. Descriptions of features or functions associated with an example indicate which features or functions exist in that example. The use of the terms "example," "for example," or "may" in the text indicates that, whether explicitly stated or not, such features or functions exist in at least the described example, whether or not they are described as examples, and that they may, but not necessarily, exist in some or all other examples. Thus, "example," "for example," "may," or "may" refers to a specific instance of a class of examples. An instance's property may be a property of only that instance, or a property of the class, or a property of a subclass of the class that includes some but not all instances of the class. Therefore, it is implicitly disclosed that features described with reference to one example but not to another may, where possible, be used as part of a work composition in that other example, but are not necessarily required to be used in that other example.

[0106] As used herein, “at least one of the following:” and “at least one” and similar wording, wherein two or more listed elements are connected by “and” or “or”, means at least any one element, or at least any two or more elements, or at least all elements.

[0107] Although examples have been described in the foregoing paragraphs with reference to various examples, it should be understood that modifications may be made to the given examples without departing from the scope of the claims.

[0108] The features described above can be used in combinations other than those explicitly described above.

[0109] Although the functionality has been described with reference to certain features, these functions can be performed by other features, whether or not they have been described.

[0110] A description of a feature (e.g., a device or a component of a device) configured to perform a function or for performing a function should also be considered as disclosing a method for performing that function. For example, a description of a device configured to perform one or more actions or for performing one or more actions should also be considered as disclosing a method for performing those one or more actions with or without the device.

[0111] Although features have been described with reference to some examples, those features may exist in other examples, whether or not they have been described.

[0112] The terms “a,” “an,” or “the” are used in this document in an inclusive rather than exclusive sense. That is, any reference to X including a / an / the Y means that X may include only one Y or may include more than one Y, unless the context clearly indicates otherwise. If “a,” “an,” or “the” is intended to be used in an exclusive sense, it will be clearly stated in the context. In some cases, the use of “at least one” or “one or more” may be used to emphasize an inclusive meaning, but the absence of these terms should not be taken as an inference of any exclusive meaning.

[0113] The presence of a feature (or combination of features) in a claim is a reference to that feature or combination of features itself, as well as features that achieve substantially the same technical effect (equivalent features). Equivalent features include, for example, features that are variations and achieve substantially the same result in substantially the same manner. Equivalent features include, for example, features that perform substantially the same function in substantially the same manner to achieve substantially the same result.

[0114] Various examples have been provided in this specification, using adjectives or adjective phrases to describe the characteristics of various instances. Such descriptions of characteristics associated with examples indicate that the characteristic exists in some examples that are exactly the same as those described, and in other examples that are substantially the same as those described.

[0115] As used herein, the terms “at least one” and “one or more” mean “any one of at least one” and “any one of one or more”, respectively.

[0116] The foregoing has described some examples of this disclosure; however, those skilled in the art will recognize possible alternative structural and methodological features that provide equivalent functionality to the specific examples of such structures and features described herein, and for the sake of brevity and clarity, have been omitted from the foregoing description. Nevertheless, the foregoing description should be understood to implicitly include references to such alternative structural and methodological features that provide equivalent functionality, unless such alternative structural or methodological features are expressly excluded in the foregoing description of the examples of this disclosure.

[0117] Despite the effort made in the foregoing specification to focus on those features considered important, an applicant may seek protection by means of the claims for any patentable feature or combination of features mentioned above and / or shown in the figures, whether or not they are emphasized.

Claims

1. A user equipment (UE), comprising: At least one processor; as well as At least one memory storing instructions that, when executed by the at least one processor, cause the UE to perform at least the following: Receive beam report configuration for reference signal (RS) resource set; Determine whether to perform beam measurement or beam prediction for the RS resource set to determine the optimal beam in the RS resource set; The optimal beam in the RS resource set is determined using at least one of beam measurement or beam prediction; and A beam report is provided that indicates the optimal beam index for the RS resource set, wherein the beam report indicates whether the UE has used beam prediction to determine the optimal beam.

2. The UE according to claim 1, wherein, The beam report contains an index indicating whether the UE has used beam prediction to determine the optimal beam.

3. The UE according to claim 2, wherein, Information indicating the index is received in the beam reporting configuration.

4. The UE according to any one of claims 1 to 3, wherein, The UE determines the use prediction for a first subset of the RS resource set and the use measurement for a second subset of the RS resource set.

5. The UE according to claim 4, wherein, The at least one processor and the at least one memory also enable the UE to perform an indication of which beams or subsets of beams have been predicted.

6. The UE according to claim 5, wherein, Provide an indication of which beams or subsets of beams have been predicted in at least one of the following: Beam report; and Additional report.

7. The UE according to any one of the preceding claims, wherein, The beam prediction is performed using a machine learning model.

8. The UE according to any one of the preceding claims, wherein, The beam report includes a best beam index.

9. The UE according to claim 8, wherein, The index is measured based on the size of the RS resource set.

10. The UE according to any one of claims 8 to 9, wherein, The beam report includes the L1-RSRP for the best beam index used in the report.

11. The UE according to any one of the preceding claims, wherein, The resource set includes one or more of the following: Synchronization Signal Block (SSB) beam; and Channel State Information-Reference Signal (CSI-RS) beam.

12. The UE according to any one of the preceding claims, wherein, The beam report is at least one of the following: Periodic; Semi-persistent; Non-periodic; and Beam report initiated by the UE.

13. The UE according to any one of the preceding claims, wherein, The RS resource set is configured as at least one of the following: Periodic; Semi-persistent; and Non-periodic CSI-RS resources.

14. A network node, comprising: At least one processor; as well as At least one memory storing instructions that, when executed by the at least one processor, cause the network node to execute at least the following: Configuration for sending beam reports for the Reference Signal (RS) resource set; Send RS resources; and Receive a beam report indicating the optimal beam index for the RS resource set, wherein the beam report indicates whether beam prediction has been used to determine the optimal beam.

15. The network node according to claim 14, wherein, The at least one processor and the at least one memory also enable the network node to perform receiving instructions about which beams or subsets of beams have been predicted.

16. The network node according to claim 15, wherein, The indication regarding which beams or subsets of beams have been predicted is received in at least one of the following: Beam report; and Additional report.

17. The network node according to any one of claims 14 to 16, wherein, The at least one processor and the at least one memory also enable the network node to perform actions based on received instructions to determine which beam predictions were used for, or subsets of beams, and to update the RS resources.

18. The network node according to claim 17, wherein, Updating the RS resources includes removing one or more beams from the resource set whose usage predictions are being made.

19. The network node according to any one of claims 14 to 18, wherein, The at least one processor and the at least one memory also enable the network node to perform performance monitoring of beam prediction operations.

20. The network node according to any one of claims 14 to 19, wherein, Predictions are used for a first subset of the RS resource set, and measurements are used for a second subset of the RS resource set.

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