Sending capability reports indicating beam prediction capabilities of user equipment

Abstract

Aspects of the present disclosure generally relate to wireless communications. In some aspects, a user equipment (UE) may send a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy. The UE may receive a request to perform a beam prediction task that remains consistent with the capability report. The UE may transmit a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request. Numerous other aspects are described.

Description

Background Art

[0001] Aspects of the present disclosure relate generally to wireless communications, and to techniques and apparatus for capability reporting.

[0002] Wireless communication systems are widely deployed to provide a variety of telecommunication services, such as telephony, video, data, messaging, and broadcasting. Typical wireless communication systems may employ multiple access technologies that can support communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, etc.). Examples of such multiple access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and long term evolution (LTE). LTE / LTE-Advanced is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP).

[0003] A wireless network may include one or more network nodes that support communications for wireless communication devices, such as user equipment (UE) or multiple UEs. A UE may communicate with a network node via downlink communications and uplink communications. A "downlink" (or "DL") refers to a communication link from a network node to a UE, and an "uplink" (or "UL") refers to a communication link from a UE to a network node. Some wireless networks may support device-to-device communications, such as via a local link (e.g., a side link (SL), a wireless local area network (WLAN) link, and / or a wireless personal area network (WPAN) link, etc.).

[0004] The above-mentioned multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different UEs to communicate at a city, country, region and / or global level. New Radio (NR) (which may be referred to as 5G) is a set of enhancements to the LTE mobile standard promulgated by 3GPP. NR is designed to better support mobile broadband Internet access by: improving spectrum efficiency; reducing costs; improving services; utilizing new spectrum; and using orthogonal frequency division multiplexing (OFDM) (CP-OFDM) with cyclic prefix (CP) on the downlink, CP-OFDM and / or single carrier frequency division multiplexing (SC-FDM) (also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink to better integrate with other open standards; and supporting beamforming, multiple input multiple output (MIMO) antenna technology and carrier aggregation. As the demand for mobile broadband access continues to increase, further improvements in LTE, NR and other radio access technologies remain useful. Summary of the invention

[0005] In some specific implementations, an apparatus for wireless communication at a user equipment (UE) includes a memory and one or more processors coupled to the memory. The one or more processors may be configured to send a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy. The one or more processors may be configured to receive a request to perform a beam prediction task consistent with the capability report. The one or more processors may be configured to send a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0006] In some specific implementations, an apparatus for wireless communication at a network node includes a memory and one or more processors coupled to the memory. The one or more processors may be configured to receive a capability report associated with a beam prediction capability of a UE, the capability report indicating a target beam prediction accuracy. The one or more processors may be configured to send a request to perform a beam prediction task consistent with the capability report. The one or more processors may be configured to receive a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0007] In some implementations, a method of wireless communication performed at an apparatus of a UE includes sending a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy. The method includes receiving a request to perform a beam prediction task consistent with the capability report. The method includes sending a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0008] In some implementations, a method of wireless communication performed at an apparatus of a network node includes receiving a capability report associated with a beam prediction capability of a UE, the capability report indicating a target beam prediction accuracy. The method includes sending a request to perform a beam prediction task consistent with the capability report. The method includes receiving a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0009] In some specific implementations, a non-transitory computer-readable medium storing an instruction set for wireless communication includes one or more instructions that, when executed by one or more processors of a UE, cause the UE to: send a capability report associated with the beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy. The one or more instructions, when executed by one or more processors of the UE, cause the UE to receive a request to perform a beam prediction task consistent with the capability report. The one or more instructions, when executed by one or more processors of the UE, cause the UE to send a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0010] In some specific implementations, a non-transitory computer-readable medium storing an instruction set for wireless communication includes one or more instructions that, when executed by one or more processors of a network node, cause the network node to receive a capability report associated with a beam prediction capability of a UE, the capability report indicating a target beam prediction accuracy. The one or more instructions, when executed by one or more processors of the network node, cause the network node to send a request to perform a beam prediction task consistent with the capability report. The one or more instructions, when executed by one or more processors of the network node, cause the network node to receive a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report, based at least in part on the request.

[0011] In some implementations, an apparatus for wireless communication includes a component for sending a capability report associated with a beam prediction capability of the apparatus, the capability report indicating a target beam prediction accuracy. The apparatus includes a component for receiving a request to perform a beam prediction task consistent with the capability report. The apparatus includes a component for sending a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0012] In some implementations, an apparatus for wireless communication includes a component for receiving a capability report associated with a beam prediction capability of a UE, the capability report indicating a target beam prediction accuracy. The apparatus includes a component for sending a request to perform a beam prediction task consistent with the capability report. The apparatus includes a component for receiving a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0013] The various aspects generally include methods, apparatuses, systems, computer program products, non-transitory computer-readable media, user equipment, base stations, network entities, network nodes, wireless communication devices and / or processing systems as fully described with reference to the accompanying drawings and the specification and as illustrated in the accompanying drawings and the specification.

[0014] The features and technical advantages of the examples according to the present disclosure have been outlined quite extensively above so that the following specific embodiments may be better understood. Additional features and advantages will be described below. The disclosed concepts and specific examples may be easily used as a basis for modifying or designing other structures for achieving the same purpose of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. When considered in conjunction with the accompanying drawings, the characteristics of the concepts disclosed herein (both their organization and methods of operation) and the associated advantages will be better understood according to the following description. Each of the drawings provided is for illustration and description purposes, and not as a definition of the limitations of the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to be able to understand the above-mentioned features of the present disclosure in detail, a more specific description briefly summarized above may be obtained by reference to various aspects (some of which are illustrated in the accompanying drawings). However, it should be noted that the accompanying drawings illustrate only certain typical aspects of the present disclosure and are therefore not to be considered as limiting the scope thereof, as the specification may admit of other equally effective aspects. The same reference numerals in different drawings may identify the same or similar elements.

[0016] Figure 1 is a diagram illustrating an example of a wireless network according to the present disclosure.

[0017] Figure 2 is a diagram illustrating an example of communication between a network node and a user equipment (UE) in a wireless network according to the present disclosure.

[0018] Figure 3 is a diagram illustrating an example decomposed base station architecture according to the present disclosure.

[0019] Figure 4 is a diagram illustrating an example of beam management according to the present disclosure.

[0020] Figure 5 is a diagram illustrating an example of predictive beam management based on artificial intelligence / machine learning (AI / ML) according to the present disclosure.

[0021] Figure 6 is a diagram illustrating an example of AI / ML model complexity according to the present disclosure.

[0022] Figure 7 , Fig. 8A , Figure 8B , Fig. 9A , Fig. 9B , Fig. 9C and Fig.10 is a diagram illustrating an example associated with sending a capability report indicating a beam prediction capability of a UE according to the present disclosure.

[0023] Figure 11 to Figure 12is a diagram illustrating an example process associated with sending a capability report indicating a beam prediction capability of a UE according to the present disclosure.

[0024] Fig.13 is a diagram of an example apparatus for wireless communications according to the present disclosure.

[0025] Fig.14 is a diagram illustrating an example of a hardware implementation for an apparatus employing a processing system according to the present disclosure.

[0026] Fig.15 is a diagram illustrating an example implementation of code and circuits for an apparatus according to the present disclosure.

[0027] Fig.16 is a diagram of an example apparatus for wireless communications according to the present disclosure.

[0028] Fig.17 is a diagram illustrating an example of a hardware implementation for an apparatus employing a processing system according to the present disclosure.

[0029] Fig.18 is a diagram illustrating an example implementation of code and circuits for an apparatus according to the present disclosure. DETAILED DESCRIPTION

[0030] A network node may schedule a user equipment (UE) to perform a beam prediction task. The beam prediction task may involve predicting the best serving beam based on past beam measurements from beam measurement results, which may reduce signaling associated with measurement reports. The serving beam that may be predicted using the beam prediction task may be associated with a receive (Rx) beam at the UE, a transmit (Tx) beam at the UE, an Rx beam at the network node, and / or a Tx beam at the network node. The UE may have certain capabilities in beam prediction, and the capabilities may vary between UEs. For example, the UE may share hardware for beam prediction with other tasks, and when another task is running, the UE may have fewer hardware resources allocated for beam prediction.

[0031] When scheduling a UE to perform a beam prediction task, the network node may not be aware of the complexity level of the artificial intelligence (AI) / machine learning (ML) model that can be run by the UE. The network node may unknowingly schedule the UE to perform a beam prediction task that is incompatible with the capabilities of the UE. The UE may attempt to perform a beam prediction task based at least in part on the scheduling of the network node, but the UE may encounter various complex situations. For example, when attempting to perform a beam prediction task, the UE may become overloaded, which may cause the UE to fail or prompt the UE to restart. The UE may attempt to perform a beam prediction task when another high-priority application is running, and the UE may not have sufficient hardware resources to perform the beam prediction task at this time. The UE can perform the beam prediction task, but the limited capabilities of the UE may result in a relatively high probability of beam prediction inaccuracy.

[0032] In some aspects, the UE may indicate a capability report to the network node, which may assist the network node when scheduling beam prediction tasks for the UE. The capability report may indicate various capabilities of the UE in terms of beam prediction. The capability report may indicate a target beam prediction accuracy (e.g., a required beam prediction accuracy or a beam prediction accuracy threshold). According to one or more examples, the capability report may indicate the level of beam prediction accuracy supported by the UE. The capability report may indicate the type of reference signal resources that can be used as measurement resources for beam prediction. According to one or more examples, the UE may indicate the type of reference signal resources that can be configured or indicated by the network node as measurement resources for beam prediction. The capability report may indicate the type of reference signal resources and / or beams that can be used as beam prediction targets for beam prediction. For example, the UE may indicate the type of beams that can be predicted and / or reported by the UE.

[0033] In some aspects, the network node may receive a capability report from the UE. The network node may determine a beam prediction task to be performed by the UE, wherein the beam prediction task may depend on the capabilities of the UE. The network node may ensure that the beam prediction task is consistent with the capability report. According to one or more examples, the network node may refrain from assigning a beam prediction task to the UE that the UE is not capable of performing. The UE may receive a request from the network node to perform a beam prediction task, which may be consistent with the capability report. When consistent with the capability report, the beam prediction task may correspond to a beam prediction task that the UE is able to perform taking into account the capabilities of the UE. On the other hand, when the beam prediction task is not consistent with the capability report, the UE may not be able to perform the beam prediction task taking into account the capabilities of the UE. The UE may perform the beam prediction task and generate a beam prediction result. The beam prediction result may meet the target beam prediction accuracy indicated in the capability report. The UE may send the beam prediction result to the network node.

[0034] In some aspects, since the network node may assign beam prediction tasks to the UE based at least in part on the capabilities of the UE, the UE may be able to perform the beam prediction tasks without failure, thereby improving the performance of the UE. The UE may be scheduled to perform beam prediction tasks that the UE is capable of performing, and the UE may not be scheduled to perform beam prediction tasks that the UE is not capable of performing. The UE may use hardware resources available for beam prediction to perform the beam prediction tasks, and the UE may not attempt to use hardware resources associated with high priority applications (e.g., applications with higher priority than the beam prediction tasks). For example, the UE may avoid or suppress the use of hardware resources associated with high priority applications. In addition, the accuracy associated with the beam prediction results may be improved because the UE may be assigned the task of performing the beam prediction tasks that the UE is capable of performing. When assigning the beam prediction tasks to the UE, the network node may use the information indicated by the UE. Otherwise, when the network node does not have the information indicated by the UE, the UE may be assigned the task of performing the beam prediction tasks that the UE cannot accurately perform, thereby reducing the performance of the UE.

[0035] The various aspects of the present disclosure are described more fully below with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms, and should not be construed as being limited to any specific structure or function presented throughout the present disclosure. Instead, these aspects are provided so that the present disclosure will be thorough and complete, and the scope of the present disclosure will be fully conveyed to those skilled in the art. It should be understood by those skilled in the art that the scope of the present disclosure is intended to cover any aspect of the disclosure disclosed herein, whether it is independently or in combination with any other aspect of the disclosure. For example, any number of aspects set forth herein may be used to implement a device or practice method. In addition, the scope of the present disclosure is intended to cover such a device or method practiced using other structures, functionality, or structure and functionality in addition to or different from the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of the present invention.

[0036] Several aspects of telecommunication systems will now be presented with reference to various devices and techniques. These devices and techniques will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, etc. (collectively referred to as "elements"). These elements may be implemented using hardware, software, or a combination thereof. Whether such elements are implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system.

[0037] Although various aspects may be described herein using terms generally associated with 5G or new radio (NR) radio access technology (RAT), various aspects of the present disclosure may be applicable to other RATs, such as 3G RAT, 4G RAT and / or RATs beyond 5G (e.g., 6G).

[0038] Figure 1 1 is a diagram illustrating an example of a wireless network 100 according to the present disclosure. The wireless network 100 may be a 5G (e.g., NR) network and / or a 4G (e.g., Long Term Evolution (LTE)) network, or may include elements of a 5G (e.g., NR) network and / or elements of a 4G (e.g., Long Term Evolution (LTE)) network, etc. The wireless network 100 may include one or more network nodes 110 (shown as network node 110a, network node 110b, network node 110c, and network node 110d), user equipment (UE) 120 or multiple UEs 120 (shown as UE 120a, UE 120b, UE 120c, UE 120d, and UE 120e), and / or other entities. The network node 110 is a network node that communicates with the UE 120. As shown in the figure, the network node 110 may include one or more network nodes. For example, the network node 110 may be a converged network node, which means that the converged network node is configured to utilize a radio protocol stack that is physically or logically integrated within a single radio access network (RAN) node (e.g., within a single device or unit). As another example, the network node 110 may be a decomposed network node (sometimes referred to as a decomposed base station), which means that the network node 110 is configured to utilize a protocol stack that is physically or logically distributed between two or more nodes (such as one or more central units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)).

[0039] In some examples, the network node 110 is or includes a network node that communicates with the UE 120 via a radio access link, such as an RU. In some examples, the network node 110 is or includes a network node that communicates with other network nodes 110 via a fronthaul link or a midhaul link, such as a DU. In some examples, the network node 110 is or includes a network node that communicates with other network nodes 110 via a midhaul link or communicates with the core network via a backhaul link, such as a CU. In some examples, the network node 110 (such as an aggregated network node 110 or a decomposed network node 110) may include multiple network nodes, such as one or more RUs, one or more CUs, and / or one or more DUs. The network node 110 may include, for example, an NR base station, an LTE base station, a Node B, an eNB (e.g., in 4G), a gNB (e.g., in 5G), an access point, a transmit receive point (TRP), a DU, a RU, a CU, a mobility element of a network, a core network node, a network element, a network equipment, a RAN node, or a combination thereof. In some examples, network nodes 110 may be interconnected to each other or to one or more other network nodes 110 in wireless network 100 via various types of fronthaul, midhaul, and / or backhaul interfaces, such as direct physical connections, air interfaces, or virtual networks, using any suitable transport network.

[0040] In some examples, the network node 110 may provide communication coverage for a particular geographic area. In the Third Generation Partnership Project (3GPP), the term "cell" may refer to the coverage area of ​​the network node 110 and / or the network node subsystem serving the coverage area, depending on the context in which the term is used. The network node 110 may provide communication coverage for a macro cell, a pico cell, a femto cell, and / or another type of cell. A macro cell may cover a relatively large geographic area (e.g., a radius of several kilometers) and may allow unrestricted access by a UE 120 with a service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by a UE 120 with a service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by a UE 120 associated with the femto cell (e.g., a UE 120 in a closed subscriber group (CSG)). A network node 110 for a macro cell may be referred to as a macro network node. A network node 110 for a pico cell may be referred to as a pico network node. The network node 110 for a femto cell may be referred to as a femto network node or a home network node. Figure 1In the example shown in , network node 110a may be a macro network node for macro cell 102a, network node 110b may be a pico network node for pico cell 102b, and network node 110c may be a femto network node for femto cell 102c. A network node may support one or more (e.g., three) cells. In some examples, a cell may not necessarily be stationary, and the geographic area of ​​a cell may move depending on the location of a mobile network node 110 (e.g., a mobile network node).

[0041] In some aspects, the term "base station" or "network node" may refer to an aggregated base station, a decomposed base station, an integrated access and backhaul (IAB) node, a relay node, or one or more components thereof. For example, in some aspects, a "base station" or "network node" may refer to a CU, a DU, a RU, a near real-time (near RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC, or a combination thereof. In some aspects, the term "base station" or "network node" may refer to a device configured to perform one or more functions (such as those described herein in conjunction with network node 110). In some aspects, the term "base station" or "network node" may refer to multiple devices configured to perform one or more functions. For example, in some distributed systems, each of a plurality of different devices (which may be located in the same geographic location or in different geographic locations) may be configured to perform at least a portion of a function, or to repeatedly perform at least a portion of the function, and the term "base station" or "network node" may refer to any one or more of these different devices. In some aspects, the term "base station" or "network node" may refer to one or more virtual base stations or one or more virtual base station functions. For example, in some aspects, two or more base station functions may be instantiated on a single device. In some aspects, the term "base station" or "network node" may refer to one of the base station functions, but not another base station function. In this way, a single device may include more than one base station.

[0042] The wireless network 100 may include one or more relay stations. A relay station is a network node that can receive transmissions of data from an upstream node (e.g., a network node 110 or a UE 120) and transmit transmissions of data to a downstream node (e.g., a UE 120 or a network node 110). A relay station may be a UE 120 that is capable of relaying transmissions for other UEs 120. Figure 1 In the example shown in , a network node 110d (e.g., a relay network node) may communicate with a network node 110a (e.g., a macro network node) and a UE 120d to facilitate communication between the network node 110a and the UE 120d. A network node 110 that relays communications may be referred to as a relay station, a relay base station, a relay network node, a relay node, a relay, etc.

[0043] The wireless network 100 may be a heterogeneous network that includes different types of network nodes 110, such as macro network nodes, pico network nodes, femto network nodes, relay network nodes, etc. These different types of network nodes 110 may have different transmit power levels, different coverage areas, and / or different impacts on interference in the wireless network 100. For example, a macro network node may have a high transmit power level (e.g., 5 watts to 40 watts), while a pico network node, a femto network node, and a relay network node may have a lower transmit power level (e.g., 0.1 watt to 2 watts).

[0044] The network controller 130 may be coupled to or in communication with a set of network nodes 110 and may provide coordination and control for the network nodes 110. The network controller 130 may communicate with the network nodes 110 via a backhaul communication link or a midhaul communication link. The network nodes 110 may also communicate directly with each other or indirectly via a wireless or wired backhaul communication link. In some aspects, the network controller 130 may be, or may include, a CU or a core network device.

[0045] UE 120 may be dispersed throughout the wireless network 100, and each UE 120 may be stationary or mobile. UE 120 may include, for example, an access terminal, a terminal, a mobile station, and / or a subscriber unit. UE 120 may be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet computer, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device, a biometric device, a wearable device (e.g., a smart watch, smart clothing, smart glasses, a smart wristband, smart jewelry (e.g., a smart ring or a smart bracelet)), an entertainment device (e.g., a music device, a video device, and / or a satellite radio), a vehicle component or sensor, a smart meter / sensor, an industrial manufacturing equipment, a global positioning system device, a UE function of a network node, and / or any other suitable device configured to communicate via a wireless or wired medium.

[0046] Some UEs 120 may be considered as machine type communication (MTC) or evolved or enhanced machine type communication (eMTC) UEs. MTC UEs and / or eMTC UEs may include, for example, robots, drones, remote devices, sensors, meters, monitors, and / or location tags that may communicate with a network node, another device (e.g., a remote device), or some other entity. Some UEs 120 may be considered as Internet of Things (IoT) devices and / or may be implemented as NB-IoT (narrowband IoT) devices. Some UEs 120 may be considered as customer premises equipment. UE 120 may be included inside a housing that houses components of UE 120, such as a processor component and / or a memory component. In some examples, the processor component and the memory component may be coupled together. For example, a processor component (e.g., one or more processors) and a memory component (e.g., a memory) may be operably coupled, communicatively coupled, electronically coupled, and / or electrically coupled.

[0047] In general, any number of wireless networks 100 may be deployed in a given geographic area. Each wireless network 100 may support a specific RAT and may operate on one or more frequencies. RAT may be referred to as a radio technology, air interface, etc. Frequency may be referred to as a carrier, frequency channel, etc. Each frequency may support a single RAT in a given geographic area to avoid interference between wireless networks of different RATs. In one example, a NR or 5G RAT network may be deployed.

[0048] In some examples, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using network node 110 as an intermediary to communicate with each other). For example, UE 120 may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocols (e.g., which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, or vehicle-to-pedestrian (V2P) protocols), and / or mesh networks. In such examples, UE 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by network node 110.

[0049] The electromagnetic spectrum is typically subdivided by frequency / wavelength into various categories, bands, channels, etc. In 5G NR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz). It should be understood that, although a portion of FR1 is greater than 6 GHz, FR1 is often (interchangeably) referred to as the "sub-6 GHz" band in various documents and articles. A similar naming issue sometimes occurs with respect to FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although different from the extremely high frequency (EHF) band (30 GHz-300 GHz) identified as the "millimeter wave" band by the International Telecommunication Union (ITU).

[0050] Frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR research has identified the operating bands for these mid-band frequencies as frequency range designation FR3 (7.125GHz-24.25GHz). The bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, so the features of FR1 and / or FR2 can be effectively extended to mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operations to more than 52.6GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6GHz-71GHz), FR4 (52.6GHz-114.25GHz) and FR5 (114.25GHz-300GHz). Each of these higher frequency bands falls within the EHF band.

[0051] Considering the above examples, unless otherwise specifically stated, it should be understood that if the term "below 6 GHz" or the like is used herein, the term may broadly refer to frequencies that may be lower than 6 GHz, may be within FR1, or may include mid-band frequencies. In addition, unless otherwise specifically stated, it should be understood that if the term "millimeter wave" or the like is used herein, the term may broadly refer to frequencies that may include mid-band frequencies, may be within FR2, FR4, FR4-a or FR4-1, and / or FR5, or may be within the EHF band. It is contemplated that the frequencies included in these operating bands (e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and / or FR5) may be modified, and the techniques described herein are applicable to those modified frequency ranges.

[0052] In some aspects, a UE (e.g., UE 120) may include a communication manager 140. As described in more detail elsewhere herein, the communication manager 140 may send a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy; receive a request to perform a beam prediction task consistent with the capability report; and send a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request. Additionally or alternatively, the communication manager 140 may perform one or more other operations described herein.

[0053] In some aspects, a network node (e.g., network node 110) may include a communication manager 150. As described in more detail elsewhere herein, the communication manager 150 may receive a capability report associated with a beam prediction capability of a UE, the capability report indicating a target beam prediction accuracy; send a request to perform a beam prediction task consistent with the capability report; and receive a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request. Additionally or alternatively, the communication manager 150 may perform one or more other operations described herein.

[0054] As indicated above, Figure 1 are provided as examples. Other examples can be found in the Figure 1 The examples described are different.

[0055] Figure 2 2 is a diagram illustrating an example 200 of a network node 110 communicating with a UE 120 in a wireless network 100 according to the present disclosure. The network node 110 may be equipped with a set of antennas 234a to 234t, such as T antennas (T≥1). The UE 120 may be equipped with a set of antennas 252a to 252r, such as R antennas (R≥1). The network node 110 of example 200 includes one or more radio frequency components, such as an antenna 234 and a modem 254. In some examples, the network node 110 may include an interface, a communication component, or another component that facilitates communication with the UE 120 or another network node. Some network nodes 110 may not include a radio frequency component that facilitates direct communication with the UE 120, such as one or more CUs or one or more DUs.

[0056] At the network node 110, a transmit processor 220 may receive data intended for a UE 120 (or a set of UEs 120) from a data source 212. The transmit processor 220 may select one or more modulation and coding schemes (MCS) for the UE 120 based at least in part on one or more channel quality indicators (CQIs) received from the UE 120. The network node 110 may process (e.g., encode and modulate) the data for the UE 120 based at least in part on the MCS selected for the UE 120, and may provide data symbols for the UE 120. The transmit processor 220 may process system information (e.g., for semi-static resource allocation information (SRPI)) and control information (e.g., CQI requests, grants, and / or upper layer signaling), and provide overhead symbols and control symbols. The transmit processor 220 may generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS) or demodulation reference signals (DMRS)) and synchronization signals (e.g., primary synchronization signals (PSS) or secondary synchronization signals (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, where applicable, and may provide a set of output symbol streams (e.g., T output symbol streams) to a corresponding set of modems 232 (e.g., T modems) (shown as modems 232a to 232t). For example, each output symbol stream may be provided to a modulator component (shown as MOD) of the modem 232. Each modem 232 may process a corresponding output symbol stream (e.g., for OFDM) using a corresponding modulator component to obtain an output sample stream. Each modem 232 may also process (e.g., convert to analog, amplify, filter, and / or upconvert) the output sample stream using a corresponding modulator component to obtain a downlink signal. The modems 232a through 232t may transmit a set of downlink signals (eg, T downlink signals) via a corresponding set of antennas 234 (eg, T antennas) (shown as antennas 234a through 234t).

[0057] At the UE 120, a set of antennas 252 (shown as antennas 252a to 252r) may receive downlink signals from the network node 110 and / or other network nodes 110 and may provide a set of received signals (e.g., R received signals) to a set of modems 254 (e.g., R modems) (shown as modems 254a to 254r). For example, each received signal may be provided to a demodulator component (shown as DEMOD) of the modem 254. Each modem 254 may use a corresponding demodulator component to condition (e.g., filter, amplify, downconvert, and / or digitize) the received signal to obtain input samples. Each modem 254 may use a demodulator component to further process the input samples (e.g., for OFDM) to obtain received symbols. A MIMO detector 256 may obtain received symbols from the modem 254, may perform MIMO detection on the received symbols where applicable, and may provide detected symbols. The receive processor 258 may process (e.g., demodulate and decode) the detected symbols, may provide decoded data for the UE 120 to the data sink 260, and may provide decoded control information and system information to the controller / processor 280. The term "controller / processor" may refer to one or more controllers, one or more processors, or a combination thereof. The channel processor may determine a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, and / or a CQI parameter, among other things. In some examples, one or more components of the UE 120 may be included in the housing 284.

[0058] The network controller 130 may include a communication unit 294, a controller / processor 290, and a memory 292. The network controller 130 may include, for example, one or more devices in a core network. The network controller 130 may communicate with the network node 110 via the communication unit 294.

[0059] One or more antennas (e.g., antennas 234a to 234t and / or antennas 252a to 252r) may include or be included within one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, and / or one or more antenna arrays, etc. Antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include one or more antenna elements (in a single housing or multiple housings), sets of coplanar antenna elements, sets of non-coplanar antenna elements, and / or may be coupled to one or more transmit and / or receive components (such as, Figure 2 One or more antenna elements of one or more components in.

[0060] On the uplink, at the UE 120, the transmit processor 264 may receive and process data from the data source 262 and control information from the controller / processor 280 (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI). The transmit processor 264 may generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be pre-decoded by the Tx MIMO processor 266, where applicable, further processed by the modem 254 (e.g., for DFT-s-OFDM or CP-OFDM), and sent to the network node 110. In some examples, the modem 254 of the UE 120 may include a modulator and a demodulator. In some examples, the UE 120 includes a transceiver. The transceiver may include any combination of an antenna 252, a modem 254, a MIMO detector 256, a receive processor 258, a transmit processor 264, and / or a Tx MIMO processor 266. The transceiver may be used by a processor (eg, controller / processor 280) and memory 282 to perform aspects of any of the methods described herein.

[0061] At the network node 110, uplink signals from the UE 120 and / or other UEs may be received by the antenna 234, processed by the modem 232 (e.g., a demodulator component (shown as DEMOD) of the modem 232), detected by the MIMO detector 236 (where applicable), and further processed by the receive processor 238 to obtain decoded data and control information transmitted by the UE 120. The receive processor 238 may provide the decoded data to the data sink 239 and the decoded control information to the controller / processor 240. The network node 110 may include a communication unit 244 and may communicate with the network controller 130 via the communication unit 244. The network node 110 may include a scheduler 246 to schedule one or more UEs 120 for downlink communication and / or uplink communication. In some examples, the modem 232 of the network node 110 may include a modulator and a demodulator. In some examples, the network node 110 includes a transceiver. The transceiver may include any combination of an antenna 234, a modem 232, a MIMO detector 236, a receive processor 238, a transmit processor 220, and / or a Tx MIMO processor 230. The transceiver may be used by a processor (e.g., controller / processor 240) and memory 242 to perform aspects of any of the methods described herein.

[0062] The controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or Figure 2Any other component in the network node 110 may perform one or more techniques associated with sending a capability report indicating the beam prediction capability of the UE, as described in more detail elsewhere herein. For example, the controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other component of the Fig.11 Process 1100, Fig.12 1200 and / or operations of other processes as described herein. Memory 242 and memory 282 may store data and program codes for network node 110 and UE 120, respectively. In some examples, memory 242 and / or memory 282 may include a non-transitory computer-readable medium storing one or more instructions (e.g., code and / or program code) for wireless communication. For example, the one or more instructions, when executed by one or more processors of network node 110 and / or UE 120 (e.g., directly executed, or executed after compilation, conversion, and / or interpretation), may cause one or more processors, UE 120, and / or network node 110 to perform or direct, for example, Fig.11 Process 1100, Fig.12 The process 1200 and / or operations of other processes as described herein. In some examples, executing instructions may include running instructions, converting instructions, compiling instructions, and / or interpreting instructions, etc.

[0063] In some aspects, a UE (e.g., UE 120) includes a component for sending a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy; a component for receiving a request to perform a beam prediction task consistent with the capability report; and / or a component for sending a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request. The components for the UE to perform the operations described herein may include, for example, one or more of the communication manager 140, the antenna 252, the modem 254, the MIMO detector 256, the receive processor 258, the transmit processor 264, the Tx MIMO processor 266, the controller / processor 280, or the memory 282.

[0064] In some aspects, a network node (e.g., network node 110) includes a component for receiving a capability report associated with a beam prediction capability of a UE, the capability report indicating a target beam prediction accuracy; a component for sending a request to perform a beam prediction task consistent with the capability report; and / or a component for receiving a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request. The components for the network node to perform the operations described herein may include, for example, one or more of the communication manager 150, the transmit processor 220, the Tx MIMO processor 230, the modem 232, the antenna 234, the MIMO detector 236, the receive processor 238, the controller / processor 240, the memory 242, or the scheduler 246.

[0065] Although Figure 2 The blocks in the 2000 and 2001 are illustrated as distinct components, but the functionality described above for these blocks may be implemented in a single hardware, software, or combined component or in various combinations of components. For example, the functionality described for the transmit processor 264, the receive processor 258, and / or the Tx MIMO processor 266 may be performed by or under the control of the controller / processor 280.

[0066] As indicated above, Figure 2 are provided as examples. Other examples can be found in the Figure 2 The examples described are different.

[0067] The deployment of a communication system (such as a 5G NR system) can be arranged with various components or components in a variety of ways. In a 5G NR system or network, a network node, a network entity, a mobility element of a network, a RAN node, a core network node, a network element, a base station or network equipment can be implemented in an aggregated or decomposed architecture. For example, a base station (such as a node B (NB), an evolved NB (eNB), an NR BS, a 5G NB, an access point (AP), a TRP or a cell, etc.) or one or more units (or one or more components) that perform base station functionality can be implemented as an aggregated base station (also referred to as an independent base station or a monolithic base station) or a decomposed base station. "Network entity" or "network node" may refer to a decomposed base station or one or more units of a decomposed base station (such as one or more CUs, one or more DUs, one or more RUs, or a combination thereof).

[0068] An aggregated base station (e.g., an aggregated network node) may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node (e.g., within a single device or unit). A decomposed base station (e.g., a decomposed network node) may be configured to utilize a protocol stack that is physically or logically distributed between two or more units (such as one or more CUs, one or more DUs, or one or more RUs). In some examples, a CU may be implemented within a network node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed across one or more other network nodes. A DU may be implemented to communicate with one or more RUs. Each of a CU, a DU, and a RU may also be implemented as a virtual unit, such as a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU), and the like.

[0069] Base station type operations or network designs may take into account the aggregated nature of base station functionality. For example, a decomposed base station may be utilized in an IAB network, an open radio access network (O-RAN (such as a network configuration initiated by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)) to facilitate the scaling of a communication system by separating base station functionality into one or more units that can be deployed separately. A decomposed base station may include functionality implemented across two or more units at various physical locations, as well as functionality implemented virtually for at least one unit, which may enable flexibility in network design. Individual units of a decomposed base station may be configured for wired or wireless communication with at least one other unit of the decomposed base station.

[0070] Figure 3 3 is a diagram illustrating an example disaggregated base station architecture 300 according to the present disclosure. The disaggregated base station architecture 300 may include a CU 310 that may communicate directly with a core network 320 via a backhaul link, or indirectly with the core network 320 through one or more disaggregated control units, such as a near-RT RIC 325 via an E2 link, or a non-RT RIC 315 associated with a service management and orchestration (SMO) framework 305, or both. The CU 310 may communicate with one or more DUs 330 via respective midhaul links, such as via an F1 interface. Each of the DUs 330 may communicate with one or more RUs 340 via respective fronthaul links. Each of the RUs 340 may communicate with one or more UEs 120 via respective radio frequency (RF) access links. In some implementations, a UE 120 may be served simultaneously by multiple RUs 340.

[0071] Each of the units (including CU 310, DU 330, RU 340) and the near-RT RIC 325, non-RT RIC 315, and SMO framework 305 may include or be coupled to one or more interfaces, the one or more interfaces being configured to receive or send signals, data, or information (collectively referred to as signals) via a wired or wireless transmission medium. Each of the units or an associated processor or controller that provides instructions to one or more communication interfaces of the corresponding unit may be configured to communicate with one or more of the other units via a transmission medium. In some examples, each of the units may include a wired interface and a wireless interface, the wired interface being configured to receive signals or send signals to one or more of the other units via a wired transmission medium, the wireless interface being configured to receive signals or send signals to one or more of the other units via a wired transmission medium, or both.

[0072] In some aspects, the CU 310 may host one or more higher layer control functions. Such control functions may include radio resource control (RRC) functions, packet data convergence protocol (PDCP) functions, or service data adaptation protocol (SDAP) functions, etc. Each control function may be implemented using an interface that is configured to communicate signals with other control functions hosted by the CU 310. The CU 310 may be configured to handle user plane functionality (e.g., central unit-user plane (CU-UP) functionality), control plane functionality (e.g., central unit-control plane (CU-CP) functionality), or a combination thereof. In some specific implementations, the CU 310 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (such as an E1 interface). As needed, the CU 310 may be implemented to communicate with the DU 330 for network control and signaling.

[0073] Each DU 330 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RUs 340. In some aspects, the DU 330 may host one or more of a radio link control (RLC) layer, a MAC layer, and one or more high physical (PHY) layers, at least in part, according to a functional partition such as that defined by 3GPP. In some aspects, one or more high PHY layers may be implemented by one or more modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc. In some aspects, the DU 330 may further host one or more low PHY layers, such as one or more modules for fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming or physical random access channel (PRACH) extraction and filtering, etc. Each layer (which may also be referred to as a module) may be implemented using an interface that is configured to communicate signals with other layers (and modules) hosted by the DU 330 or with control functions hosted by the CU 310.

[0074] Each RU 340 may implement lower layer functionality. In some deployments, the RU 340 controlled by the DU 330 may correspond to a logical node that hosts RF processing functions or low PHY layer functions based on functional split (e.g., functional split defined by 3GPP) (such as lower layer functional split), such as performing FFT, performing iFFT, digital beamforming, or PRACH extraction and filtering, etc. In this architecture, each RU 340 may be operated to handle over-the-air (OTA) communications with one or more UEs 120. In some specific implementations, real-time and non-real-time aspects of control plane and user plane communications with the RU 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration may enable each DU 330 and CU 310 to be implemented in a cloud-based RAN architecture (such as a vRAN architecture).

[0075] The SMO framework 305 may be configured to support RAN deployment and provisioning of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 305 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO framework 305 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) platform 390) to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements may include, but are not limited to, CU 310, DU 330, RU 340, non-RT RIC 315, and near-RTRIC 325. In some specific implementations, the SMO framework 305 may communicate with hardware aspects of the 4G RAN (such as an open eNB (O-eNB) 311) via the O1 interface. Additionally, in some implementations, the SMO framework 305 can communicate directly with each of the one or more RUs 340 via a corresponding O1 interface. The SMO framework 305 can also include a non-RT RIC 315 configured to support the functionality of the SMO framework 305.

[0076] The non-RT RIC 315 may be configured to include logic functions that enable non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updating, or policy-based guidance of applications / features in the near-RT RIC 325. The non-RT RIC 315 may be coupled to or communicate with the near-RT RIC 325 (such as via an A1 interface). The near-RT RIC 325 may be configured to include logic functions that enable near-real-time control and optimization of RAN elements and resources via data collection and actions through an interface (such as via an E2 interface) that connects one or more CUs 310, one or more DUs 330, or both, and the O-eNB with the near-RT RIC 325.

[0077] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 325, the non-RT RIC 315 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 325 and may be received from a non-network data source or from a network function at the SMO framework 305 or the non-RT RIC 315. In some examples, the non-RT RIC 315 or the near-RT RIC 325 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 315 may monitor long-term trends and patterns of performance and employ AI / ML models to perform corrective actions through the SMO framework 305 (such as via reconfiguration of the O1 interface) or via the creation of RAN management policies (such as A1 interface policies).

[0078] As indicated above, Figure 3 are provided as examples. Other examples can be found in the Figure 3 The examples described are different.

[0079] Figure 4 is a diagram illustrating an example 400 of beam management according to the present disclosure.

[0080] As shown in reference numeral 402, the UE may initially be in an RRC idle state or an RRC inactive state. As shown in reference numeral 404, the UE may perform initial access. As shown in reference numeral 406, the UE may perform beam management after entering the RRC connected state. Beam management may include P1, P2 and / or P3 beam management processes. The P1 beam management process may be a beam selection process, an initial beam acquisition process, a beam scanning process, a cell search process and / or a beam search process. The P2 beam management process may be a beam refinement process, a network node beam refinement process, a TRP beam refinement process and / or a transmit beam refinement process. The P3 beam management process may be a beam refinement process, a UE beam refinement process and / or a receive beam refinement process. As shown in reference numeral 408, the UE may also perform beam management using an AI / ML-based method. Beam management using an AI / ML-based approach may use AI / ML models in the spatial, time, and / or frequency domains, which may reduce signaling overhead and latency and improve beam selection accuracy. The AI / ML model may be associated with lifecycle management, which may involve model training, model deployment, model inference, model monitoring, and / or model updates. As shown in reference numeral 410, the UE may perform beam failure detection (BFD), which may be based at least in part on measurements obtained during beam management after entering RRC connected mode. As shown in reference numeral 412, the UE may perform beam failure recovery (BFR) based at least in part on BFD. As shown in reference numeral 414, when BFR is unsuccessful, the UE may declare a radio link failure (RLF).

[0081] As indicated above, Figure 4 are provided as examples. Other examples can be found in the Figure 4 The examples described are different.

[0082] Figure 5 is a diagram illustrating an example 500 of AI / ML-based predictive beam management in accordance with the present disclosure.

[0083] As shown in reference numeral 502, in AI / ML-based predictive beam management, a network node may send a plurality of first channel state information reference signals (CSI-RS) or synchronization signal blocks (SSBs) at a first time. The first CSI-RS / SSB may be associated with a first channel measurement resource (CMR). The UE may perform a first layer 1 RSRP (L1-RSRP) and / or signal to interference plus noise ratio (SINR) measurement based at least in part on the plurality of first CSI-RS / SSBs. The UE may report the first L1-RSRP / SINR measurement to the network node. As shown in reference numeral 504, the network node may send a plurality of second CSI-RS / SSBs at a second time. The second CSI-RS / SSB may be associated with a second CMR. The UE may perform a second L1-RSRP / SINR measurement based at least in part on the plurality of second CSI-RS / SSBs. The UE may report the second L1-RSRP / SINR measurement to the network node. As shown in reference numeral 506, the network node may send a plurality of third CSI-RS / SSBs at a third time. The third CSI-RS / SSB may be associated with a third CMR. The UE may perform a third L1-RSRP / SINR measurement based at least in part on the plurality of third CSI-RS / SSBs. The UE may report the third L1-RSRP / SINR measurement to the network node.

[0084] As shown by reference numeral 508, a time series of L1-RSRP / SINR measurements (e.g., a first L1-RSRP / SINR measurement, a second L1-RSRP / SINR measurement, and a third L1-RSRP / SINR measurement) may be provided as an input to an AI / ML model capable of performing AI / ML-based beam prediction. The AI / ML model may be run on a network node or a UE. When AI / ML-based beam prediction is performed at a network node, the input may be an L1-RSRP / SINR measurement reported by a UE. When AI / ML-based beam prediction is performed at a UE, the input may be an L1-RSRP / SINR measurement measured by a UE. As shown by reference numeral 510, the AI / ML model may generate an output based at least in part on the input, wherein the output may indicate a predicted L1-RSRP / SINR measurement, a predicted candidate beam, and / or a predicted beam failure / blockage. For example, the output may be based at least in part on the time series of L1-RSRP / SINR measurements. AI / ML-based beam prediction can result in reduced UE power or UE-specific reference signal overhead, as well as better latency and throughput.

[0085] As indicated above, Figure 5 are provided as examples. Other examples can be found in the Figure 5 The examples described are different.

[0086] AI / ML-based predictive beam management may involve beam management using AI / ML. In traditional beam management processes, beam quality / failure may be identified via measurement, which may involve more power / overhead required to achieve good performance. Beam accuracy may be limited due to constraints on power / overhead, and latency / throughput may be affected by beam recovery efforts. AI / ML-based predictive beam management may provide predictive beam management in the spatial, time, and / or frequency domains, which may result in power / overhead reduction and / or accuracy / latency / throughput improvements. AI / ML-based predictive beam management may predict unmeasured beam quality, which may result in lower power / overhead or better accuracy. For example, AI / ML-based predictive beam management may predict future beam blocking / failure, which may result in better latency / throughput. AI / ML-based predictive beam management may be useful because beam prediction is a highly nonlinear problem. Predicting future Tx beam quality may depend on the movement speed / trajectory of the UE, the Rx beam used or to be used, and / or interference, which may be difficult to model via conventional statistical signaling processing techniques.

[0087] AI / ML-based predictive beam management may involve prediction of beams via AI / ML at the UE or at the network node, which may involve a tradeoff between performance and UE power. To predict future DL-Tx beam quality, the UE may have more observations (via measurements) than the network node has (via UE feedback). Therefore, beam prediction at the UE may outperform beam prediction at the network node, but may involve more UE power consumption. Model training may occur at the network node or at the UE. For model training at the network node, data may be collected via the air interface or via application layer methods. For model training at the UE, additional UE compute / buffering capabilities may be used for model training and data storage.

[0088] For AI / ML based predictive beam management, a first case of beam management and a second case of beam management may be supported for characterization and baseline performance evaluation. In the first case, the spatial downlink beam prediction for beam set A may be based at least in part on the measurement results of beam set B. In the second case, the temporal downlink beam prediction for beam set A may be based at least in part on the historical measurement results of beam set B.

[0089] For the first case and the second case, a first alternative and a second alternative may be defined. In the first alternative, the beams in set A and the beams in set B may be in the same frequency range. For the first case, the beams in set B may be a subset of the beams in set A. The number of beams in set A and the number of beams in set B may be defined. The beams in set B may be determined from the beams in set A based at least in part on a fixed pattern or a random pattern. In the second alternative, the beams in set A may be different from the beams in set B (e.g., the beams in set B may not be a subset of the beams in set A). For example, the beams in set A may be associated with narrow beams, and the beams in set B may be associated with wide beams. The number of beams in set A and the number of beams in set B may be defined. A quasi-co-location (QCL) relationship may be defined between the beams in set A and the beams in set B. For the first alternative and the second alternative, set A may be associated with downlink beam prediction, and set B may be associated with downlink beam measurement. A codebook construction of set A and a codebook construction of set B may be defined.

[0090] Figure 6 is a diagram illustrating example 600 of AI / ML model complexity in accordance with the present disclosure.

[0091] As shown in reference numeral 602, a first L1-RSRP measurement that may be based at least in part on a first SSB and / or a first CSI-RS may be provided as an input to a first AI / ML model. The first AI / ML model may be based at least in part on a deep neural network (DNN) or a convolutional neural network (CNN). The complexity of the DNN / CNN may change dynamically depending on the priority of different AI / ML tasks. The first AI / ML model may provide a first predicted L1-RSRP measurement as an output. The first predicted L1-RSRP measurement may be associated with narrow beams that may be sent using CSI-RS. The first predicted L1-RSRP measurement may be based at least in part on the input of the first L1-RSRP measurement. The first predicted L1-RSRP measurement may be associated with a first mean absolute RSRP prediction error 606.

[0092] As shown by reference numeral 604, a second L1-RSRP measurement that may be based at least in part on a second SSB and / or a second CSI-RS may be provided as an input to a second AI / ML model. The second AI / ML model may be based at least in part on a DNN or a CNN. The second AI / ML model may provide a second predicted L1-RSRP measurement as an output. The second predicted L1-RSRP measurement may be associated with narrow beams that may be transmitted using CSI-RS. The second predicted L1-RSRP measurement may be based at least in part on the input of the second L1-RSRP measurement. The second predicted L1-RSRP measurement may be associated with a second average absolute RSRP prediction error 608.

[0093] The second AI / ML model may be associated with a wider and / or deeper DNN / CNN than the first AI / ML model, which may be based at least in part on the first mean absolute RSRP prediction error 606 being associated with a larger mean absolute RSRP prediction error and the second mean absolute RSRP prediction error 608 being associated with a lower mean absolute RSRP prediction error. For example, a wider and / or deeper DNN / CNN may be desired when more accurate beam prediction is desired.

[0094] As indicated above, Figure 6 are provided as examples. Other examples can be found in the Figure 6 The examples described are different.

[0095] In AI / ML-based predictive beam management, beam prediction accuracy may depend on the complexity of the AI / ML model. When assuming the same input / output dimensions (e.g., the number of L1-RSRP measurements associated with the input is the same as the number of predicted L1-RSRP measurements associated with the output), a deeper or wider AI / ML model may achieve better performance. When assuming the same output dimensions but different input dimensions / timings, an AI / ML model with more complex input dimensions / timings may provide better beam prediction accuracy, but may involve a deeper or wider neural network. Regardless of the input / output dimensions, the minimum amount of data required to train a more complex AI / ML model may be more than the minimum amount of data required to train a less complex AI / ML model.

[0096] The network node may schedule the UE to perform a beam prediction task, but may not be aware of the beam prediction capabilities of the UE. Different UEs may have different capabilities in beam prediction. Some UEs may have dedicated hardware for beam prediction, while some UEs may share hardware for beam prediction with other tasks. Other tasks may be associated with higher priorities, so when other tasks are running, fewer hardware resources may be allocated for beam prediction. When the network node schedules the UE to perform a beam prediction task, the network node may not be aware of the complexity level of the AI / ML model that can be run by the UE. The network node may also not know whether the UE is able to perform the beam prediction task according to a certain level of accuracy. The UE may attempt to perform the beam prediction task based at least in part on the scheduling of the network node. However, depending on the capabilities of the UE, the UE may not be able to perform the beam prediction task, and attempting to perform the beam prediction task may overload the UE and cause the UE to fail. In one example, when no higher priority application is running, the UE may be able to perform the beam prediction task, but when a higher priority application is triggered, the UE may no longer be able to perform the beam prediction task. In one example, the UE may attempt to perform the beam prediction task, but the limited capabilities of the UE may result in a relatively high probability of beam prediction inaccuracy.

[0097] In various aspects of the techniques and devices described herein, a UE may send a capability report associated with the beam prediction capability of the UE to a network node. The capability report may indicate a target beam prediction accuracy (e.g., a required beam prediction accuracy or a beam prediction accuracy threshold). The target beam prediction accuracy may correspond to an average absolute RSRP beam prediction error (in dB). The capability report may indicate the number of reference signal resources indicated as measurement resources for beam prediction and / or the type of reference signal resources indicated as measurement resources for beam prediction. The capability report may indicate the number of beams as predicted target beams and / or the type of beams as predicted target beams. The network node may receive a capability report from the UE. The network node may determine a beam prediction task to be performed by the UE. The network node may verify that the beam prediction task is consistent with the capability report. For example, the network node may not assign a beam prediction task to the UE that the UE cannot perform. The UE may receive a request to perform a beam prediction task from the network node, which may be consistent with the capability report. For example, the UE may receive a scheduling associated with the beam prediction task. The UE may perform a beam prediction task and generate a beam prediction result. The beam prediction result may meet a target beam prediction accuracy indicated in the capability report. For example, the beam prediction result may be associated with an accuracy level that meets the target beam prediction accuracy. The UE may send the beam prediction result to a network node. In some aspects, since the beam prediction task may be assigned to the UE based at least in part on the capabilities of the UE, the UE may be able to perform the beam prediction task without failure, thereby improving the performance of the UE.

[0098] In some aspects, the UE may send a capability report to the network node, where the capability report may be based at least in part on a tradeoff between beam prediction accuracy and AI / ML model complexity. The capability report may indicate various parameters associated with UE-side beam prediction. For example, the capability report may indicate a target beam prediction accuracy (e.g., supported beam prediction accuracy). The capability report may indicate the number of reference signal resources indicated as measurement resources for beam prediction and / or the type of reference signal resources indicated as measurement resources for beam prediction (e.g., the number and type of set B beams supported). The capability report may indicate the number of beams that are prediction target beams and / or the type of beams that are prediction target beams (e.g., the number and type of set A beams supported). Set B may be associated with reference signal resources for downlink beam measurement, and Set A may be associated with reference signal resources for downlink beam prediction.

[0099] In some aspects, the hardware-based AI / ML model can achieve a more dynamically changing model complexity and thus achieve higher beam prediction accuracy based on the priority of different AI / ML tasks and / or the UE's preference for power saving. The UE can dynamically update the relevant capabilities based at least in part on dynamic computing resource rebalancing. For example, when the UE dynamically changes its AI / ML model complexity, the UE can dynamically update the relevant capabilities. The network node can assign beam prediction tasks to the UE based at least in part on the UE's updated capabilities.

[0100] Figure 7 is a diagram illustrating an example 700 associated with sending a capability report indicating the beam prediction capability of a UE according to the present disclosure. Figure 7 As shown, example 700 includes communications between a UE (e.g., UE 120) and a network node (e.g., network node 110). In some aspects, the UE and the network node may be included in a wireless network (such as wireless network 100).

[0101] As shown by reference numeral 702, the UE may send a capability report associated with the beam prediction capability of the UE to the network node (e.g., the UE may output the capability report, and / or the network node may obtain the capability report). The UE may send the capability report via RRC signaling during initial access. The capability report may indicate a target beam prediction accuracy associated with the UE (or a required beam prediction accuracy or a beam prediction accuracy threshold). In some aspects, the target beam prediction accuracy may be associated with an average prediction error, a maximum prediction error, and / or a standard predefined prediction error, which may be associated with the beam prediction capability of the UE. The average prediction error, the maximum prediction error, and / or the standard predefined prediction error may be based at least in part on reporting a single value across multiple beams associated with the predicted target beam, or may be based at least in part on reporting multiple values ​​for multiple beams associated with the predicted target beam. The average prediction error, the maximum prediction error, and the standard predefined prediction error may each be associated with L1-RSRP, L1-SINR, a rank indicator (RI), and / or CQI.

[0102] In some aspects, the capability report may indicate the number of reference signal resources indicated as measurement resources for beam prediction and / or the type of reference signal resources indicated as measurement resources for beam prediction. The type of reference signal resources indicated as measurement resources for beam prediction may be associated with reference signal resources with a certain periodicity, reference signal resources with a single port, and / or reference signal resources with multiple ports. The capability report may indicate the number of historical time domain measurement opportunities used as input for beam prediction, and / or the interval between adjacent historical time domain opportunities used as input for beam prediction.

[0103] In some aspects, the capability report may indicate the number of beams that are prediction target beams and / or the type of beams that are prediction target beams. The type of beam indicated as a prediction target beam may be associated with a beam that carries a reference signal resource configured as a measurement resource, a beam that does not carry a reference signal resource configured as a measurement resource, a beam that is periodically transmitted, and / or a beam that is not periodically transmitted. The capability report may indicate the number of upcoming time domain opportunities predicted for the beams that are prediction target beams and / or the interval between adjacent upcoming time domain opportunities predicted for the beams that are prediction target beams.

[0104] In some aspects, the UE may perform UE capability reporting that takes into account the tradeoff between beam prediction accuracy and AI / ML model complexity. As part of the UE beam prediction capability, the UE may report the target beam prediction accuracy to the network node. As part of the UE beam prediction capability, the UE may report to the network node the number and / or type of reference signal resources indicated as measurement resources (e.g., resources associated with set B). As part of the UE beam prediction capability, the UE may report to the network node the number and / or type of beams (or reference signal resources) that are predicted target beams (e.g., beams and / or resources associated with set A). As part of the UE beam prediction capability, the UE may report to the network node one or more combinations of target beam prediction accuracy, the number and / or type of reference signal resources indicated as measurement resources, and / or the number and / or type of beams that are predicted target beams. As part of the UE's beam prediction capability, the UE may send a capability report indicating the one or more combinations to the network node.

[0105] In some aspects, the UE may expect the network node to only configure or indicate beam prediction requests that satisfy the reported capabilities as indicated in the UE capability report. For example, the UE may expect the network node to only configure or indicate beam prediction requests that satisfy the one or more combinations. In some aspects, the UE may perform UE capability reporting via RRC signaling during initial access. In one example, the UE may perform additional UE capability reporting via dynamic update after initial access.

[0106] In some aspects, the UE may report the target beam prediction accuracy to the network node. In some aspects, as part of reporting the target beam prediction accuracy, the UE may report an average (or absolute) L1-RSRP measurement, L1-SINR measurement, RI and / or CQI prediction error (e.g., expressed in dBm of the L1-RSRP measurement). The UE may report the average (or absolute) L1-RSRP measurement, L1-SINR measurement, RI and / or CQI prediction error based at least in part on reporting a single value, which may be averaged across multiple predicted target beams. The UE may report the average (or absolute) L1-RSRP measurement, L1-SINR measurement, RI and / or CQI prediction error based at least in part on reporting multiple values, each of which may correspond to each beam in the predicted target beam, and wherein the predicted target beam may be associated with a specific combination of the target beam prediction accuracy, the number and / or type of reference signal resources indicated as measurement resources, and / or the number and / or type of beams as predicted target beams.

[0107] In some aspects, as part of reporting the target beam prediction accuracy, the UE may report a maximum (or absolute) L1-RSRP measurement, L1-SINR measurement, RI, and / or CQI prediction accuracy (e.g., expressed in dBm of the L1-RSRP measurement). The UE may report the maximum (or absolute) L1-RSRP measurement, L1-SINR measurement, RI, and / or CQI prediction error based at least in part on reporting a single value, which may be an average across multiple predicted target beams. The UE may report the maximum (or absolute) L1-RSRP measurement, L1-SINR measurement, RI, and / or CQI prediction accuracy based at least in part on reporting multiple values, wherein each value may correspond to each beam in the predicted target beams, and wherein the predicted target beams may be associated with a specific combination of the target beam prediction accuracy, the number and / or type of reference signal resources indicated as measurement resources, and / or the number and / or type of beams as predicted target beams.

[0108] In some aspects, as part of reporting the target beam prediction accuracy, the UE may report a standard predefined L1-RSRP measurement, L1-SINR measurement, RI and / or CQI prediction accuracy / uncertainty level. The UE may report the standard predefined L1-RSRP measurement, L1-SINR measurement, RI and / or CQI prediction error based at least in part on reporting a single value, which may be an average across multiple predicted target beams. The UE may report the standard predefined L1-RSRP measurement, L1-SINR measurement, RI and / or CQI prediction accuracy based at least in part on reporting multiple values, wherein each value may correspond to each beam in the predicted target beam, and wherein the predicted target beam may be associated with a specific combination of the target beam prediction accuracy, the number and / or type of reference signal resources indicated as measurement resources, and / or the number and / or type of beams as predicted target beams.

[0109] In some aspects, the UE may report to the network node the number and / or type of reference signal resources indicated as measurement resources (e.g., the number / type of set B beams), and / or the number and / or type of beams that are prediction target beams (e.g., the number / type of set A beams). In some aspects, certain types of reference signal resources may be configured / indicated by the network node as measurement resources for beam prediction (e.g., set A), where the reference signal resources may be associated with periodicity. Certain types of reference signal resources may be configured / indicated by the network node as measurement resources for beam prediction (e.g., set B), where the reference signal resources may be associated with a single-port CSI-RS or SSB or multi-port CSI-RS.

[0110] In some aspects, the UE may predict and / or report certain types of beams. Beams that may be predicted and / or reported may include predicted beams that carry reference signal resources configured as measurement resources, or predicted beams that do not carry reference signal resources configured as measurement resources. Beams that may be predicted and / or reported may include predicted beams that are periodically sent by a network node, or predicted beams that are not sent by a network node. The periodically sent predicted beams may be sent with a longer periodicity than the reference signal resources indicated as measurement resources (e.g., via CSI-RS or SSB). For time domain beam prediction, the beams that may be predicted and / or reported may be associated with the number of future time domain opportunities predicted for these beams. For time domain beam prediction, the beams that may be predicted and / or reported may be associated with the intervals between adjacent future time domain opportunities predicted for these beams.

[0111] In some aspects, the capability report may indicate one or more combinations of UE capabilities, where the combination may be based at least in part on one or more of target beam prediction accuracy, the number of reference signal resources indicated as measurement resources and / or the type of reference signal resources indicated as measurement resources, and / or the number of beams as predicted target beams and / or the type of beams as predicted target beams. The capability report may indicate one or more combinations of UE capabilities based at least in part on a standard pre-definition (e.g., a pre-definition indicated in a 3GPP technical specification) or a pre-configuration received from a network node. In some aspects, the capability report may indicate one or more sets of combinations of UE capabilities, where a set of combinations of UE capabilities from one or more sets of combinations of UE capabilities may indicate one or more combinations of UE capabilities. The set of combinations of UE capabilities may be activated simultaneously for the UE.

[0112] In some aspects, the UE may perform capability reporting according to a capability reporting framework. In some aspects, the capability reporting framework may be predefined by a standard and / or preconfigured by a network node. The capabilities predefined by a standard, reconfigured by a network node and / or reported by a UE may be divided into different situations, depending on which mechanism is used to carry the prediction results reported by the UE. For example, the UE may send a medium access control control element (MAC-CE) to carry the prediction results reported by the UE. Alternatively, the UE may send a channel state information (CSI) report to carry the prediction results reported by the UE. The CSI report may be a periodic CSI report, a semi-persistent CSI report, or an aperiodic CSI report.

[0113] In some aspects, the capability reporting framework may be based at least in part on simultaneously activated combinations. The UE may report one or more sets of combinations, where each set may include one or more combinations, and where each combination may refer to a specific target beam prediction accuracy, the number and / or type of reference signal resources indicated as measurement resources, and / or the number and / or type of beams as predicted target beams. Each set of combinations may be simultaneously activated for the UE, such that the UE may be able to simultaneously perform beam prediction tasks associated with different combinations reported in each respective set.

[0114] As indicated by reference numeral 704, the UE may receive a request from a network node to perform a beam prediction task consistent with a capability report (e.g., the UE may output the request, and / or the network node may obtain the request). The beam prediction task may correspond to one or more of a target beam prediction accuracy, a number of reference signal resources indicated as measurement resources, and / or a type of reference signal resources indicated as measurement resources, and / or a number of beams as prediction target beams and / or a type of beams as prediction target beams. The network node may determine, at least in part based on the capability report received from the UE, to schedule the UE to perform the beam prediction task. For example, when the network node has a specific beam prediction task that can be performed by the UE, the network node may request the UE to perform the beam prediction task. Otherwise, the network node may request another UE to perform the beam prediction task.

[0115] As indicated by reference numeral 706, the UE may send, based at least in part on the request, to the network node a beam prediction result that satisfies a target beam prediction accuracy indicated in the capability report (e.g., the UE may output the beam prediction result and / or the network node may obtain the beam prediction result). As indicated in the capability report, the beam prediction result may be associated with a certain accuracy level. In some aspects, the UE may send the beam prediction result (or the prediction result reported by the UE) via a MAC-CE or CSI report, which may be a periodic CSI report, a semi-persistent CSI report, or an aperiodic CSI report.

[0116] As indicated by reference numeral 708, the UE may send an updated capability report indicating updated beam prediction capabilities of the UE to the network node after initial access (e.g., the UE may output an updated capability report, and / or the network node may obtain an updated capability report). For example, the capability report may be associated with a first combination of UE capabilities, and the updated capability report may be associated with a second combination of UE capabilities, wherein the second combination of UE capabilities may be different from the first combination of UE capabilities.

[0117] In some aspects, the UE may be capable of dynamic capability update. The UE may dynamically update the capability report to form an updated capability report, and the UE may send the updated capability report to the network node. The UE may send the updated capability report via RRC signaling, MAC-CE, or uplink control information (UCI). In some aspects, the UE may report multiple combinations or multiple sets of combinations during initial access. One combination may be associated with a default combination. The UE may dynamically update the default combination by reporting another identifier, which may be associated with another combination that will become the new default combination.

[0118] As an example, dedicated hardware may be used to implement an AI / ML model for beam prediction that may be formed using an AI / ML inference neural network. The depth and width associated with the AI / ML model may be changed dynamically. Other applications that have a higher priority than the AI / ML model may also share the same hardware, but the other applications may not always be activated. Other applications may be transparent to the network node. Such applications may involve UE autonomous mobile payload element (MPE) detection or real-time demodulation or decoding. When a high-priority application that may be transparent to the network node is activated, at least some of the hardware resources that the UE originally used for AI / ML-based beam prediction may be reallocated to the high-priority application. In this case, the UE may signal a dynamic capability update to the network node to indicate the updated UE capabilities.

[0119] As indicated above, Figure 7 are provided as examples. Other examples can be found in the Figure 7 The examples described are different.

[0120] FIG. 8A to FIG. 8B is a diagram illustrating an example 800 associated with sending a capability report indicating a beam prediction capability of a UE in accordance with the present disclosure.

[0121] In some aspects, the UE may send a capability report to the network node. The capability report may indicate the number and type of reference signal resources indicated as measurement resources, the number and type of reference signal resources as predicted target beams, and / or the target beam prediction accuracy. The capability report may indicate, as its UE beam prediction capability, one or more combinations of the number and type of reference signal resources indicated as measurement resources, the number and type of reference signal resources as predicted target beams, and / or the target beam prediction accuracy.

[0122] like Fig. 8A As shown, the UE may send a capability report indicating the first combination to the network node. For the number and type of reference signal (RS) resources indicated as measurement resources, the capability report may indicate two SSBs in three consecutive 20ms opportunities in history. For the number and type of reference signal resources as prediction target beams, the capability report may indicate L1-RSRP measurements of 8 narrow beams that were not transmitted. For the target beam prediction accuracy, the capability report may indicate an average absolute RSRP prediction error equal to 6dBm. The first combination may be associated with a first set of values.

[0123] like Figure 8BAs shown, the UE may send a capability report indicating the second combination to the network node. For the number and type of reference signal resources indicated as measurement resources, the capability report may indicate four SSBs in four consecutive historical 20ms opportunities. For the number and type of reference signal resources as prediction target beams, the capability report may indicate L1-RSRP measurements of 8 narrow beams that were not transmitted. For the target beam prediction accuracy, the capability report may indicate an average absolute RSRP prediction error equal to 1 dBm. The second combination may be associated with a second set of values.

[0124] As indicated above, FIG. 8A to FIG. 8B are provided as examples. Other examples can be found in the FIG. 8A to FIG. 8B The examples described are different.

[0125] Fig. 9A , Fig. 9B and Fig. 9C is a diagram illustrating example 900 associated with sending a capability report indicating beam prediction capabilities of a UE in accordance with the present disclosure.

[0126] In some aspects, the UE may report one or more sets of combinations, where each set may include one or more combinations, and where each combination may indicate a specific target beam prediction accuracy, the number and / or type of reference signal resources indicated as measurement resources, and / or the number and / or type of beams as predicted target beams. Fig. 9A As shown, the first set 902 may include a first combination, a second combination, and a fourth combination. Fig. 9B As shown, the second set 904 may include the first combination, the fourth combination, the sixth combination, and the seventh combination. Fig. 9C As shown, the third set 906 may include the second combination and the fifth combination. The UE may be able to simultaneously perform beam prediction tasks associated with different combinations reported in each respective set.

[0127] As indicated above, Fig. 9A , Fig. 9B and Fig. 9C Other examples can be found in the Fig. 9A , Fig. 9B and Fig. 9C Different from what is described.

[0128] Fig.10 is a diagram illustrating example 1000 associated with sending a capability report indicating beam prediction capabilities of a UE in accordance with the present disclosure.

[0129] As indicated by reference numeral 1002, the UE may send a capability report indicating the second combination to the network node. For the number and type of reference signal resources indicated as measurement resources, the capability report may indicate four SSBs in four consecutive historical 20ms opportunities. For the number and type of reference signal resources as predicted target beams, the capability report may indicate L1-RSRP measurements for 8 narrow beams that were not transmitted. For the target beam prediction accuracy, the capability report may indicate an average absolute RSRP prediction error equal to 1 dBm. The UE may be able to implement the second combination when the UE's AI / ML model uses hardware dedicated entirely to beam prediction. The UE's hardware may be used by other higher priority applications of the UE, but other higher priority applications may not be activated when the UE uses the second combination.

[0130] As indicated by reference numeral 1004, the UE may send an updated capability report indicating a first combination to the network node. The updated capability report may indicate a dynamic capability update, wherein the first combination may be an update to a second combination previously sent by the UE. The UE may send an updated capability report based at least in part on the UE activating one of the higher priority applications, thereby consuming some hardware previously used only by the AI / ML model. For the number and type of reference signal resources indicated as measurement resources, the updated capability report may indicate two SSBs in three consecutive 20ms opportunities in history. For the number and type of reference signal resources that are predicted target beams, the updated capability report may indicate L1-RSRP measurements for 8 narrow beams that were not sent. For target beam prediction accuracy, the updated capability report may indicate an average absolute RSRP prediction error equal to 6dBm. The first combination may be associated with a first set of values. Because the first combination may be used when fewer hardware resources are available at the UE (e.g., because a higher priority application is executing simultaneously), the target prediction accuracy and / or mean absolute RSRP prediction error of the first combination may be greater than the target prediction accuracy and / or mean absolute RSRP prediction error associated with the second combination (during which other higher priority applications are not using the UE's hardware resources).

[0131] As indicated above, Fig.10 are provided as examples. Other examples can be found in the Fig.10 The examples described are different.

[0132] Fig.11 is a diagram illustrating an example process 1100 performed, for example, by a UE in accordance with the present disclosure. Example process 1100 is an example in which a UE (eg, UE 120) performs operations associated with sending a capability report indicating beam prediction capabilities of the UE.

[0133] like Fig.11As shown, in some aspects, process 1100 may include sending a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy (block 1110). Fig.13 The sending component 1304 depicted in may send a capability report associated with the beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy, as described above.

[0134] like Fig.11 As further shown, in some aspects, process 1100 may include receiving a request to perform a beam prediction task consistent with a capability report (block 1120). Fig.13 The receiving component 1302 depicted in can receive a request to perform a beam prediction task consistent with a capability report, as described above.

[0135] like Fig.11 As further shown, in some aspects, process 1100 may include sending a beam prediction result that satisfies a target beam prediction accuracy indicated in the capability report based at least in part on the request (block 1130). Fig.13 The sending component 1304 depicted in may send a beam prediction result that meets a target beam prediction accuracy indicated in the capability report based at least in part on the request, as described above.

[0136] Process 1100 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in combination with one or more other processes described elsewhere herein.

[0137] In a first aspect, target beam prediction accuracy is associated with one or more of an average prediction error, a maximum prediction error, or a standard predefined prediction error, wherein the average prediction error, the maximum prediction error, or the standard predefined prediction error is based at least in part on reporting a single value across multiple beams associated with the predicted target beam, or is based at least in part on reporting multiple values ​​for multiple beams associated with the predicted target beam.

[0138] In a second aspect, alone or in combination with the first aspect, the average prediction error, the maximum prediction error and the standard predefined prediction error are each associated with one or more of L1-RSRP, L1-SINR, RI or CQI.

[0139] In a third aspect, alone or in combination with one or more of the first and second aspects, the capability report indicates the number of reference signal resources indicated as measurement resources for beam prediction or one or more of the types of reference signal resources indicated as measurement resources for beam prediction.

[0140] In a fourth aspect, either alone or in combination with one or more of the first to third aspects, the type of reference signal resources indicated as measurement resources for beam prediction is associated with one or more of reference signal resources having a certain periodicity, reference signal resources having a single port, or reference signal resources having multiple ports.

[0141] In a fifth aspect, either alone or in combination with one or more of the first to fourth aspects, the capability report indicates one or more of the number of historical time domain measurement opportunities used as input for beam prediction or the intervals between adjacent historical time domain opportunities used as input for beam prediction.

[0142] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the capability report indicates one or more of the number of beams as predicted target beams or the type of beams as predicted target beams.

[0143] In the seventh aspect, alone or in combination with one or more of the first to sixth aspects, the type of beam indicated as a predicted target beam is associated with one or more of a beam carrying a reference signal resource configured as a measurement resource, a beam not carrying a reference signal resource configured as a measurement resource, a beam that is periodically sent, or a beam that is not periodically sent.

[0144] In an eighth aspect, either alone or in combination with one or more of aspects one to seven, the capability report indicates one or more of the number of upcoming time domain opportunities predicted for a beam that is a predicted target beam or the intervals between adjacent upcoming time domain opportunities predicted for a beam that is a predicted target beam.

[0145] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, sending the capability report comprises sending the capability report via RRC signaling during initial access.

[0146] In the tenth aspect, alone or in combination with one or more of the first to ninth aspects, the capability report indicates one or more combinations of UE capabilities, the UE capabilities including one or more of target beam prediction accuracy, the number of reference signal resources indicated as measurement resources or the types of reference signal resources indicated as measurement resources, and one or more of the number of beams as predicted target beams or the types of beams as predicted target beams, wherein the capability report indicates one or more combinations of UE capabilities at least partially based on standard predefinition or preconfiguration.

[0147] In the eleventh aspect, alone or in combination with one or more of the first to tenth aspects, the capability report indicates one or more sets of combinations of UE capabilities, wherein a set of combinations of UE capabilities from the one or more sets of combinations of UE capabilities indicates one or more combinations of UE capabilities, and the set of combinations of UE capabilities is configured to be activated simultaneously for the UE.

[0148] In a twelfth aspect, either alone or in combination with one or more of aspects 1 to eleven, process 1100 includes sending an updated capability report indicating updated beam prediction capabilities of the UE after initial access, wherein the capability report is associated with a first combination of UE capabilities and the updated capability report is associated with a second combination of UE capabilities.

[0149] although Fig.11 Example blocks of process 1100 are shown, but in some aspects, process 1100 may include Fig.11 The blocks depicted may be additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted. Additionally or alternatively, two or more of the blocks of process 1100 may be performed in parallel.

[0150] Fig.12 is a diagram illustrating an example process 1200 performed, for example, by a network node in accordance with the present disclosure. The example process 1200 is an example in which a network node (eg, network node 110) performs operations associated with sending a capability report indicating a beam prediction capability of a UE.

[0151] like Fig.12 As shown, in some aspects, process 1200 may include receiving a capability report associated with a beam prediction capability of a UE, the capability report indicating a target beam prediction accuracy (block 1210). For example, a network node (e.g., using Fig.16 The receiving component 1602 depicted in may receive a capability report associated with the beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy, as described above.

[0152] like Fig.12 As further shown, in some aspects, process 1200 may include sending a request to perform a beam prediction task consistent with the capability report (block 1220). Fig.16 The sending component 1604 depicted in may send a request to perform a beam prediction task consistent with the capability report, as described above.

[0153] like Fig.12As further shown, in some aspects, process 1200 may include receiving a beam prediction result that satisfies a target beam prediction accuracy indicated in the capability report based at least in part on the request (block 1230). Fig.16 The receiving component 1602 depicted in may receive a beam prediction result that satisfies a target beam prediction accuracy indicated in the capability report based at least in part on the request, as described above.

[0154] Process 1200 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in combination with one or more other processes described elsewhere herein.

[0155] In a first aspect, target beam prediction accuracy is associated with one or more of an average prediction error, a maximum prediction error, or a standard predefined prediction error, wherein the average prediction error, the maximum prediction error, or the standard predefined prediction error is based at least in part on reporting a single value across multiple beams associated with the predicted target beam, or is based at least in part on reporting multiple values ​​for multiple beams associated with the predicted target beam.

[0156] In a second aspect, alone or in combination with the first aspect, the average prediction error, the maximum prediction error and the standard predefined prediction error are each associated with one or more of L1-RSRP, L1-SINR, RI or CQI.

[0157] In a third aspect, alone or in combination with one or more of the first and second aspects, the capability report indicates the number of reference signal resources indicated as measurement resources for beam prediction or one or more of the types of reference signal resources indicated as measurement resources for beam prediction.

[0158] In a fourth aspect, either alone or in combination with one or more of the first to third aspects, the type of reference signal resources indicated as measurement resources for beam prediction is associated with one or more of reference signal resources having a certain periodicity, reference signal resources having a single port, or reference signal resources having multiple ports.

[0159] In a fifth aspect, either alone or in combination with one or more of the first to fourth aspects, the capability report indicates one or more of the number of historical time domain measurement opportunities used as input for beam prediction or the intervals between adjacent historical time domain opportunities used as input for beam prediction.

[0160] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the capability report indicates one or more of the number of beams as predicted target beams or the type of beams as predicted target beams.

[0161] In the seventh aspect, alone or in combination with one or more of the first to sixth aspects, the type of beam indicated as a predicted target beam is associated with one or more of a beam carrying a reference signal resource configured as a measurement resource, a beam not carrying a reference signal resource configured as a measurement resource, a beam that is periodically sent, or a beam that is not periodically sent.

[0162] In an eighth aspect, either alone or in combination with one or more of aspects one to seven, the capability report indicates one or more of the number of upcoming time domain opportunities predicted for a beam that is a predicted target beam or the intervals between adjacent upcoming time domain opportunities predicted for a beam that is a predicted target beam.

[0163] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, receiving the capability report comprises receiving the capability report via RRC signaling during initial access.

[0164] In the tenth aspect, alone or in combination with one or more of the first to ninth aspects, the capability report indicates one or more combinations of UE capabilities, the UE capabilities including one or more of target beam prediction accuracy, the number of reference signal resources indicated as measurement resources or the types of reference signal resources indicated as measurement resources, and one or more of the number of beams as predicted target beams or the types of beams as predicted target beams, wherein the capability report indicates one or more combinations of UE capabilities based at least in part on standard predefinition or preconfiguration.

[0165] In the eleventh aspect, alone or in combination with one or more of the first to tenth aspects, the capability report indicates one or more sets of combinations of UE capabilities, wherein a set of combinations of UE capabilities from the one or more sets of combinations of UE capabilities indicates one or more combinations of UE capabilities, and the set of combinations of UE capabilities is configured to be activated simultaneously for the UE.

[0166] In a twelfth aspect, either alone or in combination with one or more of aspects 1 to eleven, process 1200 includes receiving an updated capability report indicating updated beam prediction capabilities of the UE after initial access, wherein the capability report is associated with a first combination of UE capabilities and the updated capability report is associated with a second combination of UE capabilities.

[0167] although Fig.12 An example block diagram of process 1200 is shown, but in some aspects, process 1200 may include Fig.12The blocks depicted may be additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted. Additionally or alternatively, two or more of the blocks of process 1200 may be performed in parallel.

[0168] Fig.13 1 is a diagram of an example apparatus 1300 for wireless communication according to the present disclosure. Apparatus 1300 may be a UE, or a UE may include apparatus 1300. In some aspects, apparatus 1300 includes a receiving component 1302 and a transmitting component 1304, which may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 1300 may communicate with another apparatus 1306 (such as a UE, a base station, or another wireless communication device) using receiving component 1302 and transmitting component 1304.

[0169] In some aspects, the apparatus 1300 may be configured to perform Figure 7 , Fig. 8A , Figure 8B , Fig. 9A , Fig. 9B , Fig. 9C and Fig.10 Additionally or alternatively, the apparatus 1300 may be configured to perform one or more processes described herein, such as Fig.11 The process 1100. In some aspects, Fig.13 The device 1300 and / or one or more components shown may include a combination of Figure 2 Additionally or alternatively, Fig.13 One or more of the components shown may be combined with Figure 2 Additionally or alternatively, one or more components in the component set may be implemented at least in part as software stored in a memory. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and can be executed by a controller or processor to perform the function or operation of the component.

[0170] The receiving component 1302 may receive communications, such as reference signals, control information, data communications, or combinations thereof, from the device 1306. The receiving component 1302 may provide the received communications to one or more other components of the device 1300. In some aspects, the receiving component 1302 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, etc.) on the received communications and may provide the processed signals to the one or more other components of the device 1300. In some aspects, the receiving component 1302 may include combining Figure 2One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof of the described UE.

[0171] Transmit component 1304 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to device 1306. In some aspects, one or more other components of device 1300 may generate communications and may provide the generated communications to transmit component 1304 for transmission to device 1306. In some aspects, transmit component 1304 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to device 1306. In some aspects, transmit component 1304 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described UE. In some aspects, the transmit component 1304 can be co-located with the receive component 1302 in a transceiver.

[0172] The sending component 1304 may send a capability report associated with the beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy. The receiving component 1302 may receive a request to perform a beam prediction task consistent with the capability report. The sending component 1304 may send a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request. The sending component 1304 may send an updated capability report indicating updated beam prediction capabilities of the UE after initial access, wherein the capability report is associated with a first combination of UE capabilities and the updated capability report is associated with a second combination of UE capabilities.

[0173] Fig.13 The number and arrangement of components shown are provided as examples. In practice, there may be Fig.13 Additional components, fewer components, different components, or components arranged in a different manner than those shown. Fig.13 Two or more components shown may be implemented in a single component, or Fig.13 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Fig.13 The illustrated set of component(s) may be described as being executable by Fig.13 Another collection of components shown performs one or more functions.

[0174] Fig.14 is a diagram illustrating an example 1400 of a hardware implementation for an apparatus 1405 employing a processing system 1410 according to the present disclosure. The apparatus 1405 may be a UE.

[0175] The processing system 1410 may be implemented using a bus architecture, generally represented by bus 1415. Bus 1415 may include any number of interconnecting buses and bridges, depending on the specific application of the processing system 1410 and the overall design constraints. Bus 1415 links together various circuits including one or more processors and / or hardware components (represented by processor 1420, illustrated components, and computer readable media / memory 1425). Bus 1415 may also link various other circuits, such as timing sources, peripherals, voltage regulators, and / or power management circuits.

[0176] The processing system 1410 may be coupled to a transceiver 1430. The transceiver 1430 is coupled to one or more antennas 1435. The transceiver 1430 provides components for communicating with various other devices through a transmission medium. The transceiver 1430 receives signals from one or more antennas 1435, extracts information from the received signals, and provides the extracted information to the processing system 1410 (specifically the receiving component 1302). In addition, the transceiver 1430 receives information from the processing system 1410 (specifically the transmitting component 1304) and generates a signal to be applied to the one or more antennas 1435 based at least in part on the received information.

[0177] The processing system 1410 includes a processor 1420 coupled to a computer readable medium / memory 1425. The processor 1420 is responsible for general processing, including executing software stored on the computer readable medium / memory 1425. The software, when executed by the processor 1420, causes the processing system 1410 to perform various functions described herein for any particular device. The computer readable medium / memory 1425 may also be used to store data manipulated by the processor 1420 when executing the software. The processing system also includes at least one of the illustrated components. The component may be: a software module running in the processor 1420, a software module resident / stored in the computer readable medium / memory 1425, one or more hardware modules coupled to the processor 1420, or some combination thereof.

[0178] In some aspects, the processing system 1410 may be a component of the UE 120 and may include the memory 282, and / or at least one of the TX MIMO processor 266, the Rx processor 258, and / or the controller / processor 280. In some aspects, the apparatus 1405 for wireless communication includes a component for sending a capability report associated with the beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy; a component for receiving a request to perform a beam prediction task consistent with the capability report; and a component for sending a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request. The aforementioned components may be one or more of the aforementioned components of the processing system 1410 of the apparatus 1300 and / or the apparatus 1405 configured to perform the functions recited by the aforementioned components. As described elsewhere herein, the processing system 1410 may include the TX MIMO processor 266, the Rx processor 258, and / or the controller / processor 280. In one configuration, the aforementioned components may be the TX MIMO processor 266, the Rx processor 258, and / or the controller / processor 280 configured to perform the functions and / or operations recited herein.

[0179] Fig.14 are provided as examples. Other examples can be combined with Fig.14 The examples described are different.

[0180] Fig.15 is a diagram of an example 1500 illustrating a specific implementation of code and circuits for an apparatus 1505 according to the present disclosure. The apparatus 1505 may be a UE, or a UE may include the apparatus 1505.

[0181] like Fig.15 As shown, the device 1505 may include circuitry for sending a capability report associated with the beam prediction capability of the UE, the capability report indicating the target beam prediction accuracy (circuitry 1520). For example, the circuitry 1520 may enable the device 1505 to send a capability report associated with the beam prediction capability of the UE, the capability report indicating the target beam prediction accuracy.

[0182] like Fig.15 As shown, the apparatus 1505 may include code stored in the computer-readable medium 1425 for sending a capability report associated with the beam prediction capability of the UE, the capability report indicating the target beam prediction accuracy (code 1525). For example, when the code 1525 is executed by the processor 1420, it may cause the processor 1420 to cause the transceiver 1430 to send a capability report associated with the beam prediction capability of the UE, the capability report indicating the target beam prediction accuracy.

[0183] like Fig.15As shown, the device 1505 may include a circuit (circuit 1530) for receiving a request to perform a beam prediction task consistent with the capability report. For example, the circuit 1530 may enable the device 1505 to receive a request to perform a beam prediction task consistent with the capability report.

[0184] like Fig.15 As shown, the apparatus 1505 may include code (code 1535) stored in the computer-readable medium 1425 for receiving a request to perform a beam prediction task consistent with the capability report. For example, when the code 1535 is executed by the processor 1420, it may cause the processor 1420 to cause the transceiver 1430 to receive a request to perform a beam prediction task consistent with the capability report.

[0185] like Fig.15 As shown, the device 1505 may include a circuit (circuit 1540) for sending a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request. For example, the circuit 1540 may enable the device 1505 to send a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0186] like Fig.15 As shown, the device 1505 may include code (code 1545) stored in the computer-readable medium 1425 for sending a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request. For example, when the code 1545 is executed by the processor 1420, it may cause the processor 1420 to cause the transceiver 1430 to send a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0187] Fig.15 are provided as examples. Other examples can be combined with Fig.15 The examples described are different.

[0188] Fig.16 1 is a diagram of an example apparatus 1600 for wireless communication according to the present disclosure. Apparatus 1600 may be a network node, or a network node may include apparatus 1600. In some aspects, apparatus 1600 includes a receiving component 1602 and a sending component 1604, which may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 1600 may communicate with another apparatus 1606 (such as a UE, a base station, or another wireless communication device) using receiving component 1602 and sending component 1604.

[0189] In some aspects, the apparatus 1600 may be configured to perform Figure 7, Fig. 8A , Figure 8B , Fig. 9A , Fig. 9B , Fig. 9C and Fig.10 Additionally or alternatively, the apparatus 1600 may be configured to perform one or more processes described herein, such as Fig.12 The process 1200. In some aspects, Fig.16 The device 1600 and / or one or more components shown may include a combination of Figure 2 Additionally or alternatively, Fig.16 One or more of the components shown may be combined with Figure 2 Additionally or alternatively, one or more components in the component set may be implemented at least in part as software stored in a memory. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and can be executed by a controller or processor to perform the function or operation of the component.

[0190] The receiving component 1602 may receive communications from the device 1606, such as reference signals, control information, data communications, or combinations thereof. The receiving component 1602 may provide the received communications to one or more other components of the device 1600. In some aspects, the receiving component 1602 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, etc.) on the received communications and may provide the processed signals to the one or more other components of the device 1600. In some aspects, the receiving component 1602 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof, of the described network nodes.

[0191] Transmit component 1604 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to device 1606. In some aspects, one or more other components of device 1600 may generate communications and may provide the generated communications to transmit component 1604 for transmission to device 1606. In some aspects, transmit component 1604 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to device 1606. In some aspects, transmit component 1604 may include combining Figure 2One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described network nodes. In some aspects, the transmit component 1604 can be co-located with the receive component 1602 in a transceiver.

[0192] The receiving component 1602 may receive a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy. The sending component 1604 may send a request to perform a beam prediction task consistent with the capability report. The receiving component 1602 may receive a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request. The receiving component 1602 may receive an updated capability report indicating an updated beam prediction capability of the UE after initial access, wherein the capability report is associated with a first combination of UE capabilities and the updated capability report is associated with a second combination of UE capabilities.

[0193] Fig.16 The number and arrangement of components shown are provided as examples. In practice, there may be Fig.16 Additional components, fewer components, different components, or components arranged in a different manner than those shown. Fig.16 Two or more components shown may be implemented in a single component, or Fig.16 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Fig.16 The illustrated set of component(s) may be described as being executable by Fig.16 Another collection of components shown performs one or more functions.

[0194] Fig.17 is a diagram illustrating an example 1700 of a hardware implementation for an apparatus 1705 employing a processing system 1710 according to the present disclosure. The apparatus 1705 may be a network node.

[0195] The processing system 1710 may be implemented using a bus architecture, generally represented by bus 1715. Bus 1715 may include any number of interconnecting buses and bridges, depending on the specific application of the processing system 1710 and the overall design constraints. Bus 1715 links together various circuits including one or more processors and / or hardware components (represented by processor 1720, illustrated components, and computer readable media / memory 1725). Bus 1715 may also link various other circuits, such as timing sources, peripherals, voltage regulators, and / or power management circuits.

[0196] The processing system 1710 may be coupled to a transceiver 1730. The transceiver 1730 is coupled to one or more antennas 1735. The transceiver 1730 provides components for communicating with various other devices through a transmission medium. The transceiver 1730 receives signals from one or more antennas 1735, extracts information from the received signals, and provides the extracted information to the processing system 1710 (specifically the receiving component 1602). In addition, the transceiver 1730 receives information from the processing system 1710 (specifically the transmitting component 1604) and generates a signal to be applied to one or more antennas 1735 based at least in part on the received information.

[0197] The processing system 1710 includes a processor 1720 coupled to a computer-readable medium / memory 1725. The processor 1720 is responsible for general processing, including executing software stored on the computer-readable medium / memory 1725. The software, when executed by the processor 1720, causes the processing system 1710 to perform various functions described herein for any particular device. The computer-readable medium / memory 1725 may also be used to store data manipulated by the processor 1720 when executing the software. The processing system also includes at least one of the illustrated components. The component may be: a software module running in the processor 1720, a software module resident / stored in the computer-readable medium / memory 1725, one or more hardware modules coupled to the processor 1720, or some combination thereof.

[0198] In some aspects, the processing system 1710 may be a component of the base station 110 and may include the memory 242, and / or at least one of the TX MIMO processor 230, the Rx processor 238, and / or the controller / processor 240. In some aspects, the apparatus 1705 for wireless communication includes a component for receiving a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy; a component for sending a request to perform a beam prediction task consistent with the capability report; and a component for receiving a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request. The aforementioned components may be one or more of the aforementioned components of the processing system 1710 of the apparatus 1600 and / or the apparatus 1705 configured to perform the functions recited by the aforementioned components. As described elsewhere herein, the processing system 1710 may include the TX MIMO processor 230, the receive processor 238, and / or the controller / processor 240. In one configuration, the aforementioned components may be the TX MIMO processor 230, the receive processor 238, and / or the controller / processor 240 configured to perform the functions and / or operations stated herein.

[0199] Fig.17are provided as examples. Other examples can be combined with Fig.17 The examples described are different.

[0200] Fig.18 is a diagram of an example 1800 illustrating a specific implementation of code and circuits for an apparatus 1805 according to the present disclosure. The apparatus 1805 may be a network node, or a network node may include the apparatus 1805.

[0201] like Fig.18 As shown, the device 1805 may include circuitry for receiving a capability report associated with the beam prediction capability of the UE, the capability report indicating the target beam prediction accuracy (circuitry 1820). For example, the circuitry 1820 may enable the device 1805 to receive a capability report associated with the beam prediction capability of the UE, the capability report indicating the target beam prediction accuracy.

[0202] like Fig.18 As shown, the apparatus 1805 may include code stored in the computer-readable medium 1725 for receiving a capability report associated with the beam prediction capability of the UE, the capability report indicating the target beam prediction accuracy (code 1825). For example, when the code 1825 is executed by the processor 1720, it may cause the processor 1720 to cause the transceiver 1730 to receive a capability report associated with the beam prediction capability of the UE, the capability report indicating the target beam prediction accuracy.

[0203] like Fig.18 As shown, the device 1805 may include a circuit (circuit 1830) for sending a request to perform a beam prediction task consistent with the capability report. For example, the circuit 1830 may enable the device 1805 to send a request to perform a beam prediction task consistent with the capability report.

[0204] like Fig.18 As shown, the apparatus 1805 may include a code (code 1835) for sending a request to perform a beam prediction task consistent with the capability report stored in the computer-readable medium 1725. For example, when the code 1835 is executed by the processor 1720, it may cause the processor 1720 to cause the transceiver 1730 to send a request to perform a beam prediction task consistent with the capability report.

[0205] like Fig.18 As shown, the device 1805 may include a circuit (circuit 1840) for receiving a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request. For example, the circuit 1840 may enable the device 1805 to receive a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0206] like Fig.18 As shown, the device 1805 may include code (code 1845) stored in the computer-readable medium 1725 for receiving a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request. For example, when the code 1845 is executed by the processor 1720, it may cause the processor 1720 to cause the transceiver 1730 to receive a beam prediction result that meets the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0207] Fig.18 are provided as examples. Other examples can be combined with Fig.18 The examples described are different.

[0208] The following provides an overview of some aspects of the disclosure:

[0209] Aspect 1: A method for wireless communication performed at a device of a user equipment (UE), the method comprising: sending a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy; receiving a request to perform a beam prediction task consistent with the capability report; and sending a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0210] Aspect 2: The method according to aspect 1, wherein the target beam prediction accuracy is associated with an average prediction error.

[0211] Aspect 3: The method according to any one of aspects 1 to 2, wherein the target beam prediction accuracy is associated with a maximum prediction error.

[0212] Aspect 4: The method according to any one of aspects 1 to 3, wherein the target beam prediction accuracy is associated with a standard predefined prediction error.

[0213] Aspect 5: The method according to any one of aspects 1 to 4, wherein the average prediction error, the maximum prediction error, or the standard predefined prediction error is based at least in part on reporting a single value across multiple beams associated with the prediction target beam.

[0214] Aspect 6: The method according to any one of aspects 1 to 5, wherein an average prediction error, a maximum prediction error, or a standard predefined prediction error is based at least in part on reporting multiple values ​​for the multiple beams associated with the predicted target beam.

[0215] Aspect 7: A method according to any one of aspects 1 to 6, wherein the average prediction error is associated with one or more of the following: layer 1 reference signal received power, layer 1 signal to interference plus noise ratio, rank indicator or channel quality indicator.

[0216] Aspect 8: The method according to any one of aspects 1 to 7, wherein the maximum prediction error is associated with one or more of the following: layer 1 reference signal received power, layer 1 signal to interference plus noise ratio, rank indicator or channel quality indicator.

[0217] Aspect 9: A method according to any one of Aspects 1 to 8, wherein the standard predefined prediction error is associated with one or more of the following: layer 1 reference signal received power, layer 1 signal to interference plus noise ratio, rank indicator or channel quality indicator.

[0218] Aspect 10: The method according to any one of aspects 1 to 9, wherein the capability report indicates the number of reference signal resources indicated as measurement resources for beam prediction.

[0219] Aspect 11: The method according to any one of Aspects 1 to 10, wherein the capability report indicates a type of reference signal resource indicated as a measurement resource for beam prediction.

[0220] Aspect 12: The method according to any one of Aspects 1 to 11, wherein the type of reference signal resource indicated as the measurement resource for beam prediction is associated with a reference signal resource having a certain periodicity.

[0221] Aspect 13: The method according to any one of Aspects 1 to 12, wherein the type of reference signal resource indicated as the measurement resource for beam prediction is associated with a reference signal resource having a single port.

[0222] Aspect 14: The method according to any one of Aspects 1 to 13, wherein the type of reference signal resource indicated as the measurement resource for beam prediction is associated with a reference signal resource having multiple ports.

[0223] Aspect 15: The method according to any one of aspects 1 to 14, wherein the capability report indicates the number of historical time-domain measurement opportunities used as input for beam prediction.

[0224] Aspect 16: The method according to any one of aspects 1 to 15, wherein the capability report indicates the interval between adjacent historical time domain opportunities used as input for beam prediction.

[0225] Aspect 17: The method according to any one of Aspects 1 to 16, wherein the capability report indicates the number of beams that are prediction target beams.

[0226] Aspect 18: The method according to any one of Aspects 1 to 17, wherein the capability report indicates the type of beam as the predicted target beam.

[0227] Aspect 19: The method according to any one of Aspects 1 to 18, wherein the type of the beam serving as the prediction target beam is associated with a beam carrying a reference signal resource configured as a measurement resource.

[0228] Aspect 20: The method according to any one of Aspects 1 to 19, wherein the type of the beam serving as the prediction target beam is associated with a beam that does not carry a reference signal resource configured as a measurement resource.

[0229] Aspect 21: The method according to any one of Aspects 1 to 20, wherein the type of the beam serving as the prediction target beam is associated with a beam that is periodically transmitted.

[0230] Aspect 22: The method according to any one of Aspects 1 to 21, wherein the type of the beam serving as the prediction target beam is associated with a beam that is not periodically transmitted.

[0231] Aspect 23: The method according to any one of Aspects 1 to 22, wherein the capability report indicates the number of upcoming time domain opportunities predicted for the beam that is the prediction target beam.

[0232] Aspect 24: The method according to any one of Aspects 1 to 23, wherein the capability report indicates an interval between adjacent upcoming time domain opportunities predicted for a beam that is a prediction target beam.

[0233] Aspect 25: The method according to any one of aspects 1 to 24, wherein sending the capability report comprises sending the capability report via radio resource control signaling during initial access.

[0234] Aspect 26: A method according to any one of Aspects 1 to 25, wherein the capability report indicates one or more combinations of UE capabilities, and the UE capabilities include: target beam prediction accuracy, one or more of the number of reference signal resources indicated as measurement resources or the type of reference signal resources indicated as measurement resources, and one or more of the number of beams as predicted target beams or the type of beams as predicted target beams.

[0235] Aspect 27: The method according to any one of aspects 1 to 26, wherein the capability report indicates one or more combinations of UE capabilities based at least in part on standard pre-definition or pre-configuration.

[0236] Aspect 28: A method according to any one of aspects 1 to 27, wherein the capability report indicates one or more sets of combinations of UE capabilities.

[0237] Aspect 29: The method according to any one of aspects 1 to 28, wherein a set of combinations of UE capabilities from one or more sets of combinations of UE capabilities indicates one or more combinations of UE capabilities.

[0238] Aspect 30: The method according to any one of aspects 1 to 29, wherein a set of combinations of UE capabilities are configured to be activated simultaneously for the UE.

[0239] Aspect 31: According to the method of any one of Aspects 1 to 30, the method further includes: sending an updated capability report indicating the updated beam prediction capability of the UE after initial access.

[0240] Aspect 32: The method according to any one of aspects 1 to 31, wherein the capability report is associated with a first combination of UE capabilities and the updated capability report is associated with a second combination of UE capabilities.

[0241] Aspect 33: A method for wireless communication performed at a device of a network node, the method comprising: receiving a capability report associated with a beam prediction capability of a user equipment (UE), the capability report indicating a target beam prediction accuracy; sending a request to perform a beam prediction task consistent with the capability report; and receiving a beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report based at least in part on the request.

[0242] Aspect 34: The method according to aspect 33, wherein the target beam prediction accuracy is associated with an average prediction error.

[0243] Aspect 35: A method according to any one of aspects 33 to 34, wherein the target beam prediction accuracy is associated with a maximum prediction error.

[0244] Aspect 36: The method according to any one of Aspects 33 to 35, wherein the target beam prediction accuracy is associated with a standard predefined prediction error.

[0245] Aspect 37: A method according to any one of aspects 33 to 36, wherein the average prediction error, the maximum prediction error or the standard predefined prediction error is based at least in part on reporting a single value across multiple beams associated with the prediction target beam.

[0246] Aspect 38: A method according to any one of Aspects 33 to 37, wherein an average prediction error, a maximum prediction error or a standard predefined prediction error is based at least in part on reporting multiple values ​​for the multiple beams associated with the predicted target beam.

[0247] Aspect 39: A method according to any one of Aspects 33 to 38, wherein the average prediction error is associated with one or more of the following: layer 1 reference signal received power, layer 1 signal to interference plus noise ratio, rank indicator or channel quality indicator.

[0248] Aspect 40: A method according to any one of aspects 33 to 39, wherein the maximum prediction error is associated with one or more of the following: layer 1 reference signal received power, layer 1 signal to interference plus noise ratio, rank indicator or channel quality indicator.

[0249] Aspect 41: A method according to any one of Aspects 33 to 40, wherein the standard predefined prediction error is associated with one or more of the following: layer 1 reference signal received power, layer 1 signal to interference plus noise ratio, rank indicator or channel quality indicator.

[0250] Aspect 42: The method according to any one of Aspects 33 to 41, wherein the capability report indicates the number of reference signal resources indicated as measurement resources for beam prediction.

[0251] Aspect 43: The method according to any one of Aspects 33 to 42, wherein the capability report indicates a type of reference signal resource indicated as a measurement resource for beam prediction.

[0252] Aspect 44: The method according to any one of Aspects 33 to 43, wherein the type of reference signal resource indicated as the measurement resource for beam prediction is associated with a reference signal resource having a certain periodicity.

[0253] Aspect 45: The method according to any one of Aspects 33 to 44, wherein the type of reference signal resource indicated as the measurement resource for beam prediction is associated with a reference signal resource having a single port.

[0254] Aspect 46: The method according to any one of Aspects 33 to 45, wherein the type of reference signal resource indicated as the measurement resource for beam prediction is associated with a reference signal resource having multiple ports.

[0255] Aspect 47: A method according to any one of Aspects 33 to 46, wherein the capability report indicates a number of historical time-domain measurement opportunities used as input for beam prediction.

[0256] Aspect 48: A method according to any one of aspects 33 to 47, wherein the capability report indicates the interval between adjacent historical time domain opportunities used as input for beam prediction.

[0257] Aspect 49: A method according to any one of Aspects 33 to 48, wherein the capability report indicates the number of beams that are prediction target beams.

[0258] Aspect 50: A method according to any one of Aspects 33 to 49, wherein the capability report indicates a type of beam as a predicted target beam.

[0259] Aspect 51: The method according to any one of Aspects 33 to 50, wherein the type of the beam serving as the prediction target beam is associated with a beam carrying a reference signal resource configured as a measurement resource.

[0260] Aspect 52: The method according to any one of Aspects 33 to 51, wherein the type of the beam serving as the prediction target beam is associated with a beam that does not carry a reference signal resource configured as a measurement resource.

[0261] Aspect 53: The method according to any one of Aspects 33 to 52, wherein the type of the beam serving as the prediction target beam is associated with a beam that is periodically transmitted.

[0262] Aspect 54: The method according to any one of Aspects 33 to 53, wherein the type of the beam serving as the prediction target beam is associated with a beam that is not periodically transmitted.

[0263] Aspect 55: The method according to any one of Aspects 33 to 54, wherein the capability report indicates the number of upcoming time domain opportunities predicted for the beam that is the prediction target beam.

[0264] Aspect 56: The method according to any one of Aspects 33 to 55, wherein the capability report indicates the interval between adjacent upcoming time domain opportunities predicted for the beam being the prediction target beam.

[0265] Aspect 57: The method according to any one of aspects 33 to 56, wherein receiving the capability report comprises receiving the capability report via radio resource control signaling during initial access.

[0266] Aspect 58: A method according to any one of Aspects 33 to 57, wherein the capability report indicates one or more combinations of UE capabilities, the UE capabilities including: target beam prediction accuracy, one or more of the number of reference signal resources indicated as measurement resources or the type of reference signal resources indicated as measurement resources, and one or more of the number of beams as predicted target beams or the type of beams as predicted target beams.

[0267] Aspect 59: A method according to any one of aspects 33 to 58, wherein the capability report indicates one or more combinations of UE capabilities based at least in part on standard pre-definition or pre-configuration.

[0268] Aspect 60: A method according to any one of aspects 33 to 59, wherein the capability report indicates one or more sets of combinations of UE capabilities.

[0269] Aspect 61: A method according to any one of aspects 33 to 60, wherein a set of combinations of UE capabilities from one or more sets of combinations of UE capabilities indicates one or more combinations of UE capabilities.

[0270] Aspect 62: A method according to any one of aspects 33 to 61, wherein a set of combinations of UE capabilities are configured to be activated simultaneously for the UE.

[0271] Aspect 63: According to the method of any one of Aspects 33 to 62, the method further includes: receiving an updated capability report indicating an updated beam prediction capability of the UE after initial access.

[0272] Aspect 64: A method according to any one of aspects 33 to 63, wherein the capability report is associated with a first combination of UE capabilities and the updated capability report is associated with a second combination of UE capabilities.

[0273] Aspect 65: An apparatus for performing wireless communications at a device, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more of the methods described in Aspects 1 to 32.

[0274] Aspect 66: A device for wireless communication, the device comprising: a memory; and one or more processors, the one or more processors coupled to the memory, the one or more processors configured to execute the method according to one or more of aspects 1 to 32.

[0275] Aspect 67: An apparatus for wireless communication, the apparatus comprising at least one component for performing the method according to one or more of aspects 1 to 32.

[0276] Aspect 68: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform the method according to one or more of aspects 1 to 32.

[0277] Aspect 69: A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising one or more instructions that, when executed by one or more processors of a device, cause the device to perform one or more of the methods described in aspects 1 to 32.

[0278] Aspect 70: An apparatus for performing wireless communications at a device, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more of the methods described in Aspects 33 to 64.

[0279] Aspect 71: A device for wireless communication, the device comprising: a memory; and one or more processors, the one or more processors coupled to the memory, the one or more processors configured to execute the method according to one or more of aspects 33 to 64.

[0280] Aspect 72: An apparatus for wireless communication, the apparatus comprising at least one component for performing the method according to one or more of aspects 33 to 64.

[0281] Aspect 73: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform the method according to one or more of aspects 33 to 64.

[0282] Aspect 74: A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising one or more instructions that, when executed by one or more processors of a device, cause the device to perform one or more of the methods described in aspects 33 to 64.

[0283] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the aspects to the precise forms disclosed. Modifications and variations may be made in light of the above disclosure or may be acquired from practice of the various aspects.

[0284] As used herein, the term "component" is intended to be broadly interpreted as a combination of hardware and / or hardware and software. Whether referred to as software, firmware, middleware, microcode, hardware description language or other names, "software" should be broadly interpreted as meaning instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, processes and / or functions, etc. As used herein, a "processor" is implemented in a combination of hardware and / or hardware and software. It will be apparent that the systems and / or methods described herein can be implemented by a combination of hardware and / or hardware and software in different forms. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit various aspects. Therefore, there is no reference to a specific software code herein to describe the operation and behavior of the system and / or method, because those skilled in the art will understand that software and hardware can be designed to implement the system and / or method based at least in part on the description herein.

[0285] As used herein, "satisfying a threshold" may refer to a value being greater than a threshold, greater than or equal to a threshold, less than a threshold, less than or equal to a threshold, equal to a threshold, not equal to a threshold, etc., depending on the context.

[0286] Although the specific combination of features is set forth in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. Many of these features can be combined in a manner that is not specifically described in the claims and / or is not disclosed in the specification. The disclosure of various aspects includes each dependent claim combined with each other claim in the claim set. As used herein, the phrase "at least one of" the list of items refers to any combination of these items (it includes a single member). As an example, "at least one of a, b or c" is intended to cover a, b, c, a+b, a+c, b+c and a+b+c, and any combination with multiple identical elements (e.g., a+a, a+a+a, a+a+b, a+a+c, a+b+b, a+c+c, b+b, b+b+b, b+b+c, c+c and c+c+c, or any other ordering of a, b and c).

[0287] Any element, action or instruction used herein should not be interpreted as key or necessary, unless explicitly described as such. In addition, as used herein, the article "one" is intended to include one or more items, and can be used interchangeably with "one or more". In addition, as used herein, the article "said" is intended to include one or more items connected to the article "said", and can be used interchangeably with "one or more". In addition, as used herein, the terms "set" and "group" are intended to include one or more items, and can be used interchangeably with "one or more". If only one item is intended to be referred to, the phrase "only one" or similar terms will be used. In addition, as used herein, the terms "have", "have", "have" etc. are intended to be open terms, which do not limit the elements they modify (for example, "an element with" A can also have B). In addition, the phrase "based on" is intended to represent "based at least in part on", unless otherwise explicitly stated. Furthermore, as used herein, the term "or" when used in a series is intended to be open-ended and used interchangeably with "and / or" unless explicitly stated otherwise (e.g., if used in conjunction with "either" or "only one of").

Claims

1. An apparatus for wireless communication at a user equipment (UE), the apparatus comprising: Memory; and one or more processors coupled to the memory and configured to: sending a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy; receiving a request to perform a beam prediction task consistent with the capability report; as well as A beam prediction result that meets the target beam prediction accuracy indicated in the capability report is sent based at least in part on the request.

2. The apparatus of claim 1 , wherein the target beam prediction accuracy is associated with one or more of the following: The average prediction error, The maximum prediction error, or The standard predefined prediction error, Wherein the average prediction error, the maximum prediction error or the standard predefined prediction error is based at least in part on reporting a single value across multiple beams associated with a predicted target beam, or is based at least in part on reporting multiple values ​​for the multiple beams associated with the predicted target beam.

3. The apparatus of claim 2, wherein the average prediction error, the maximum prediction error, and the standard predefined prediction error are each associated with one or more of: Layer 1 reference signal received power, Layer 1 signal to interference plus noise ratio, rank indicator, or Channel Quality Indicator.

4. The apparatus of claim 1 , wherein the capability report indicates one or more of: the number of reference signal resources indicated as measurement resources for beam prediction, or The type of reference signal resource indicated as the measurement resource used for beam prediction.

5. The apparatus of claim 4 , wherein the type of reference signal resource indicated as a measurement resource for beam prediction is associated with one or more of: Reference signal resources with a certain periodicity, A reference signal resource with a single port, or A reference signal resource with multiple ports.

6. The apparatus of claim 4, wherein the capability report indicates one or more of: the number of historical time-domain measurement opportunities used as input to the beam prediction, or The interval between adjacent historical time domain opportunities used as input for the beam prediction.

7. The apparatus of claim 1, wherein the capability report indicates one or more of: The number of beams that are the target beams for prediction, or The type of beam that is the prediction target beam.

8. The apparatus of claim 7, wherein the type of beam indicated as a predicted target beam is associated with one or more of: a beam carrying a reference signal resource configured as a measurement resource, beams that do not carry reference signal resources configured as measurement resources, A beam that is transmitted periodically, or A beam that is not transmitted periodically.

9. The apparatus of claim 7, wherein the capability report indicates one or more of: the number of upcoming time domain opportunities predicted for the beam being the prediction target beam, or The interval between adjacent upcoming time domain opportunities predicted for the beam as the prediction target beam.

10. The apparatus of claim 1, wherein to send the capability report, the one or more processors are configured to send the capability report via radio resource control signaling during initial access.

11. The apparatus of claim 1 , wherein the capability report indicates one or more combinations of UE capabilities, the UE capabilities comprising: Target beam prediction accuracy, one or more of a number of reference signal resources indicated as measurement resources or a type of reference signal resources indicated as measurement resources, and one or more of the number of beams indicated as predicted target beams or the type of beams indicated as predicted target beams, The capability report indicates the one or more combinations of UE capabilities based at least in part on standard pre-definition or pre-configuration.

12. The apparatus of claim 11, wherein the capability report indicates one or more sets of combinations of UE capabilities, wherein a set of combinations of UE capabilities from the one or more sets of combinations of UE capabilities indicates the one or more combinations of UE capabilities, and wherein the sets of combinations of UE capabilities are configured to be activated simultaneously for the UE.

13. The apparatus of claim 1, wherein the one or more processors are further configured to: An updated capability report indicating updated beam prediction capabilities of the UE is sent after initial access, wherein the capability report is associated with a first combination of UE capabilities and the updated capability report is associated with a second combination of UE capabilities.

14. An apparatus for wireless communication at a network node, the apparatus comprising: Memory; and one or more processors coupled to the memory and configured to: receiving a capability report associated with a beam prediction capability of a user equipment (UE), the capability report indicating a target beam prediction accuracy; sending a request to perform a beam prediction task consistent with the capability report; as well as A beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report is received based at least in part on the request.

15. The apparatus of claim 14, wherein the target beam prediction accuracy is associated with one or more of: The average prediction error, The maximum prediction error, or The standard predefined prediction error, Wherein the average prediction error, the maximum prediction error or the standard predefined prediction error is based at least in part on reporting a single value across multiple beams associated with a predicted target beam, or is based at least in part on reporting multiple values ​​for the multiple beams associated with the predicted target beam.

16. The apparatus of claim 15, wherein the average prediction error, the maximum prediction error, and the standard predefined prediction error are each associated with one or more of: Layer 1 reference signal received power, Layer 1 signal to interference plus noise ratio, rank indicator, or Channel Quality Indicator.

17. The apparatus of claim 14, wherein the capability report indicates one or more of: the number of reference signal resources indicated as measurement resources for beam prediction, or The type of reference signal resource indicated as the measurement resource used for beam prediction.

18. The apparatus of claim 17, wherein the type of reference signal resources indicated as measurement resources for beam prediction is associated with one or more of: Reference signal resources with a certain periodicity, A reference signal resource with a single port, or A reference signal resource with multiple ports.

19. The apparatus of claim 17, wherein the capability report indicates one or more of: the number of historical time-domain measurement opportunities used as input to the beam prediction, or The interval between adjacent historical time domain opportunities used as input for the beam prediction.

20. The apparatus of claim 14, wherein the capability report indicates one or more of: The number of beams that are the target beams for prediction, or The type of beam that is the prediction target beam.

21. The apparatus of claim 20, wherein the type of beam indicated as a predicted target beam is associated with one or more of: a beam carrying a reference signal resource configured as a measurement resource, beams that do not carry reference signal resources configured as measurement resources, A beam that is transmitted periodically, or A beam that is not transmitted periodically.

22. An apparatus according to claim 20, wherein the capability report indicates one or more of the following: the number of upcoming time domain opportunities predicted for the beam as the prediction target beam or the interval between adjacent upcoming time domain opportunities predicted for the beam as the prediction target beam.

23. The apparatus of claim 14, wherein to receive the capability report, the one or more processors are configured to receive the capability report via radio resource control signaling during initial access.

24. The apparatus of claim 14, wherein the capability report indicates one or more combinations of UE capabilities, the UE capabilities comprising: Target beam prediction accuracy, one or more of a number of reference signal resources indicated as measurement resources or a type of reference signal resources indicated as measurement resources, and one or more of the number of beams that are prediction target beams or the type of beams that are prediction target beams, The capability report indicates the one or more combinations of UE capabilities based at least in part on standard pre-definition or pre-configuration.

25. An apparatus according to claim 24, wherein the capability report indicates one or more sets of combinations of UE capabilities, wherein a set of combinations of UE capabilities from the one or more sets of combinations of UE capabilities indicates the one or more combinations of UE capabilities, and wherein the sets of combinations of UE capabilities are configured to be activated simultaneously for the UE.

26. The apparatus of claim 14, wherein the one or more processors are further configured to: An updated capability report indicating updated beam prediction capabilities of the UE is received after initial access, wherein the capability report is associated with a first combination of UE capabilities and the updated capability report is associated with a second combination of UE capabilities.

27. A method of wireless communication performed at a device of a user equipment (UE), the method comprising: sending a capability report associated with a beam prediction capability of the UE, the capability report indicating a target beam prediction accuracy; receiving a request to perform a beam prediction task consistent with the capability report; as well as A beam prediction result that meets the target beam prediction accuracy indicated in the capability report is sent based at least in part on the request.

28. The method of claim 27, wherein: The target beam prediction accuracy is associated with one or more of an average prediction error, a maximum prediction error, or a standard predefined prediction error; The capability report indicates one or more of the number of reference signal resources indicated as measurement resources for beam prediction or the type of reference signal resources indicated as measurement resources for beam prediction; or The capability report indicates one or more of the number of beams that are predicted target beams or the type of beams that are predicted target beams.

29. A method of wireless communication performed at an apparatus of a network node, the method comprising: receiving a capability report associated with a beam prediction capability of a user equipment, the capability report indicating a target beam prediction accuracy, sending a request to perform a beam prediction task consistent with the capability report, and A beam prediction result that satisfies the target beam prediction accuracy indicated in the capability report is received based at least in part on the request.

30. The method of claim 29, wherein: The target beam prediction accuracy is associated with one or more of an average prediction error, a maximum prediction error, or a standard predefined prediction error; The capability report indicates one or more of the number of reference signal resources indicated as measurement resources for beam prediction or the type of reference signal resources indicated as measurement resources for beam prediction; or The capability report indicates one or more of the number of beams that are predicted target beams or the type of beams that are predicted target beams.

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