User equipment features for beam prediction

Abstract

Various aspects of the present disclosure generally relate to wireless communications. In some aspects, a user equipment (UE) may transmit information indicating whether the UE supports a set of features for beam prediction based on artificial intelligence or machine learning (AI / ML). The UE may perform communications based at least in part on the information indicating whether the UE supports the set of features. Numerous other aspects are described.

Description

Technical Field

[0001] Aspects of the present disclosure relate generally to wireless communications and to techniques and apparatus for user equipment characteristic signaling for beam prediction. Background Art

[0002] Wireless communication systems are widely deployed to provide a variety of telecommunication services, such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems may employ multiple access technologies capable of supporting 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 communication for wireless communication devices, such as user equipment (UE) or multiple UEs. The UE may communicate with the network node via downlink and uplink communications. A "downlink" (or "DL") refers to the communication link from the network node to the UE, and an "uplink" (or "UL") refers to the communication link from the UE to the network node. Some wireless networks may support device-to-device communication, such as via a local link (e.g., a sidelink (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, regional, 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 better integrating with other open standards by using orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) (CP-OFDM) on the downlink and 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; as well as 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] Some aspects described herein relate to a method of wireless communication performed by a user equipment (UE). The method may include sending information indicating whether the UE supports a feature set for artificial intelligence or machine learning (AI / ML)-based beam prediction. The method may include performing communication based at least in part on the information indicating whether the UE supports the feature set.

[0006] Some aspects described herein relate to a method of wireless communication performed by a network node. The method may include obtaining information indicating whether a UE supports a feature set for AI / ML-based beam prediction. The method may include performing communication based at least in part on the information indicating whether the UE supports the feature set.

[0007] Some aspects described herein relate to a UE for wireless communication. The UE may include: a memory; and one or more processors coupled to the memory. The one or more processors may be configured to send information indicating whether the UE supports a feature set for AI / ML-based beam prediction. The one or more processors may be configured to perform communication based at least in part on the information indicating whether the UE supports the feature set.

[0008] Some aspects described herein relate to a network node for wireless communication. The network node may include: a memory; and one or more processors coupled to the memory. The one or more processors may be configured to obtain information indicating whether a UE supports a feature set for AI / ML-based beam prediction. The one or more processors may be configured to perform communication based at least in part on the information indicating whether the UE supports the feature set.

[0009] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a UE. The instruction set, when executed by one or more processors of the UE, may cause the UE to send information indicating whether the UE supports a feature set for AI / ML-based beam prediction. The instruction set, when executed by one or more processors of the UE, may cause the UE to perform communications based at least in part on the information indicating whether the UE supports the feature set.

[0010] Some aspects described herein relate to a non-transitory computer-readable medium storing a set of instructions for wireless communication by a network node. The set of instructions, when executed by one or more processors of the network node, may cause the network node to obtain information indicating whether a UE supports a feature set for AI / ML-based beam prediction. The set of instructions, when executed by one or more processors of the network node, may cause the network node to perform communications based at least in part on the information indicating whether the UE supports the feature set.

[0011] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for transmitting information indicating whether the apparatus supports a feature set for AI / ML-based beam prediction. The apparatus may include means for performing communication based at least in part on the information indicating whether the apparatus supports the feature set.

[0012] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for obtaining information indicating whether a UE supports a feature set for AI / ML-based beam prediction. The apparatus may include means for performing communication based at least in part on the information indicating whether the UE supports the feature set.

[0013] The various aspects collectively include methods, apparatus, 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 herein with reference to and as illustrated in the accompanying drawings and appendices.

[0014] The features and technical advantages of the examples according to the present disclosure have been outlined quite broadly above so that the detailed description that follows may be better understood. Additional features and advantages will be described below. The concepts and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for achieving the same purposes of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. The characteristics of the concepts disclosed herein, both in terms of their organization and method of operation, and the associated advantages will be better understood by considering the following description in conjunction with the accompanying drawings. Each of the figures in the accompanying drawings is provided for the purpose of illustration and description and not as a definition of limitations of the claims.

[0015] Although various aspects are described in this disclosure by illustrating some examples, it will be understood by those skilled in the art that such aspects can be implemented in many different arrangements and scenarios. The technology described herein can be implemented using different platform types, devices, systems, shapes, sizes and / or packaging arrangements. For example, some aspects can be implemented via integrated chip implementations or other devices based on non-module components (e.g., end-user devices, vehicles, communication equipment, computing equipment, industrial equipment, retail / shopping equipment, medical equipment and / or artificial intelligence devices). Various aspects can be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components and / or system-level components. The equipment incorporating the various aspects and features described may include additional components and features for implementing and practicing the various aspects claimed and described. For example, the transmission and reception of wireless signals may include one or more components (e.g., hardware components, including antennas, radio frequency chains, power amplifiers, modulators, buffers, processors, interleavers, adders and / or summers) for analog and digital purposes. The various aspects described herein are intended to be practiced in various devices, components, systems, distributed arrangements and / or end-user devices of various sizes, shapes and compositions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to enable a detailed understanding of the above-described features of the present disclosure, a more particular description, briefly summarized above, may be obtained by reference to various aspects, some of which are illustrated in the accompanying drawings. It should be noted, however, that the drawings illustrate only certain typical aspects of the present disclosure and are not therefore to be considered limiting of its scope, as the description may admit to other equally effective aspects. The same reference numerals in different drawings may identify the same or similar elements.

[0017] Figure 1 is a diagram illustrating an example of a wireless network according to the present disclosure.

[0018] 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.

[0019] Figure 3 is a diagram illustrating an example decomposed base station architecture according to the present disclosure.

[0020] Figure 4 is a diagram illustrating an example of a beam management process according to the present disclosure.

[0021] Figure 5 is a diagram illustrating an example of beam management according to the present disclosure.

[0022] Figure 6 is a diagram illustrating an example of signaling regarding features of beam management based on artificial intelligence or machine learning according to the present disclosure.

[0023] Figure 7 is a diagram illustrating an example process performed, for example, by a UE according to the present disclosure.

[0024] Figure 8 is a diagram illustrating an example process, for example, performed by a network node, according to the present disclosure.

[0025] Figure 9 is a diagram of an example apparatus for wireless communications according to the present disclosure.

[0026] Figure 10 is a diagram of an example apparatus for wireless communications according to the present disclosure. DETAILED DESCRIPTION

[0027] A user equipment (UE) may support artificial intelligence (AI) and / or machine learning (ML) (AI / ML)-based beam prediction. In AI / ML-based beam prediction, the UE may use a set of ML models to predict properties of a second set of beams based, at least in part, on parameters of a first set of beams. For example, in a process known as predictive beam management, AI / ML-based beam prediction may be used to increase the number of beams that can be selected for beam management operations. AI / ML-based beam prediction may be performed in the spatial domain (where measurements of the first set of beams are used to predict parameters of the second set of beams), in the temporal domain (where historical measurements of either the first set of beams or the second set of beams are used to predict parameters of the second set of beams), or in a combination thereof. Lifecycle management (LCM) of AI / ML-based beam prediction may be functionality-based (where the network is aware of the AI / ML functionality at the UE and controls the AI / ML functionality, rather than the underlying AI / ML model used to perform the AI / ML functionality) or model identifier (ID)-based (where the AI / ML model is registered with the network via a model ID and controls the AI / ML model at the UE). Different UEs may support different AI / ML functionalities for AI / ML-based beam prediction (e.g., for LCM based on functionality) due to, for example, available computational or power resources at the UE, the beamforming capabilities of the UE, the specific AI / ML model or functionality implemented at the UE, whether the UE supports time-domain and / or spatial-domain beam prediction, etc. The AI / ML functionality for AI / ML-based beam prediction may affect the configuration of communications between the UE and the network, such as the configuration of reference signals for beam management. However, the network may not be aware of the AI / ML functionality supported by the UE (e.g., features for AI / ML-based beam prediction). If the network is unaware of the AI / ML functionality supported by the UE, the network may configure communications (such as reference signal configuration) in a manner that does not properly account for the AI / ML functionality, resulting in inefficient operation of the AI / ML model and inaccurate or suboptimal beam management.

[0028] Some of the techniques described herein provide signaling of features supported by a UE for AI / ML-based beam prediction. For example, a UE may send information indicating whether the UE supports a feature set for AI / ML-based beam prediction, and may perform communication (e.g., with a network node) based at least in part on the information indicating whether the UE supports the feature set. Thus, the network node may be made aware of the AI / ML functionality supported by the UE, which enables the network to configure communications (such as reference signal configuration) in a manner that takes the AI / ML functionality into account, thereby resulting in more efficient operation of the AI / ML model and improved accuracy of beam management.

[0029] 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 interpreted 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 will 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 implemented 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 devices or methods that are 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 claims.

[0030] 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, "elements"). These elements can be implemented using hardware, software, or a combination thereof. Whether these elements are implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system.

[0031] Although various aspects may be described herein using terminology 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 post-5G (e.g., 6G) RATs.

[0032] Figure 11 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 (illustrated as network node 110a, network node 110b, network node 110c, and network node 110d), one or more UEs 120 (illustrated 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, meaning 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 disaggregated network node (sometimes referred to as a disaggregated base station), meaning 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)).

[0033] In some examples, network node 110 is or includes a network node (such as an RU) that communicates with UE 120 via a radio access link. In some examples, network node 110 is or includes a network node (such as a DU) that communicates with other network nodes 110 via a fronthaul link or a midhaul link. In some examples, network node 110 is or includes a network node (such as a CU) that communicates with other network nodes 110 via a midhaul link or communicates with a core network via a backhaul link. In some examples, network node 110 (such as a converged 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. 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, an RU, a CU, a mobility element of a network, a core network node, a network element, 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 using any suitable transport network via various types of fronthaul interfaces, midhaul interfaces, and / or backhaul interfaces, such as direct physical connections, air interfaces, or virtual networks.

[0034] In some examples, 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 ​​network node 110 and / or a network node subsystem serving that coverage area, depending on the context in which the term is used. Network node 110 may provide communication coverage for a macrocell, a picocell, a femtocell, and / or another type of cell. A macrocell may cover a relatively large geographic area (e.g., a radius of several kilometers) and may allow unrestricted access by UEs 120 with service subscriptions. A picocell may cover a relatively small geographic area and may allow unrestricted access by UEs 120 with service subscriptions. A femtocell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by UEs 120 associated with the femtocell (e.g., UEs 120 in a closed subscriber group (CSG)). A network node 110 for a macrocell may be referred to as a macro network node. A network node 110 for a picocell 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 1 In the example shown, 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, the cells may not necessarily be stationary, and the geographic area of ​​the cells may move depending on the location of a mobile network node 110 (e.g., a mobile network node).

[0035] In some aspects, the term "base station" or "network node" may refer to a converged 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 multiple different devices (which may be located in the same geographic location or 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.

[0036] 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 can relay transmissions for other UEs 120. Figure 1 In the example shown in , a network node 110 d (e.g., a relay network node) may communicate with a network node 110 a (e.g., a macro network node) and a UE 120 d to facilitate communications between the network node 110 a and the UE 120 d. 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.

[0037] 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).

[0038] 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 backhaul communication link or a wired backhaul communication link. In some aspects, the network controller 130 may be or may include a CU or a core network device.

[0039] UEs 120 may be dispersed throughout 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 smartphone), 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 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, 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.

[0040] Some UEs 120 may be considered 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 can communicate with a network node, another device (e.g., a remote device), or some other entity. Some UEs 120 may be considered Internet of Things (IoT) devices and / or may be implemented as NB-IoT (narrowband IoT) devices. Some UEs 120 may be considered customer premises equipment. The UE 120 may be included within a housing that houses components of the 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, the processor component (e.g., one or more processors) and the memory component (e.g., memory) may be operatively coupled, communicatively coupled, electronically coupled, and / or electrically coupled.

[0041] Generally speaking, 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. A RAT may be referred to as a radio technology, air interface, etc. A 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 some cases, NR or 5G RAT networks may be deployed.

[0042] In some examples, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) can 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 can 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 can perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by network node 110.

[0043] The devices of the wireless network 100 can communicate using an electromagnetic spectrum, which can be subdivided into various categories, bands, channels, etc. based on frequency or wavelength. For example, the devices of the wireless network 100 can communicate using one or more operating bands. In 5G NR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz to 7.125 GHz) and FR2 (24.25 GHz to 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 it is different from the extremely high frequency (EHF) band (30 GHz to 300 GHz) identified as the "millimeter wave" band by the International Telecommunication Union (ITU).

[0044] Frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR research has identified the operating band for these mid-band frequencies as frequency range designation FR3 (7.125 GHz to 24.25 GHz). The frequency bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, thus effectively extending the features of FR1 and / or FR2 to mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operation to more than 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6 GHz to 71 GHz), FR4 (52.6 GHz to 114.25 GHz), and FR5 (114.25 GHz to 300 GHz). Each of these higher frequency bands falls within the EHF band.

[0045] With the above examples in mind, unless otherwise specifically stated, it should be understood that if the term "sub-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. Additionally, 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, 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.

[0046] In some aspects, UE 120 may include a communication manager 140. As described in greater detail elsewhere herein, communication manager 140 may transmit information indicating whether the UE supports a feature set for AI / ML-based beam prediction, and perform communications based at least in part on the information indicating whether the UE supports the feature set. Additionally or alternatively, communication manager 140 may perform one or more other operations described herein.

[0047] In some aspects, the network node 110 may include a communication manager 150. As described in greater detail elsewhere herein, the communication manager 150 may obtain information indicating whether a UE supports a feature set for AI / ML-based beam prediction; and perform communications based at least in part on the information indicating whether the UE supports the feature set. Additionally or alternatively, the communication manager 150 may perform one or more other operations described herein.

[0048] As indicated above, Figure 1 are provided as examples. Other examples can be found in the Figure 1The examples described are different.

[0049] Figure 2 2 is a diagram illustrating 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 through 234t, such as T antennas (T ≥ 1). The UE 120 may be equipped with a set of antennas 252a through 252r, such as R antennas (R ≥ 1). The network node 110 of example 200 includes one or more radio frequency components, such as antennas 234 and a modem 232. 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 radio frequency components, such as one or more CUs or one or more DUs, that facilitate direct communication with the UE 120.

[0050] At network node 110, transmit processor 220 may receive data intended for UE 120 (or a group of UEs 120) from data source 212. Transmit processor 220 may select one or more modulation and coding schemes (MCS) for UE 120 based at least in part on one or more channel quality indicators (CQIs) received from UE 120. Network node 110 may process (e.g., encode and modulate) the data for UE 120 based at least in part on the MCS selected for UE 120 and may provide data symbols for UE 120. 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. 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, as 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 through 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 frequency upconvert) the output sample stream using a corresponding modulator component to obtain a downlink signal. The modems 232a through 232t may transmit the set of downlink signals (e.g., T downlink signals) via a corresponding set of antennas 234 (e.g., T antennas) (shown as antennas 234a through 234t).

[0051] At the UE 120, a set of antennas 252 (shown as antennas 252a through 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 through 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 the 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, if 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 a 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, among other things, 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. In some examples, one or more components of the UE 120 may be included in a housing 284.

[0052] 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.

[0053] 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. The antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include one or more antenna elements (within a single housing or multiple housings), sets of coplanar antenna elements, sets of non-coplanar antenna elements, and / or 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.

[0054] On the uplink, at the UE 120, a transmit processor 264 may receive and process data from a data source 262 and control information (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI) from the controller / processor 280. 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, if applicable, further processed by the modem 254 (e.g., for DFT-s-OFDM or CP-OFDM), and transmitted 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 execute the instructions herein (eg, reference Figures 4 to 10 ) any aspects of any of the methods described.

[0055] At network node 110, uplink signals from UE 120 and / or other UEs may be received by antenna 234, processed by modem 232 (e.g., a demodulator component (shown as DEMOD) of modem 232), detected by MIMO detector 236 (if applicable), and further processed by receive processor 238 to obtain decoded data and control information transmitted by UE 120. Receive processor 238 may provide the decoded data to a data sink 239 and the decoded control information to controller / processor 240. Network node 110 may include a communication unit 244 and may communicate with network controller 130 via communication unit 244. Network node 110 may include a scheduler 246 to schedule one or more UEs 120 for downlink and / or uplink communications. In some examples, modem 232 of network node 110 may include a modulator and a demodulator. In some examples, network node 110 includes a transceiver. The transceiver may include any combination of antenna 234, modem 232, MIMO detector 236, receive processor 238, transmit processor 220, and / or TX MIMO processor 230. The transceiver may be used by a processor (e.g., controller / processor 240) and memory 242 to execute the instructions herein (e.g., reference 242). Figures 4 to 10 ) any aspects of any of the methods described.

[0056] 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 may perform one or more techniques associated with AI / ML-based beam prediction, 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 may perform or direct e.g. Figure 7 The process of 700 Figure 8 800 and / or 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 (e.g., directly or after compilation, conversion, and / or interpretation) by one or more processors of network node 110 and / or UE 120, may cause the one or more processors, UE 120, and / or network node 110 to perform or direct, for example, Figure 7 The process of 700 Figure 8 The process 800 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.

[0057] In some aspects, the UE 120 includes means for transmitting information indicating whether the UE supports a feature set for AI / ML-based beam prediction and / or means for performing communications based at least in part on the information indicating whether the UE supports the feature set. Means for the UE 120 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.

[0058] In some aspects, the network node 110 includes means for obtaining information indicating whether the UE supports a feature set for AI / ML-based beam prediction; and / or means for performing communications based at least in part on the information indicating whether the UE supports the feature set. Means for the network node 110 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.

[0059] Although Figure 2 The blocks in FIG. 2 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.

[0060] As indicated above, Figure 2 are provided as examples. Other examples can be found in the Figure 2 The examples described are different.

[0061] The deployment of a communication system (such as a 5G NR system) can be arranged with various components or constituent parts in a variety of ways. In a 5G NR system or network, a network node, a network entity, a mobility element of the 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 base station, a 5G NB, an access point (AP), a TRP or a cell, etc.) or one or more units (or one or more components) performing base station functionality can be implemented as an aggregated base station (also known as an independent base station or a monolithic base station) or a decomposed base station. A "network entity" or a "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).

[0062] A converged base station (e.g., a converged 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, the 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 spread across one or more other network nodes. The DU may be implemented to communicate with one or more RUs. Each of the CU, DU, and 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), among others.

[0063] Base station type operation or network design may take into account the aggregated nature of base station functionality. For example, a disaggregated 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 scaling of the communication system by separating base station functionality into one or more units that can be deployed separately. A disaggregated 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. Various units of the disaggregated base station may be configured for wired or wireless communication with at least one other unit of the disaggregated base station.

[0064] Figure 3 FIG2 is a diagram illustrating an example decomposed base station architecture 300 according to the present disclosure. The decomposed 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 decomposed control units (such as a near-RT RIC 325 via an E2 link, 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.

[0065] Each of the units (including the CU 310, DU 330, RU 340) and the near-RT RIC 325, the non-RT RIC 315, and the SMO framework 305 may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, 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 the transmission medium. In some examples, each of the units may include a wired interface configured to receive signals or transmit signals to one or more of the other units via a wired transmission medium, and a wireless interface that may include a receiver, a transmitter, or a transceiver (such as an RF transceiver) configured to receive signals or transmit signals to one or more of the other units via a wireless transmission medium, or both.

[0066] 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, among others. 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 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.

[0067] 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, a DU 330 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more higher physical (PHY) layers, at least in part according to a functional split (such as that defined by 3GPP). In some aspects, the one or more higher 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, a DU 330 may also host one or more lower PHY layers, such as those implemented by one or more modules for fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, or physical random access channel (PRACH) extraction and filtering. Each layer (which may also be referred to as a module) may be implemented using an interface configured to communicate signals with other layers (and modules) hosted by the DU 330 or with control functions hosted by the CU 310.

[0068] Each RU 340 may implement low-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 low-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 communications 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).

[0069] The SMO framework 305 can be configured to support RAN deployment and provisioning of both non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO framework 305 can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which can be managed via an operations and maintenance interface (such as the O1 interface). For virtualized network elements, the SMO framework 305 can be configured to interact with a cloud computing platform (such as the 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 the O2 interface). Such virtualized network elements can include, but are not limited to, CU 310, DU 330, RU 340, non-RT RIC 315, and near-RT RIC 325. In some implementations, the SMO framework 305 can communicate with hardware aspects of the 4G RAN (such as the 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.

[0070] The non-RT RIC 315 can be configured to include logic that enables 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 can be coupled to or in communication with the near-RT RIC 325 (such as via an A1 interface). The near-RT RIC 325 can be configured to include logic that enables near-real-time control and optimization of RAN elements and resources through data collection and actions over 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.

[0071] In some implementations, the non-RT RIC 315 can receive parameters or external enrichment information from an external server to generate an AI / ML model to be deployed in the near-RT RIC 325. Such information can be utilized by the near-RT RIC 325 and can be received from non-network data sources or from network functions 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 can be configured to tune RAN behavior or performance. For example, the non-RT RIC 315 can monitor long-term trends and patterns in performance and employ AI / ML models to perform corrective actions through the SMO framework 305 (such as via reconfiguration of the O1 interface) or through the creation of RAN management policies (such as A1 interface policies).

[0072] As indicated above, Figure 3 are provided as examples. Other examples can be found in the Figure 3 The examples described are different.

[0073] Figure 4 4 are diagrams illustrating examples 400, 410, and 420 of a beam management process according to the present disclosure. Figure 4 As shown, examples 400, 410, and 420 include UE 120 communicating with network node 110 in a wireless network (e.g., wireless network 100). Figure 4 The devices shown are provided as examples, and the wireless network may support communication and beam management between other devices (e.g., between UE 120 and network node 110 or TRP, between mobile terminal nodes and control nodes, between IAB child nodes and IAB parent nodes, and / or between scheduled nodes and scheduling nodes). In some aspects, UE 120 and network node 110 may be in a connected state (e.g., an RRC connected state).

[0074] like Figure 4 As shown, example 400 may include network node 110 and UE 120 communicating to perform beam management using a channel state information reference signal (CSI-RS). Example 400 depicts a first beam management procedure (e.g., P1 CSI-RS beam management). The first beam management procedure may be referred to as a beam selection procedure, an initial beam acquisition procedure, a beam scanning procedure, a cell search procedure, and / or a beam search procedure. Figure 4 As shown in example 400, a CSI-RS may be configured to be sent from a network node 110 to a UE 120. The CSI-RS may be configured to be periodic (e.g., using RRC signaling), semi-persistent (e.g., using MAC control element (MAC-CE) signaling), and / or aperiodic (e.g., using downlink control information (DCI)).

[0075] The first beam management process may include network node 110 performing beam scanning on multiple transmit (Tx) beams. Network node 110 may transmit a CSI-RS using each of the multiple transmit beams used for beam management. To enable UE 120 to perform receive (Rx) beam scanning, network node 110 may transmit each CSI-RS multiple times (e.g., using repetitions) within the same RS resource set using the transmit beam, so that UE 120 can sweep the receive beam in multiple transmission instances. For example, if network node 110 has a set of N transmit beams and UE 120 has a set of M receive beams, CSI-RS may be transmitted M times on each of the N transmit beams, so that UE 120 can receive M instances of CSI-RS per transmit beam. In other words, for each transmit beam of network node 110, UE 120 may perform beam scanning on its receive beam. Thus, the first beam management procedure may enable UE 120 to measure CSI-RS on different transmit beams using different receive beams to support selection of a network node 110 transmit beam / UE 120 receive beam pair. UE 120 may report the measurements to network node 110 to enable network node 110 to select one or more beam pairs for communication between network node 110 and UE 120. Although example 400 has been described in conjunction with CSI-RS, the first beam management procedure may also use synchronization signal blocks (SSBs) to perform beam management in a similar manner as described above.

[0076] like Figure 4 As shown, example 410 may include network node 110 and UE 120 communicating to perform beam management using CSI-RS. Example 410 depicts a second beam management procedure (e.g., P2 CSI-RS beam management). This second beam management procedure may be referred to as a beam refinement procedure, a network node beam refinement procedure, a TRP beam refinement procedure, and / or a transmit beam refinement procedure. Figure 4As shown in example 410, a CSI-RS may be configured to be transmitted from network node 110 to UE 120. The CSI-RS may be configured to be aperiodic (e.g., using DCI). The second beam management procedure may include network node 110 performing beam scanning on one or more transmit beams. The one or more transmit beams may be a subset of all transmit beams associated with network node 110 (e.g., determined at least in part based on measurements reported by UE 120 in conjunction with the first beam management procedure). Network node 110 may transmit a CSI-RS using each of the one or more transmit beams used for beam management. UE 120 may measure each CSI-RS using a single (e.g., identical) receive beam (e.g., determined at least in part based on measurements performed in conjunction with the first beam management procedure). The second beam management procedure may enable network node 110 to select an optimal transmit beam based at least in part on the CSI-RS measurements reported by UE 120 (e.g., measured by UE 120 using a single receive beam).

[0077] like Figure 4 As shown, example 420 depicts a third beam management process (e.g., P3 CSI-RS beam management). This third beam management process may be referred to as a beam refinement process, a UE beam refinement process, and / or a receive beam refinement process. Figure 4 As shown in Example 420, one or more CSI-RSs may be configured to be transmitted from the network node 110 to the UE 120. The CSI-RS may be configured to be aperiodic (e.g., using DCI). The third beam management procedure may include the network node 110 transmitting the one or more CSI-RSs using a single transmit beam (e.g., determined at least in part based on measurements reported by the UE 120 in conjunction with the first beam management procedure and / or the second beam management procedure). To enable the UE 120 to perform receive beam scanning, the network node 110 may transmit the CSI-RS multiple times (e.g., using repetitions) within the same RS resource set using the transmit beam, such that the UE 120 may sweep across the one or more receive beams in multiple transmission instances. The one or more receive beams may be a subset of all receive beams associated with the UE 120 (e.g., determined at least in part based on measurements performed in conjunction with the first beam management procedure and / or the second beam management procedure). The third beam management process may enable the network node 110 and / or UE 120 to select the best receive beam based at least in part on reported measurements received from the UE 120 (e.g., reported measurements of the CSI-RS of the transmit beam using the one or more receive beams).

[0078] As indicated above, Figure 4 is provided as an example of a beam management process. Other examples of beam management processes can be found in the Figure 4 For example, the UE 120 and the network node 110 may perform the third beam management procedure before performing the second beam management procedure, and / or the UE 120 and the network node 110 may perform a similar beam management procedure to select a UE transmit beam.

[0079] Figure 5 is a diagram illustrating an example 500 of beam management according to the present disclosure.

[0080] like Figure 5 As shown, the UE may initially be in an RRC idle state or an RRC inactive state. The UE may perform initial access and may perform beam management after entering an RRC connected state due to the initial access. Beam management may include P1, P2, and / or P3 beam management procedures, as described herein. The UE may also perform beam management using an AI / ML-based approach. The UE may perform beam failure detection (BFD), and the UE may perform beam failure recovery (BFR) based at least in part on BFD. When BFR is unsuccessful, the UE may declare a radio link failure (RLF).

[0081] As indicated above, Figure 5 are provided as examples. Other examples can be found in the Figure 5 The examples described are different.

[0082] The network node may include an ML component. The ML component may include one or more ML models for facilitating wireless communication tasks. For example, the ML model may be used to facilitate determining parameter values ​​associated with measurements. The ML model may be used to estimate a set of parameters (e.g., interference and / or channel state information (CSI), etc.) on current and / or future resources based on a common set of inputs (e.g., signal measurements). For example, the ML model may jointly estimate interference and CSI on future resources using the same input CSI-RS. In another example, the ML model may use the same input measurements to estimate interference across multiple future time slots and / or symbols.

[0083] In some cases, in order to develop a machine learning model for an ML component, the UE may collect data and provide the collected data to the ML component. The ML component may implement a functional framework for developing an ML model. The functional framework may include a data collection function, a model training function, a model inference function, and an actor function. The data collection function may provide training data as input data to the model training function and provide inference data as input to the model inference function. Examples of input data may include measurements from network nodes, feedback from the actor function, and / or output from the ML model. In some cases, the data collection function may collect and provide data. For example, in some cases, the data collection function may be configured so that ML algorithm-specific data preparation (e.g., data preprocessing, data cleaning, data formatting and / or transformation, etc.) is not performed by the data collection function.

[0084] The model training function may perform ML model training, validation and / or testing, etc. The model training function may also perform data preparation (e.g., data preprocessing, data cleaning, data formatting and / or transformation, etc.) based on the training data delivered by the data collection function. The model training function may deploy the ML model to the model inference function, monitor the ML model and / or deploy updates to the ML model. The model inference function may provide ML model inference output (e.g., prediction, classification, estimation and / or decision, etc.). In some cases, the model inference function may provide model performance feedback to the model training function. The model inference function may also perform data preparation (e.g., data preprocessing, data cleaning, data formatting and / or transformation, etc.) based on the inference data delivered by the data collection function. The actor function may receive output from the model inference function and perform one or more wireless communication tasks based on the output. The actor function may provide feedback, which may be stored by the data collection function for use as training data and / or inference data.

[0085] AI / ML-based predictive beam management (also referred to herein as AI / ML-based beam prediction) may involve beam management using AI / ML. One problem with traditional beam management procedures is that beam quality / failure is identified via measurement, which may require power / overhead to achieve good performance. In addition, 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 domain (SD), time domain (TD), and / or frequency domain (FD), 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 UE's moving speed / trajectory, the Rx beam used or to be used, and / or interference, which may be difficult to model via conventional statistical signaling processing techniques.

[0086] AI / ML-based predictive beam management may involve prediction of beams at the UE or at the network node via AI / ML, which may involve a trade-off between performance and UE power consumption. To predict future DL-Tx beam quality, the UE may have more observations (via measurements) than the network node (which may obtain the UE's observations 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 an enhanced air interface or via application layer methods. For model training at the UE, model training and data storage may require additional UE computation / buffering effort.

[0087] A network node and / or UE may perform AI / ML-based SD beam prediction / selection. Layer 1 RSRP (L1-RSRP) measurements may be reported by the UE, or the L1-RSRP measurements may be measured by the UE. The L1-RSRP measurements may be associated with SD compressed beam measurements. The L1-RSRP measurements reported by the UE may be used to perform inferences at the network node. The L1-RSRP measurements measured by the UE may be used to perform inferences at the UE. AI / ML-based SD beam prediction / selection may be based at least in part on L1-RSRP measurements (measured or reported), where an input of a first set of beams to the AI / ML model may produce an output of a second set of beams. The second set of beams may have more beams than the first set of beams. The output of the second set of beams from the AI / ML model may result in fewer beam measurements, thereby reducing UE power. The output of the second set of beams derived from the first set of beams may be associated with codebook-based SD prediction / selection. Codebook-based SD prediction / selection may be associated with initial access, secondary cell group (SCG) setup, serving beam refinement and / or link quality (e.g., channel quality indicator (CQI) or precoding matrix indicator (PMI)) and interference adaptation.

[0088] Channel or L1-RSRP measurements may be reported by the UE, or may be measured by the UE. Channel or L1-RSRP measurements may be facilitated via raw channel extraction. Channel or L1-RSRP measurements reported by the UE may be used to perform inference at the network node. Channel or L1-RSRP measurements measured by the UE may be used to perform inference at the UE. AI / ML-based SD beam prediction / selection may be based at least in part on channel or L1-RSRP measurements (measured or reported), where the channel / beam input to the AI / ML model may produce an output of pointing direction, angle of departure (AoD), or angle of arrival (AoA). The output from the AI / ML model may indicate a specific beam (associated with a specific direction), while the input may be associated with multiple beams. The output of pointing direction, AoD, or AoA may result in better beam management accuracy without excessive beam scanning. The output of pointing direction, AoD, or AoA derived from the channel / beam input may be associated with non-codebook-based prediction / selection. Non-codebook based prediction / selection may be associated with serving beam refinement and / or link quality (eg, CQI or PMI) and interference adaptation.

[0089] The network node and / or UE may perform AI / ML based TD beam prediction / selection. When implementing TD beam prediction / selection, a plurality of UE reports or measurements (e.g., channel or L1-RSRP measurements reported by or measured by the UE) over a historical period (e.g., in a time series) may be provided as input to the AI / ML model. The AI / ML model may generate an output associated with codebook-based TD beam prediction, or may generate an output associated with non-codebook-based TD point direction, AoD, and / or AoA prediction. Codebook-based TD beam prediction and non-codebook-based TD pointing direction, AoD, and / or AoA prediction may be associated with TD beam prediction. Joint TD beam prediction may be associated with serving beam refinement, link quality (e.g., CQI or PMI) and interference adaptation, beam failure / blocking prediction, and / or RLF prediction.

[0090] The network node and / or UE may perform AI / ML-based SD and TD beam prediction / selection. When implementing SD and TD beam prediction / selection, multiple UE reports or measurements (e.g., channel or L1-RSRP measurements reported by or measured by the UE) over a period of time (e.g., in a time series) may be provided as input to the AI / ML model. The AI / ML model may generate outputs associated with codebook-based SD and TD beam prediction. The AI / ML model may generate outputs associated with non-codebook-based SD and TD pointing directions, AoD, and / or AoA predictions. Codebook-based SD and TD beam predictions and non-codebook-based SD and TD pointing directions, AoD, and / or AoA predictions may be associated with joint SD and TD beam prediction. Joint SD and TD beam prediction may be associated with serving beam refinement, link quality (e.g., CQI or PMI) and interference adaptation, beam failure / blocking prediction, and / or RLF prediction.

[0091] For AI / ML-based beam management, both the first and second scenarios of beam management can be supported for characterization and baseline performance evaluation. In the first scenario, the SD downlink beam prediction for beam set A can be based at least in part on measurements of beam set B. In the second scenario, the temporal downlink beam prediction for beam set A can be based at least in part on historical measurements of beam set B. Thus, set A can correspond to the output of the ML model, and set B can correspond to the input of the model.

[0092] For the first and second cases, 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 within 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 and second alternatives, 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.

[0093] AI / ML-based beam management can be managed according to a lifecycle management (LCM) methodology. The LCM methodology generally defines how the network manages AI / ML-based beam management, such as identifying specific ML models or functions supported by the UE and controlling (e.g., activating, deactivating, and / or monitoring) these ML models or functions.

[0094] One example of an LCM methodology is functionality-based LCM. In functionality-based LCM, a network node can be aware of specific AI / ML functionality at a UE through UE capability reporting. The network node can control specific AI / ML functions (e.g., activate, deactivate, or monitor their performance). AI / ML functions may include, for example, wide-to-narrow beam prediction, narrow-to-narrow beam prediction, prediction using a fixed beam set for measurement, prediction using a variable beam set for measurement, and / or prediction using auxiliary information and / or additional reference signals to improve the quality of beam prediction. For each of these AI / ML functions, the UE may implement one or more AI / ML models that may be transparent to the network.

[0095] Another example of an LCM methodology is model identifier (ID)-based LCM. In model ID-based LCM, a specific AI / ML model is registered with the network using a model identifier. The network can be made aware of the AI / ML functionality and supported model IDs via UE capability reporting. During the inference phase (e.g., applying a model), the network can control AI / ML model inference, including selecting a model, activating a model, deactivating a model, switching between models, falling back from one model to another, and monitoring a model.

[0096] Figure 6 6 is a diagram illustrating an example 600 of signaling regarding features of AI / ML-based beam management according to the present disclosure. Example 600 includes a UE (e.g., UE 120) and a network node (e.g., network node 110).

[0097] As shown by reference numeral 610, the UE may send and the network node may receive information indicating whether the UE supports a feature set for AI / ML-based beam prediction. For example, the information may include capability information (e.g., UE capability information), or the capability information may include the information. The information may include a set of fields. Each field may indicate whether a feature is supported or a value associated with a supported feature. In some aspects, the information (or feature set) may be used for functionality-based LCM for AI / ML-based beam prediction. For example, the feature set may indicate whether the UE supports one or more AI / ML functions, as described below.

[0098] In some aspects, the feature set includes a feature for predicting parameters of a first set of beams based at least in part on measurements regarding a second set of beams. In some aspects, the feature can be used for wide-to-narrow beam prediction. "Wide-to-narrow beam prediction" can relate to a second set of beams (e.g., set B, the input beam set) having a wider beamwidth (e.g., in some examples, the beams on which SSBs are transmitted) than a first set of beams (i.e., the output beam set) of set A. For example, the beams of set B can be different from the beams of set A. Additionally or alternatively, the feature can be used for narrow-to-narrow beam prediction. "Narrow-to-narrow beam prediction" can relate to a second set of beams (e.g., set B) that is a proper subset of the first set of beams (e.g., set A). For example, set B can include multiple beams, at least one of which is used to generate set A. In some aspects, the feature can relate to spatial beam prediction. For example, wide-to-narrow beam prediction and narrow-to-narrow beam prediction can be examples of spatial beam prediction.

[0099] In some aspects, the feature set includes a feature indicating the resolution of an output beam set (e.g., set A) for AI / ML based beam prediction. For example, for narrow-to-narrow beam prediction, the UE (e.g., one or more AI / ML models) may have the capability to determine how refined the prediction of beams in the output beam set may be. For example, the feature indicating the resolution may indicate how many beams in the output beam set may be predicted given the number of beam measurements on beams in set B. As a more specific example, the feature indicating the resolution may indicate whether, for 8 beam measurements on set B, set A may include 64 beams, 256 beams, etc. In some aspects, the feature indicating the resolution may be used for spatial domain beam prediction.

[0100] In some aspects, a feature set includes a feature indicating whether an input beam set (e.g., set B) for AI / ML-based beam prediction is fixed or variable. A fixed beam set may include the same beam across multiple beam management instances. A variable beam set may include different beams across multiple beam management instances (e.g., a first beam management instance may involve measurements of a first set of beams, and a second beam management instance may involve measurements of a second set of beams that is different from the first set of beams). The feature set may include a feature indicating that the UE supports a fixed input beam set. Additionally or alternatively, the feature set may include a feature indicating that the UE supports a variable input beam set. In some aspects, the feature indicating whether the input beam set is fixed or variable may be used for spatial beam prediction. Additionally or alternatively, the feature indicating whether the input beam set is fixed or variable may be used for time-domain beam prediction. Additionally or alternatively, the feature indicating whether the input beam set is fixed or variable may be used for combined spatial and time-domain beam prediction.

[0101] In some aspects, the feature set includes a feature indicating whether the UE supports assistance information as an input to AI / ML-based beam prediction. The assistance information may include, for example, information about the beam shape (e.g., the network beam shape), such as a function indicating the beam pattern, the beam boresight direction, the beam width (e.g., the 3dB beam width), etc. As another example, the assistance information may include information derived from additional reference signals (e.g., in addition to the CSI-RS or SSB used to measure the beam) (such as a demodulation reference signal), which may improve the quality of beam prediction. In some aspects, the feature indicating whether the UE supports assistance information as an input to AI / ML-based beam prediction may be used for spatial domain beam prediction. Additionally or alternatively, the feature indicating whether the UE supports assistance information as an input to AI / ML-based beam prediction may be used for time domain beam prediction. Additionally or alternatively, the feature indicating whether the UE supports assistance information as an input to AI / ML-based beam prediction may be used for combined spatial and time domain beam prediction.

[0102] In some aspects, the feature set includes a feature indicating at least one of the following: the number of consecutive beam management periods from which input information for AI / ML-based beam prediction is obtained, or the number of beam management periods for which input information is used to determine output information for AI / ML-based beam prediction. For example, time-domain beam prediction may occur over several beam management periods. Measurements (e.g., L1-RSRP measurements) from a first number of consecutive (e.g., consecutive) beam management periods may be provided as input to the AI / ML model. The AI / ML model may provide predictions regarding measurements in a second number of beam management periods. For example, the AI / ML model may receive as input L1-RSRP measurements from x consecutive beam management periods out of every x+y consecutive beam management periods, and may then provide predictions regarding the next y consecutive beam management periods. In this example, the feature may indicate x and / or y.

[0103] In some aspects, the feature set includes a feature indicating a time-based prediction capability of the AI / ML based beam prediction. The time-based prediction capability may indicate a future time horizon for which the AI / ML model can provide predictions regarding the output beam set. For example, the time-based prediction capability may be in terms of time (e.g., absolute timing) or in terms of the number of beam management instances. In some aspects, the feature set includes a feature indicating a time length of historical measurements used for time-domain beam prediction. For example, a feature may indicate an input sequence length used as input to the AI / ML model (e.g., time length of a series of measurements, number of measurements in the series of measurements, etc.).

[0104] In some aspects, the feature set includes a feature indicating whether the UE supports combined spatial and time domain AI / ML-based beam prediction. For example, the feature may indicate whether the UE supports spatiotemporal beam prediction (referred to elsewhere herein as joint SD and TD beam prediction).

[0105] In some aspects, feature sets may have a hierarchical relationship. For example, a first feature may be considered a sub-feature of a second feature. The information indicated by reference numeral 610 may include the first feature only if the second feature is also included (e.g., a prerequisite for the first feature). As an example, a feature indicating the resolution of the output beam set may be a sub-feature of the narrow-to-narrow beam prediction feature. As another example, features related to time domain beam prediction may be included only if a feature indicating that the UE supports time domain beam prediction is also included. As another example, features related to spatial domain beam prediction may be included only if a feature indicating that the UE supports spatial domain beam prediction is also included. As another example, support for a sub-feature may imply that the corresponding feature is also supported.

[0106] Therefore, different features may be defined for temporal (time) beam prediction and spatial beam prediction.

[0107] As shown by reference numeral 620, the UE and the network node may perform communication based at least in part on information indicating whether the UE supports a feature set. For example, the network node may output (e.g., send to the UE, or provide for transmission to the UE) configuration information based at least in part on the information indicating whether the UE supports a feature set. The configuration information may include, for example, a reference signal configuration (such as a CSI-RS configuration). The configuration information may be based at least in part on the information indicating whether the UE supports a feature set, because the configuration information may configure reference signaling that supports the features indicated by the information indicating whether the UE supports a feature set. As another example, based at least in part on the features (e.g., according to the configuration information), the network node may output and the UE may receive (e.g., measure) reference signaling. As yet another example, the UE may report information determined using an AI / ML function that the feature set indicates is supported by the UE.

[0108] As mentioned above, Figure 6 are provided as examples. Other examples can be found in the Figure 6 The examples described are different.

[0109] Figure 7 is a diagram illustrating an example process 700, performed, for example, by a UE, according to the present disclosure. Example process 700 is an example in which a UE (eg, UE 120) performs operations associated with UE features for beam prediction.

[0110] like Figure 7 As shown, in some aspects, process 700 may include sending information indicating whether the UE supports a feature set for AI / ML-based beam prediction (block 710). For example, a UE (e.g., using Figure 9 The transmitting component 904 and / or the communication manager 906 depicted in FIG may send information indicating whether the UE supports a feature set for AI / ML based beam prediction, as described above.

[0111] like Figure 7 As further shown in FIG. 7 , in some aspects, process 700 may include performing communications based at least in part on information indicating whether the UE supports a feature set (block 720). For example, a UE (e.g., using Figure 9 The communication manager 906 depicted in FIG may perform communications based at least in part on information indicating whether the UE supports a feature set, as described above.

[0112] Process 700 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0113] In a first aspect, the feature set includes at least one of: one or more features related to spatial domain beam prediction, one or more features related to time domain beam prediction, or a combination thereof.

[0114] In a second aspect, alone or in combination with the first aspect, the information indicating whether the UE supports the feature set is used for functionality-based lifecycle management of the AI / ML-based beam prediction.

[0115] In a third aspect, alone or in combination with one or more of the first and second aspects, the information indicating whether the UE supports the feature set includes capability information.

[0116] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the feature set includes features for wide-to-narrow beam prediction.

[0117] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, the feature set includes features for narrow-to-narrow beam prediction.

[0118] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the feature set includes features indicating a resolution of the output beam set of the AI / ML based beam prediction.

[0119] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the feature set includes a feature indicating whether the input beam set for the AI / ML based beam prediction is fixed or variable.

[0120] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, the feature set includes a feature indicating whether the UE supports auxiliary information as an input to the AI / ML based beam prediction.

[0121] In a ninth aspect, alone or in combination with one or more of aspects one to eight, the feature set comprises features indicating at least one of: the number of consecutive beam management cycles from which input information for the AI / ML-based beam prediction is obtained, or the number of beam management cycles from which the input information is used to determine output information for the AI / ML-based beam prediction.

[0122] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the feature set includes features indicative of a time-based prediction capability of the AI / ML-based beam prediction.

[0123] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, the feature set includes a feature indicating whether the UE supports combined spatial and time domain AI / ML-based beam prediction.

[0124] although Figure 7 Example blocks of process 700 are shown, but in some aspects, process 700 may include Figure 7 7. In some embodiments, the process 700 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in FIG. Additionally or alternatively, two or more blocks of the blocks of process 700 may be executed in parallel.

[0125] Figure 8 is a diagram illustrating an example process 800, for example, performed by a network node, in accordance with the present disclosure. The example process 800 is an example in which a network node (eg, network node 110) performs operations associated with UE features for beam prediction.

[0126] like Figure 8 As shown, in some aspects, process 800 may include obtaining information indicating whether the UE supports a feature set for AI / ML-based beam prediction (block 810). For example, a network node (e.g., using Figure 10 The receiving component 1002 and / or the communication manager 1006 depicted in FIG may obtain information indicating whether the UE supports a feature set for AI / ML-based beam prediction, as described above.

[0127] like Figure 8 As further shown in FIG. 8 , in some aspects, process 800 may include performing communications based at least in part on information indicating whether the UE supports a feature set (block 820). For example, a network node (e.g., using Figure 10 The communication manager 1006 depicted in FIG may perform communications based at least in part on information indicating whether the UE supports a feature set, as described above.

[0128] Process 800 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0129] In a first aspect, the feature set includes at least one of: one or more features related to spatial domain beam prediction, one or more features related to time domain beam prediction, or a combination thereof.

[0130] In a second aspect, alone or in combination with the first aspect, the information indicating whether the UE supports the feature set is used for functionality-based lifecycle management of the AI / ML-based beam prediction.

[0131] In a third aspect, alone or in combination with one or more of the first and second aspects, the information indicating whether the UE supports the feature set includes capability information.

[0132] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the feature set includes features for wide-to-narrow beam prediction.

[0133] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, the feature set includes features for narrow-to-narrow beam prediction.

[0134] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the feature set includes features indicating a resolution of the output beam set of the AI / ML based beam prediction.

[0135] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the feature set includes a feature indicating whether the input beam set for the AI / ML based beam prediction is fixed or variable.

[0136] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, the feature set includes a feature indicating whether the UE supports auxiliary information as an input to the AI / ML based beam prediction.

[0137] In a ninth aspect, alone or in combination with one or more of aspects one to eight, the feature set comprises features indicating at least one of: the number of consecutive beam management cycles from which input information for the AI / ML-based beam prediction is obtained, or the number of beam management cycles from which the input information is used to determine output information for the AI / ML-based beam prediction.

[0138] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the feature set includes features indicative of a time-based prediction capability of the AI / ML-based beam prediction.

[0139] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, the feature set includes a feature indicating whether the UE supports combined spatial and time domain AI / ML-based beam prediction.

[0140] although Figure 8 Example blocks of process 800 are shown, but in some aspects, process 800 may include Figure 8 800. In some embodiments, the process 800 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in FIG. Additionally or alternatively, two or more blocks of the blocks of process 800 may be executed in parallel.

[0141] Figure 9 9 is a diagram of an example apparatus 900 for wireless communication according to the present disclosure. Apparatus 900 may be a UE, or a UE may include apparatus 900. In some aspects, apparatus 900 includes a receiving component 902, a sending component 904, and / or a communication manager 906 that may communicate with each other (e.g., via one or more buses and / or one or more other components). In some aspects, communication manager 906 is a communication manager that is configured to communicate with one another. Figure 1 The described communication manager 140. As shown, the device 900 can communicate using a receiving component 902 and a sending component 904 with another device 908, such as a UE or a network node such as a CU, DU, RU, or base station.

[0142] In some aspects, the apparatus 900 may be configured to perform Figures 4 to 6 Additionally or alternatively, the apparatus 900 may be configured to perform one or more of the processes described herein such as Figure 7 In some aspects, Figure 9 The device 900 and / or one or more components shown may include a combination of Figure 2 Additionally or alternatively, one or more components of the UE described. Figure 9 One or more of the components shown may be combined 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 that is 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.

[0143] The receiving component 902 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from the device 908. The receiving component 902 may provide the received communications to one or more other components of the device 900. In some aspects, the receiving component 902 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 one or more other components of the device 900. In some aspects, the receiving component 902 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof of the described UE.

[0144] The transmitting component 904 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the device 908. In some aspects, one or more other components of the device 900 may generate communications and may provide the generated communications to the transmitting component 904 for transmission to the device 908. In some aspects, the transmitting component 904 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 the device 908. In some aspects, the transmitting component 904 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 transmitting component 904 can be co-located with the receiving component 902 in a transceiver.

[0145] The communication manager 906 can support the operation of the receiving component 902 and / or the sending component 904. For example, the communication manager 906 can receive information associated with configuring the receipt of communications by the receiving component 902 and / or the sending of communications by the sending component 904. Additionally or alternatively, the communication manager 906 can generate and / or provide control information to the receiving component 902 and / or the sending component 904 to control the receipt and / or sending of communications.

[0146] The transmitting component 904 can transmit information indicating whether the UE supports a feature set for AI / ML based beam prediction. The communication manager 906 can perform communications based at least in part on the information indicating whether the UE supports the feature set.

[0147] Figure 9 The number and arrangement of components shown are provided as examples. Figure 9 There may be additional components, fewer components, different components, or components arranged differently than those shown. Figure 9 Two or more components shown may be implemented in a single component, or Figure 9 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Figure 9 The illustrated set of component(s) may be described as being executable by Figure 9 Another collection of components shown performs one or more functions.

[0148] Figure 101 is a diagram of an example apparatus 1000 for wireless communication according to the present disclosure. Apparatus 1000 may be a network node, or a network node may include apparatus 1000. In some aspects, apparatus 1000 includes a receiving component 1002, a sending component 1004, and / or a communication manager 1006 that may communicate with each other (e.g., via one or more buses and / or one or more other components). In some aspects, communication manager 1006 is a communication manager that is configured to communicate with one another. Figure 1 The described communication manager 150. As shown, the apparatus 1000 can communicate with another apparatus 1008, such as a UE or a network node, such as a CU, DU, RU, or base station, using a receiving component 1002 and a sending component 1004.

[0149] In some aspects, the apparatus 1000 may be configured to perform Figures 4 to 6 Additionally or alternatively, the apparatus 1000 may be configured to perform one or more of the processes described herein such as Figure 8 In some aspects, Figure 10 The device 1000 and / or one or more components shown may include a combination of Figure 2 Additionally or alternatively, one or more components of the described network node. Figure 10 One or more of the components shown may be combined 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 that is 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.

[0150] The receiving component 1002 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from the apparatus 1008. The receiving component 1002 may provide the received communications to one or more other components of the apparatus 1000. In some aspects, the receiving component 1002 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 one or more other components of the apparatus 1000. In some aspects, the receiving component 1002 may include processing the received communications in conjunction with one or more other components of the apparatus 1000. Figure 2One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof of the described network nodes. In some aspects, the receiving component 1002 and / or the transmitting component 1004 may include or be included in a network interface. The network interface may be configured to obtain and / or output signals for the apparatus 1000 via one or more communication links (such as a backhaul link, a midhaul link, and / or a fronthaul link).

[0151] The transmitting component 1004 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the apparatus 1008. In some aspects, one or more other components of the apparatus 1000 may generate communications and may provide the generated communications to the transmitting component 1004 for transmission to the apparatus 1008. In some aspects, the transmitting component 1004 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 the apparatus 1008. In some aspects, the transmitting component 1004 may include a processor in conjunction with a processor. Figure 2 One 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 transmitting component 1004 can be co-located with the receiving component 1002 in a transceiver.

[0152] The communications manager 1006 can support the operation of the receiving component 1002 and / or the sending component 1004. For example, the communications manager 1006 can receive information associated with configuring the receipt of communications by the receiving component 1002 and / or the sending of communications by the sending component 1004. Additionally or alternatively, the communications manager 1006 can generate and / or provide control information to the receiving component 1002 and / or the sending component 1004 to control the receipt and / or sending of communications.

[0153] Receiving component 1002 can obtain information indicating whether the UE supports a feature set for AI / ML based beam prediction.Communication manager 1006 can perform communications based at least in part on the information indicating whether the UE supports the feature set.

[0154] Figure 10 The number and arrangement of components shown are provided as examples. Figure 10 There may be additional components, fewer components, different components, or components arranged differently than those shown. Figure 10 Two or more components shown may be implemented in a single component, or Figure 10 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Figure 10The illustrated set of component(s) may be described as being executable by Figure 10 Another collection of components shown performs one or more functions.

[0155] The following provides an overview of some aspects of the disclosure:

[0156] Aspect 1: A method of wireless communication performed by a user equipment (UE), the method comprising: sending information indicating whether the UE supports a feature set for beam prediction based on artificial intelligence or machine learning (AI / ML); and performing communication based at least in part on the information indicating whether the UE supports the feature set.

[0157] Aspect 2: The method according to aspect 1, wherein the feature set comprises at least one of the following: one or more features related to spatial domain beam prediction, one or more features related to time domain beam prediction, or a combination thereof.

[0158] Aspect 3: The method according to any one of aspects 1 to 2, wherein the information indicating whether the UE supports the feature set is used for functionality-based lifecycle management of the AI / ML-based beam prediction.

[0159] Aspect 4: The method according to any one of aspects 1 to 3, wherein the information indicating whether the UE supports the feature set includes capability information.

[0160] Aspect 5: The method according to any one of aspects 1 to 4, wherein the feature set includes features for wide-to-narrow beam prediction.

[0161] Aspect 6: The method according to any one of aspects 1 to 5, wherein the feature set includes features for narrow-to-narrow beam prediction.

[0162] Aspect 7: The method according to any one of aspects 1 to 6, wherein the feature set includes a feature indicating a resolution of the output beam set of the AI / ML-based beam prediction.

[0163] Aspect 8: The method according to any one of aspects 1 to 7, wherein the feature set includes a feature indicating whether the input beam set used for the AI / ML-based beam prediction is fixed or variable.

[0164] Aspect 9: The method according to any one of aspects 1 to 8, wherein the feature set includes a feature indicating whether the UE supports auxiliary information as an input to the AI / ML-based beam prediction.

[0165] Aspect 10: A method according to any one of Aspects 1 to 9, wherein the feature set includes a feature indicating at least one of the following: the number of consecutive beam management cycles from which input information for the AI / ML-based beam prediction is obtained, or the number of beam management cycles from which the input information is used to determine output information for the AI / ML-based beam prediction.

[0166] Aspect 11: A method according to any one of Aspects 1 to 10, wherein the feature set includes a feature indicating a time-based prediction capability of the AI / ML-based beam prediction, and the time-based prediction capability indicates how far in advance the UE can perform time-domain beam prediction.

[0167] Aspect 12: The method according to any one of aspects 1 to 11, wherein the feature set includes a feature indicating whether the UE supports combined spatial and time domain AI / ML-based beam prediction.

[0168] Aspect 13: A method of wireless communication performed by a network node, the method comprising: obtaining information indicating whether a user equipment (UE) supports a feature set for beam prediction based on artificial intelligence or machine learning (AI / ML); and performing communication based at least in part on the information indicating whether the UE supports the feature set.

[0169] Aspect 14: The method according to aspect 13, wherein the feature set includes at least one of the following: one or more features related to spatial domain beam prediction, one or more features related to time domain beam prediction, or a combination thereof.

[0170] Aspect 15: The method according to any one of aspects 13 to 14, wherein the information indicating whether the UE supports the feature set is used for functionality-based lifecycle management of the AI / ML-based beam prediction.

[0171] Aspect 16: The method according to any one of aspects 13 to 15, wherein the information indicating whether the UE supports the feature set includes capability information.

[0172] Aspect 17: The method according to any one of aspects 13 to 16, wherein the feature set includes features for wide-to-narrow beam prediction.

[0173] Aspect 18: The method according to any one of aspects 13 to 17, wherein the feature set includes features for narrow-to-narrow beam prediction.

[0174] Aspect 19: The method according to any one of aspects 13 to 18, wherein the feature set includes a feature indicating a resolution of the output beam set of the AI / ML-based beam prediction.

[0175] Aspect 20: The method according to any one of aspects 13 to 19, wherein the feature set includes a feature indicating whether the input beam set used for the AI / ML-based beam prediction is fixed or variable.

[0176] Aspect 21: The method according to any one of aspects 13 to 20, wherein the feature set includes a feature indicating whether the UE supports assistance information as an input to the AI / ML-based beam prediction.

[0177] Aspect 22: A method according to any one of Aspects 13 to 21, wherein the feature set includes a feature indicating at least one of the following: the number of consecutive beam management cycles from which input information for the AI / ML-based beam prediction is obtained, or the number of beam management cycles from which the input information is used to determine output information for the AI / ML-based beam prediction.

[0178] Aspect 23: The method according to any one of aspects 13 to 22, wherein the feature set includes features indicative of a time-based prediction capability of the AI / ML-based beam prediction.

[0179] Aspect 24: The method according to any one of aspects 13 to 23, wherein the feature set includes a feature indicating whether the UE supports combined spatial and time domain AI / ML-based beam prediction.

[0180] Aspect 25: The method of aspect 1, wherein the set of features comprises features for predicting parameters of a first set of beams based at least in part on measurements regarding a second set of beams.

[0181] Aspect 26: The method according to aspect 25, wherein the first set of beams has a first beamwidth, the second set of beams has a second beamwidth, and the first beamwidth is narrower than the second beamwidth.

[0182] Aspect 27: The method according to aspect 25, wherein the second set of beams is a proper subset of the first set of beams.

[0183] Aspect 28: The method of aspect 15, wherein the set of features comprises features for predicting parameters of a first set of beams based at least in part on measurements regarding a second set of beams.

[0184] Aspect 29: The method according to aspect 28, wherein the first set of beams has a first beamwidth, the second set of beams has a second beamwidth, and the first beamwidth is narrower than the second beamwidth.

[0185] Aspect 30: The method according to aspect 28, wherein the second set of beams is a proper subset of the first set of beams.

[0186] Aspect 31: The method according to aspect 1, wherein the feature set includes a feature indicating a time length of historical measurements used for time-domain beam prediction.

[0187] Aspect 32: The method according to aspect 15, wherein the feature set includes a feature indicating a time length of historical measurements used for time-domain beam prediction.

[0188] Aspect 33: An apparatus for wireless communication 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.

[0189] Aspect 34: A device for wireless communication, the device comprising: a memory; and one or more processors coupled to the memory, the one or more processors configured to perform the method according to one or more of aspects 1 to 32.

[0190] Aspect 35: 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.

[0191] Aspect 36: 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.

[0192] Aspect 37: 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.

[0193] 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 are possible in light of the above disclosure or may be acquired from practice of the aspects.

[0194] As used herein, the term "component" is intended to be broadly interpreted as hardware and / or a combination of hardware and software. Whether referred to as software, firmware, middleware, microcode, hardware description language or other names, "software" should be broadly interpreted to mean 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 hardware and / or a combination of hardware and software. It will be apparent that the systems and / or methods described herein can be implemented by different forms of hardware and / or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit various aspects. Therefore, no reference is made herein to specific software code to describe the operation and behavior of the systems and / or methods, as those skilled in the art will appreciate that software and hardware can be designed to implement the systems and / or methods based at least in part on the description herein.

[0195] As used herein, "satisfying a threshold" may mean that a value is 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.

[0196] Although the specific combination of features is stated 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 not specifically described in the claims and / or 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 item list refers to any combination of these items (which includes a single member). As an example, "at least one of a, b or c" is intended to encompass a, b, c, a+b, a+c, b+c and a+b+c, as well as any combination of 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 arrangement of a, b and c).

[0197] 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 projects and can be used interchangeably with "one or more". In addition, as used herein, the article "said" is intended to include one or more projects mentioned in conjunction with 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 projects and can be used interchangeably with "one or more". If only want to refer to a project, 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 "at least partially based on", unless explicitly stated otherwise. 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. A user equipment (UE) for wireless communication, the user equipment (UE) comprising: Memory; and one or more processors coupled to the memory and configured to: sending information indicating whether the UE supports a feature set for artificial intelligence or machine learning (AI / ML) based beam prediction; and Communicating is performed based at least in part on the information indicating whether the UE supports the feature set.

2. The UE according to claim 1, wherein the feature set comprises at least one of the following: One or more features related to spatial beam prediction, One or more features related to time-domain beam prediction, or A combination of them.

3. The UE according to claim 1, wherein the information indicating whether the UE supports the feature set is used for functionality-based lifecycle management of the AI / ML-based beam prediction. The UE according to claim 1 , wherein the information indicating whether the UE supports the feature set comprises capability information. 5 . The UE of claim 1 , wherein the feature set comprises features for predicting parameters of a first set of beams based at least in part on measurements with respect to a second set of beams. The UE according to claim 5 , wherein the first beam set has a first beamwidth, the second beam set has a second beamwidth, and the first beamwidth is narrower than the second beamwidth. The UE according to claim 5 , wherein the second beam set is a proper subset of the first beam set.

8. The UE of claim 1, wherein the feature set comprises a feature indicating a resolution of an output beam set of the AI / ML-based beam prediction. 9 . The UE of claim 1 , wherein the feature set comprises a feature indicating whether an input beam set for the AI / ML-based beam prediction is fixed or variable.

10. The UE of claim 1, wherein the feature set includes a feature indicating whether the UE supports assistance information as an input to the AI / ML based beam prediction.

11. The UE of claim 1 , wherein the feature set comprises a feature indicating at least one of: the number of consecutive beam management periods from which input information for said AI / ML based beam prediction is obtained, or The input information is used to determine the number of beam management cycles of the output information of the AI / ML-based beam prediction.

12. The UE of claim 1, wherein the feature set includes a feature indicating a time-based prediction capability of time-domain beam prediction for the AI / ML-based beam prediction, the time-based prediction capability indicating how far in advance the UE can perform time-domain beam prediction.

13. The UE of claim 1, wherein the feature set comprises a feature indicating whether the UE supports combined spatial and time domain AI / ML-based beam prediction.

14. The UE of claim 1, wherein the feature set comprises a feature indicating a time length of historical measurements used for time-domain beam prediction.

15. A network node for wireless communication, the network node comprising: Memory; and one or more processors coupled to the memory and configured to: obtaining information indicating whether a user equipment (UE) supports a feature set for artificial intelligence or machine learning (AI / ML) based beam prediction; and Communicating is performed based at least in part on the information indicating whether the UE supports the feature set.

16. The network node according to claim 15, wherein the feature set comprises at least one of the following: One or more features related to spatial beam prediction, One or more features related to time-domain beam prediction, or A combination of them.

17. The network node of claim 15, wherein the information indicating whether the UE supports the feature set is used for functionality-based lifecycle management of the AI / ML-based beam prediction.

18. The network node of claim 15, wherein the information indicating whether the UE supports the feature set comprises capability information.

19. The network node of claim 15, wherein the set of features comprises features for predicting parameters of a first set of beams based at least in part on measurements regarding a second set of beams.

20. The network node of claim 19, wherein the first set of beams has a first beamwidth, the second set of beams has a second beamwidth, and the first beamwidth is narrower than the second beamwidth.

21. The network node of claim 19, wherein the second set of beams is a proper subset of the first set of beams.

22. The network node of claim 15, wherein the feature set comprises a feature indicating a resolution of an output beam set of the AI / ML based beam prediction.

23. The network node of claim 15, wherein the feature set includes a feature indicating whether an input beam set for the AI / ML-based beam prediction is fixed or variable.

24. The network node of claim 15, wherein the feature set comprises a feature indicating whether the UE supports assistance information as an input to the AI / ML based beam prediction.

25. The network node of claim 15, wherein the feature set comprises features indicative of at least one of: the number of consecutive beam management periods from which input information for said AI / ML based beam prediction is obtained, or The input information is used to determine the number of beam management cycles of the output information of the AI / ML-based beam prediction.

26. The network node of claim 15, wherein the feature set includes a feature indicative of a time-based prediction capability of the AI / ML-based beam prediction.

27. The network node of claim 15, wherein the feature set includes a feature indicating whether the UE supports combined spatial and time domain AI / ML based beam prediction.

28. A method of wireless communication performed by a user equipment (UE), the method comprising: sending information indicating whether the UE supports a feature set for artificial intelligence or machine learning (AI / ML) based beam prediction; as well as Communicating is performed based at least in part on the information indicating whether the UE supports the feature set.

29. The method of claim 28, wherein the feature set comprises at least one of: One or more features related to spatial beam prediction, One or more features related to time-domain beam prediction, or A combination of them.

30. A method of wireless communication performed by a network node, the method comprising: obtaining information indicating whether a user equipment (UE) supports a feature set for artificial intelligence or machine learning (AI / ML) based beam prediction; as well as Communicating is performed based at least in part on the information indicating whether the UE supports the feature set.

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