Channel characteristic prediction based at least in part on subset of downlink reference signal resources

Abstract

Aspects of the present disclosure generally relate to wireless communications. In some aspects, a user equipment (UE) can receive, from a network node, an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement occasion, where the subset of the first set of downlink reference signal resources is associated with an input for a model, the model is used to determine predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement occasion. The UE can identify the subset of the first set of downlink reference signal resources based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources. Numerous other aspects are described.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to PCT patent application No. PCT / CN2022 / 122014, entitled "CHANNEL CHARACTERISTICPREDICTIONS BASED AT LEAST IN PART ON A SUBSET OF DOWNLINK REFERENCE SIGNALRESOURCES", filed on September 28, 2022 and assigned to the assignee of this application. The disclosure of the prior application is considered part of this patent application and is incorporated by reference into this patent application. Technical Field

[0003] Aspects of the present disclosure relate generally to wireless communications and to techniques and apparatus for channel characteristic prediction based at least in part on a subset of downlink reference signal resources. Background Art

[0004] Wireless communication systems are widely deployed to provide a variety of telecommunication services, such as telephony, video, data, messaging, and broadcasting. Typical wireless communication systems may employ multiple access technologies that can support communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, etc.). Examples of such multiple access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and long term evolution (LTE). LTE / LTE-Advanced is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP).

[0005] A wireless network may include one or more network nodes that support communications for wireless communication devices, such as user equipment (UE) or multiple UEs. The UE may communicate with the network nodes via downlink communications and uplink communications. A "downlink" (or "DL") refers to a communication link from a network node to a UE, and an "uplink" (or "UL") refers to a communication link from a UE to a network node. Some wireless networks may support device-to-device communications, such as via a local link (e.g., a sidelink (SL), a wireless local area network (WLAN) link, and / or a wireless personal area network (WPAN) link, etc.).

[0006] The above-mentioned multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different UEs to communicate at a city, country, region and / or global level. New Radio (NR) (which may be referred to as 5G) is a set of enhancements to the LTE mobile standard promulgated by 3GPP. NR is designed to better support mobile broadband Internet access by: improving spectrum efficiency; reducing costs; improving services; utilizing new spectrum; and using orthogonal frequency division multiplexing (OFDM) (CP-OFDM) with cyclic prefix (CP) on the downlink, CP-OFDM and / or single carrier frequency division multiplexing (SC-FDM) (also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink to better integrate with other open standards; and supporting beamforming, multiple input multiple output (MIMO) antenna technology and carrier aggregation. As the demand for mobile broadband access continues to increase, further improvements in LTE, NR and other radio access technologies remain useful. Summary of the invention

[0007] Some aspects described herein relate to a method of wireless communication performed by a user equipment (UE). The method may include receiving from a network node an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity. The method may include identifying the subset of the first set of downlink reference signal resources based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources.

[0008] Some aspects described herein relate to a method of wireless communication performed by a network node. The method may include selecting a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity. The method may include sending an indication of the technique for identifying the subset of the first set of downlink reference signal resources to a UE.

[0009] Some aspects described herein relate to a UE for wireless communication. The UE may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be configured to receive from a network node an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity. The one or more processors may be configured to identify the subset of the first set of downlink reference signal resources based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources.

[0010] Some aspects described herein relate to a network node for wireless communication. The network node may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be configured to select a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity. The one or more processors may be configured to send an indication of the technique for identifying the subset of the first set of downlink reference signal resources to a UE.

[0011] 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 receive from a network node an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining a predicted channel characteristic associated with a second set of downlink reference signal resources associated with the measurement opportunity. The instruction set, when executed by one or more processors of the UE, may cause the UE to identify the subset of the first set of downlink reference signal resources based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources.

[0012] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a network node. The instruction set, when executed by one or more processors of the network node, can cause the network node to select a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity. The instruction set, when executed by one or more processors of the network node, can cause the network node to send an indication of the technique for identifying the subset of the first set of downlink reference signal resources to a UE.

[0013] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for receiving, from a network node, an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity. The apparatus may include means for identifying the subset of the first set of downlink reference signal resources based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources.

[0014] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for selecting a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity. The apparatus may include means for sending an indication of the technique for identifying the subset of the first set of downlink reference signal resources to a UE.

[0015] 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 the drawings and description and as illustrated in the drawings and description.

[0016] The features and technical advantages of examples according to the present disclosure have been outlined quite extensively above so that the following specific embodiments may be better understood. Additional features and advantages will be described below. The disclosed concepts and specific examples may be easily used as a basis for modifying or designing other structures for achieving the same purpose of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. 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 to the claims.

[0017] Although various aspects are described in the present 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 for analog and digital purposes (e.g., hardware components, including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders and / or summers). 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 configurations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to fully understand the above-mentioned features of the present disclosure, a more specific description of the invention briefly summarized above can be obtained by referring to various aspects (some of which are illustrated in the accompanying drawings). However, it should be noted that the accompanying drawings only illustrate certain typical aspects of the present disclosure and are therefore not to be considered as limiting the scope thereof, as the specification may admit other equally effective aspects. The same figure numbers in different drawings may identify the same or similar elements.

[0019] Figure 1 is a diagram illustrating an example of a wireless network according to the present disclosure.

[0020] 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.

[0021] Figure 3 is a diagram illustrating an example decomposed base station architecture according to the present disclosure.

[0022] Figure 4 is a diagram illustrating an example of a channel state information reference signal beam management process according to the present disclosure.

[0023] Figure 5 is a diagram illustrating an example architecture of a functional framework for radio access network intelligence enabled by data collection according to the present disclosure.

[0024] Figure 6 is a diagram illustrating an example of an artificial intelligence / machine learning based beam management process according to the present disclosure.

[0025] FIG. 7A to FIG. 7B is a diagram illustrating an example associated with channel characteristic prediction based at least in part on a subset of downlink reference signal resources according to the present disclosure.

[0026] Figure 8 is a diagram of an example associated with channel characteristic prediction based at least in part on a subset of downlink reference signal resources in accordance with the present disclosure.

[0027] Fig. 9 is a diagram illustrating an example process performed, for example, by a UE according to the present disclosure.

[0028] Fig.10 is a diagram illustrating an example process, for example, performed by a network node, according to the present disclosure.

[0029] Fig.11 is a diagram of an example apparatus for wireless communications according to the present disclosure.

[0030] Fig.12 is a diagram of an example apparatus for wireless communications according to the present disclosure. DETAILED DESCRIPTION

[0031] The various aspects of the present disclosure are described more fully below with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms, and should not be interpreted as being limited to any specific structure or function presented throughout the present disclosure. On the contrary, these aspects are provided so that the present disclosure will be thorough and complete, and the scope of the present disclosure will be fully conveyed to those skilled in the art. It should be understood by those skilled in the art that the scope of the present disclosure is intended to cover any aspect of the disclosure disclosed herein, whether it is 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 a device or method that is 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.

[0032] Several aspects of telecommunication systems will now be presented with reference to various devices and techniques. These devices and techniques will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, etc. (collectively referred to as "elements"). These elements 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.

[0033] Although various aspects may be described herein using terms generally associated with 5G or new radio (NR) radio access technology (RAT), various aspects of the present disclosure may be applicable to other RATs, such as 3G RAT, 4G RAT and / or RATs beyond 5G (e.g., 6G).

[0034] 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 (shown as network node 110a, network node 110b, network node 110c, and network node 110d), user equipment (UE) 120 or multiple UEs 120 (shown as UE 120a, UE 120b, UE 120c, UE 120d, and UE 120e), and / or other entities. The network node 110 is a network node that communicates with the UE 120. As shown in the figure, the network node 110 may include one or more network nodes. For example, the network node 110 may be a converged network node, which means that the converged network node is configured to utilize a radio protocol stack that is physically or logically integrated within a single radio access network (RAN) node (e.g., within a single device or unit). As another example, the network node 110 may be a decomposed network node (sometimes referred to as a decomposed base station), which means that the network node 110 is configured to utilize a protocol stack that is physically or logically distributed between two or more nodes (such as one or more central units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)).

[0035] In some examples, the network node 110 is or includes a network node (such as an RU) that communicates with the UE 120 via a radio access link. In some examples, the 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, the 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 the core network via a backhaul link. In some examples, the network node 110 (such as an aggregated network node 110 or a decomposed network node 110) may include multiple network nodes, such as one or more RUs, one or more CUs, and / or one or more DUs. The network node 110 may include, for example, an NR base station, an LTE base station, a Node B, an eNB (e.g., in 4G), a gNB (e.g., in 5G), an access point, a transmit receive point (TRP), a DU, a RU, a CU, a mobility element of a network, a core network node, a network element, a network equipment, a RAN node, or a combination thereof. In some examples, network nodes 110 may be interconnected to each other or to one or more other network nodes 110 in wireless network 100 via various types of fronthaul, midhaul, and / or backhaul interfaces (such as direct physical connections, air interfaces, or virtual networks) using any suitable transport network.

[0036] In some examples, the network node 110 may provide communication coverage for a particular geographic area. In the Third Generation Partnership Project (3GPP), the term "cell" may refer to the coverage area of ​​the network node 110 and / or the network node subsystem serving the coverage area, depending on the context in which the term is used. The network node 110 may provide communication coverage for a macro cell, a pico cell, a femto cell, and / or another type of cell. A macro cell may cover a relatively large geographic area (e.g., a radius of several kilometers) and may allow unrestricted access by a UE 120 with a service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by a UE 120 with a service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by a UE 120 associated with the femto cell (e.g., a UE 120 in a closed subscriber group (CSG)). A network node 110 for a macro cell may be referred to as a macro network node. A network node 110 for a pico cell may be referred to as a pico network node. The network node 110 for a femto cell may be referred to as a femto network node or a home network node. Figure 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. The 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 the mobile network node 110 (e.g., a mobile network node).

[0037] 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 the network node 110. In some aspects, the term "base station" or "network node" may refer to a plurality of devices configured to perform one or more functions. For example, in some distributed systems, each of a number of different devices (which may be located in the same geographic location or in different geographic locations) may be configured to perform at least a portion of a function, or to repeat the performance of 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. Thus, a single device may include more than one base station.

[0038] The wireless network 100 may include one or more relay stations. A relay station is a network node that can receive transmissions of data from an upstream node (e.g., a network node 110 or a UE 120) and transmit transmissions of data to a downstream node (e.g., a UE 120 or a network node 110). A relay station may be a UE 120 that is capable of relaying transmissions for other UEs 120. Figure 1 In the example shown, a network node 110d (e.g., a relay network node) may communicate with a network node 110a (e.g., a macro network node) and a UE 120d to facilitate communications between the network node 110a and the UE 120d. A network node 110 that relays communications may be referred to as a relay station, a relay base station, a relay network node, a relay node, a relay, etc.

[0039] 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).

[0040] 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.

[0041] UE 120 can be distributed throughout the wireless network 100, and each UE 120 can be stationary or mobile. UE 120 can include, for example, an access terminal, a terminal, a mobile station and / or a subscriber unit. UE 120 can be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet computer, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device, a biometric device, a wearable device (e.g., a smart watch, smart clothing, smart glasses, a smart wristband, smart jewelry (e.g., a smart ring or a smart bracelet)), an entertainment device (e.g., a music device, a video device and / or a satellite radio), a vehicle component or sensor, a smart meter / sensor, an industrial manufacturing equipment, a global positioning system device, a UE function of a network node and / or any other suitable device configured to communicate via a wireless or wired medium.

[0042] Some UEs 120 may be considered as machine type communication (MTC) or evolved or enhanced machine type communication (eMTC) UEs. MTC UEs and / or eMTC UEs may include, for example, robots, drones, remote devices, sensors, meters, monitors, and / or location tags, which may communicate with a network node, another device (e.g., a remote device), or some other entity. Some UEs 120 may be considered as Internet of Things (IoT) devices, and / or may be implemented as NB-IoT (narrowband IoT) devices. Some UEs 120 may be considered as customer premises equipment. UE 120 may be included inside a housing that houses components of UE 120, such as a processor component and / or a memory component. In some examples, a processor component and a memory component may be coupled together. For example, a processor component (e.g., one or more processors) and a memory component (e.g., a memory) may be operably coupled, communicatively coupled, electronically coupled, and / or electrically coupled.

[0043] In general, any number of wireless networks 100 may be deployed in a given geographic area. Each wireless network 100 may support a specific RAT and may operate on one or more frequencies. RAT may be referred to as a radio technology, air interface, etc. Frequency may be referred to as a carrier, frequency channel, etc. Each frequency may support a single RAT in a given geographic area to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks may be deployed.

[0044] In some examples, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using network node 110 as an intermediary to communicate with each other). For example, UE 120 may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocols (e.g., which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, or vehicle-to-pedestrian (V2P) protocols), and / or mesh networks. In such examples, UE 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by network node 110.

[0045] The devices of the wireless network 100 may communicate using an electromagnetic spectrum, which may be subdivided into various categories, bands, channels, etc., based on frequency or wavelength. For example, the devices of the wireless network 100 may communicate using one or more operating bands. In 5G NR, two initial operating bands have been identified as frequency ranges designated FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz). It should be understood that although portions of FR1 are greater than 6 GHz, FR1 is often (interchangeably) referred to as a "below 6 GHz" band in various documents and articles. Similar naming issues sometimes occur with respect to FR2, which is often (interchangeably) referred to as a "millimeter wave" band in documents and articles, although different from the extremely high frequency (EHF) band (30 GHz-300 GHz) identified as a "millimeter wave" band by the International Telecommunication Union (ITU).

[0046] Frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR research has identified the operating bands for these mid-band frequencies as frequency range designation FR3 (7.125GHz-24.25GHz). The bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, so the features of FR1 and / or FR2 can be effectively extended to mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operations to more than 52.6GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6GHz-71GHz), FR4 (52.6GHz-114.25GHz) and FR5 (114.25GHz-300GHz). Each of these higher frequency bands falls within the EHF band.

[0047] With the above examples in mind, unless otherwise specifically stated, it should be understood that if the term "below 6 GHz" or the like is used herein, the term may broadly refer to frequencies that may be lower than 6 GHz, may be within FR1, or may include mid-band frequencies. 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 or FR4-1, and / or FR5, or may be within the EHF band. It is contemplated that the frequencies included in these operating bands (e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and / or FR5) may be modified, and the techniques described herein are applicable to those modified frequency ranges.

[0048] In some aspects, UE 120 may include communications manager 140. As described in more detail elsewhere herein, communications manager 140 may receive from network node 110 an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; and identify the subset of the first set of downlink reference signal resources based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources. Additionally or alternatively, communications manager 140 may perform one or more other operations described herein.

[0049] In some aspects, the network node 110 may include a communications manager 150. As described in greater detail elsewhere herein, the communications manager 150 may select a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; and send an indication of the technique for identifying the subset of the first set of downlink reference signal resources to the UE 120. Additionally or alternatively, the communications manager 150 may perform one or more other operations described herein.

[0050] As indicated above, Figure 1 are provided as examples. Other examples can be found in the Figure 1 The examples described are different.

[0051] Figure 2 2 is a diagram illustrating an example 200 of a network node 110 communicating with a UE 120 in a wireless network 100 according to the present disclosure. The network node 110 may be equipped with a set of antennas 234a to 234t, such as T antennas (T≥1). The UE 120 may be equipped with a set of antennas 252a to 252r, such as R antennas (R≥1). The network node 110 of example 200 includes one or more radio frequency components, such as an antenna 234 and a modem 254. In some examples, the network node 110 may include an interface, a communication component, or another component that facilitates communication with the UE 120 or another network node. Some network nodes 110 may not include a radio frequency component that facilitates direct communication with the UE 120, such as one or more CUs or one or more DUs.

[0052] At network node 110, transmit processor 220 may receive data intended for UE 120 (or a set 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. The transmit processor 220 may generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS) or demodulation reference signals (DMRS)) and synchronization signals (e.g., primary synchronization signals (PSS) or secondary synchronization signals (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, where applicable, and may provide a set of output symbol streams (e.g., T output symbol streams) to a corresponding set of modems 232 (e.g., T modems) (shown as modems 232a to 232t). For example, each output symbol stream may be provided to a modulator component (shown as MOD) of the modem 232. Each modem 232 may process a corresponding output symbol stream (e.g., for OFDM) using a corresponding modulator component to obtain an output sample stream. Each modem 232 may also process (e.g., convert to analog, amplify, filter, and / or upconvert) the output sample stream using a corresponding modulator component to obtain a downlink signal. The modems 232a through 232t may transmit a set of downlink signals (eg, T downlink signals) via a corresponding set of antennas 234 (eg, T antennas) (shown as antennas 234a through 234t).

[0053] At the UE 120, a set of antennas 252 (shown as antennas 252a to 252r) may receive downlink signals from the network node 110 and / or other network nodes 110 and may provide a set of received signals (e.g., R received signals) to a set of modems 254 (e.g., R modems) (shown as modems 254a to 254r). For example, each received signal may be provided to a demodulator component (shown as DEMOD) of the modem 254. Each modem 254 may use a corresponding demodulator component to condition (e.g., filter, amplify, downconvert, and / or digitize) the received signal to obtain input samples. Each modem 254 may use a demodulator component to further process the input samples (e.g., for OFDM) to obtain received symbols. A MIMO detector 256 may obtain received symbols from the modem 254, may perform MIMO detection on the received symbols where applicable, and may provide detected symbols. The receive processor 258 may process (e.g., demodulate and decode) the detected symbols, may provide decoded data for the UE 120 to the data sink 260, and may provide decoded control information and system information to the controller / processor 280. The term "controller / processor" may refer to one or more controllers, one or more processors, or a combination thereof. The channel processor may determine a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, and / or a CQI parameter, among other things. In some examples, one or more components of the UE 120 may be included in the housing 284.

[0054] 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.

[0055] One or more antennas (e.g., antennas 234a to 234t and / or antennas 252a to 252r) may include or be included within one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, and / or one or more antenna arrays, etc. Antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include one or more antenna elements (in a single housing or multiple housings), sets of coplanar antenna elements, sets of non-coplanar antenna elements, and / or may be coupled to one or more transmit and / or receive components (such as, Figure 2 One or more antenna elements of one or more components in.

[0056] On the uplink, at the UE 120, the transmit processor 264 may receive and process data from the data source 262 and control information from the controller / processor 280 (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI). The transmit processor 264 may generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be pre-decoded by the TX MIMO processor 266, where applicable, further processed by the modem 254 (e.g., for DFT-s-OFDM or CP-OFDM), and sent to the network node 110. In some examples, the modem 254 of the UE 120 may include a modulator and a demodulator. In some examples, the UE 120 includes a transceiver. The transceiver may include any combination of an antenna 252, a modem 254, a MIMO detector 256, a receive processor 258, a transmit processor 264, and / or a TX MIMO processor 266. The transceiver may be used by a processor (eg, controller / processor 280) and memory 282 to perform the functions described herein (eg, with reference to FIG. 7A to FIG. 12 ) or any aspects of any of the methods described herein.

[0057] At the network node 110, uplink signals from the UE 120 and / or other UEs may be received by the antenna 234, processed by the modem 232 (e.g., a demodulator component (shown as DEMOD) of the modem 232), detected by the MIMO detector 236 (where applicable), and further processed by the receive processor 238 to obtain decoded data and control information transmitted by the UE 120. The receive processor 238 may provide the decoded data to the data sink 239 and the decoded control information to the controller / processor 240. The network node 110 may include a communication unit 244 and may communicate with the network controller 130 via the communication unit 244. The network node 110 may include a scheduler 246 to schedule one or more UEs 120 for downlink communication and / or uplink communication. In some examples, the modem 232 of the network node 110 may include a modulator and a demodulator. In some examples, the network node 110 includes a transceiver. The transceiver may include any combination of an antenna 234, a modem 232, a MIMO detector 236, a receive processor 238, a transmit processor 220, and / or a TX MIMO processor 230. The transceiver may be used by a processor (e.g., controller / processor 240) and memory 242 to perform aspects of any of the methods described herein (e.g., reference 2 FIG. 7A to FIG. 12 ).

[0058] The controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other components of the controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or the like may perform one or more techniques associated with channel characteristic prediction based at least in part on a subset of downlink reference signal resources, as described in more detail elsewhere herein. Figure 2 Any other component of the can perform or direct e.g. Fig. 9 The process of 900 Fig.10 1000 and / or operations of other processes as described herein. Memory 242 and memory 282 may store data and program codes for network node 110 and UE 120, respectively. In some examples, memory 242 and / or memory 282 may include a non-transitory computer-readable medium storing one or more instructions (e.g., code and / or program code) for wireless communication. For example, the one or more instructions, when executed (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, Fig. 9 The process of 900 Fig.10 The process 1000 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.

[0059] In some aspects, the UE 120 includes means for receiving, from the network node 110, an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; and / or means for identifying the subset of the first set of downlink reference signal resources based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources. Means for the UE 120 to perform operations described herein may include, for example, one or more of the communications 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.

[0060] In some aspects, the network node 110 includes means for selecting a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; and / or means for sending an indication of the technique for identifying the subset of the first set of downlink reference signal resources to the UE 120. Means for the network node to perform operations described herein may include, for example, one or more of the communications 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.

[0061] In some aspects, a single processor may perform all functions described as being performed by one or more processors. In some aspects, one or more processors may collectively perform a set of functions. For example, a first (one or more) set of processors in one or more processors may perform a first function described as being performed by one or more processors, and a second (one or more) set of processors in one or more processors may perform a second function described as being performed by one or more processors. The first set of processors and the second set of processors may be the same set of processors or may be different sets of processors. References to "one or more processors" should be understood to refer to the combination of Figure 2 Any one or more of the processors described. References to "one or more memories" should be understood to refer to any one or more memories of the corresponding device, such as in conjunction with Figure 2 For example, functions described as being performed by one or more memories may be performed by the same subset of the one or more memories or by a different subset of the one or more memories.

[0062] Although Figure 2 The blocks in the 2000 and 2010 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.

[0063] As indicated above, Figure 2 are provided as examples. Other examples can be found in the Figure 2 The examples described are different.

[0064] The deployment of a communication system (such as a 5G NR system) can be arranged with various components or components in a variety of ways. In a 5G NR system or network, a network node, a network entity, a mobility element of a network, a RAN node, a core network node, a network element, a base station or network equipment can be implemented in an aggregated or decomposed architecture. For example, a base station (such as a node B (NB), an evolved NB (eNB), an NR 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 called an independent base station or a monolithic base station) or a decomposed base station. "Network entity" or "network node" may refer to a decomposed base station or one or more units of a decomposed base station (such as one or more CUs, one or more DUs, one or more RUs or a combination thereof).

[0065] An aggregated base station (e.g., an aggregated network node) may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node (e.g., within a single device or unit). A decomposed base station (e.g., a decomposed network node) may be configured to utilize a protocol stack that is physically or logically distributed between two or more units (such as one or more CUs, one or more DUs, or one or more RUs). In some examples, a CU may be implemented within a network node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed across one or more other network nodes. A DU may be implemented to communicate with one or more RUs. Each of a CU, a DU, and a RU may also be implemented as a virtual unit, such as a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU), and the like.

[0066] Base station type operations or network designs may take into account the aggregated nature of base station functionality. For example, a decomposed base station may be utilized in an IAB network, an open radio access network (O-RAN (such as a network configuration initiated by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)) to facilitate the scaling of a communication system by separating base station functionality into one or more units that can be deployed separately. A decomposed base station may include functionality implemented across two or more units at various physical locations, as well as functionality implemented virtually for at least one unit, which may enable flexibility in network design. Individual units of a decomposed base station may be configured for wired or wireless communication with at least one other unit of the decomposed base station.

[0067] Figure 33 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, which 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, or a non-RT RIC 315 associated with a service management and orchestration (SMO) framework 305, or both). The CU 310 may communicate with one or more DUs 330 via respective midhaul links, such as via an F1 interface. Each of the DUs 330 may communicate with one or more RUs 340 via respective fronthaul links. Each of the RUs 340 may communicate with one or more UEs 120 via respective radio frequency (RF) access links. In some implementations, a UE 120 may be served simultaneously by multiple RUs 340.

[0068] Each of the units (including CU 310, DU 330, RU 340) and the near-RT RIC 325, non-RT RIC 315, and SMO framework 305 may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively referred to as signals) via a wired or wireless transmission medium. Each of the units or an associated processor or controller that provides instructions to one or more communication interfaces of the corresponding unit may be configured to communicate with one or more of the other units via a transmission medium. In some examples, each of the units may include a wired interface and a wireless interface, the wired interface being configured to receive signals or transmit signals to one or more of the other units via a wired transmission medium, the wireless interface being configured to receive signals or transmit signals to one or more of the other units via a wired transmission medium, or both.

[0069] In some aspects, the CU 310 may host one or more higher layer control functions. Such control functions may include radio resource control (RRC) functions, packet data convergence protocol (PDCP) functions, or service data adaptation protocol (SDAP) functions, etc. Each control function may be implemented using an interface that is configured to communicate signals with other control functions hosted by the CU 310. The CU 310 may be configured to handle user plane functionality (e.g., central unit-user plane (CU-UP) functionality), control plane functionality (e.g., central unit-control plane (CU-CP) functionality), or a combination thereof. In some specific implementations, the CU 310 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (such as an E1 interface). As needed, the CU 310 may be implemented to communicate with the DU 330 for network control and signaling.

[0070] Each DU 330 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RUs 340. In some aspects, the DU 330 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers, at least in part according to a functional split (such as a functional split defined by 3GPP). In some aspects, the one or more high PHY layers may be implemented by one or more modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc. In some aspects, the DU 330 may also host one or more low PHY layers, such as one or more modules for fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming or physical random access channel (PRACH) extraction and filtering, etc. Each layer (which may also be referred to as a module) may be implemented using an interface that is configured to communicate signals with other layers (and modules) hosted by the DU 330 or with control functions hosted by the CU 310.

[0071] Each RU 340 may implement lower layer functionality. In some deployments, the RU 340 controlled by the DU 330 may correspond to a logical node that hosts RF processing functions, or low PHY layer functions (such as performing FFT, performing iFFT, digital beamforming, or PRACH extraction and filtering, etc.) based on functional splitting (e.g., functional splitting defined by 3GPP). In this architecture, each RU 340 may be operated to handle over-the-air (OTA) communications with one or more UEs 120. In some specific implementations, real-time and non-real-time aspects of control plane and user plane communications with the RU 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration may enable each DU 330 and CU 310 to be implemented in a cloud-based RAN architecture such as a vRAN architecture.

[0072] The SMO framework 305 may be configured to support RAN deployment and provisioning of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 305 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO framework 305 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) platform 390) to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements may include, but are not limited to, CU 310, DU 330, RU 340, non-RT RIC 315, and near-RT RIC 325. In some specific implementations, the SMO framework 305 may communicate with hardware aspects of the 4G RAN (such as an open eNB (O-eNB) 311) via the O1 interface. Additionally, in some implementations, the SMO framework 305 can communicate directly with each of the one or more RUs 340 via a corresponding O1 interface. The SMO framework 305 can also include a non-RT RIC 315 configured to support the functionality of the SMO framework 305.

[0073] The non-RT RIC 315 may be configured to include logic functions that enable non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updating, or policy-based guidance of applications / features in the near-RT RIC 325. The non-RT RIC 315 may be coupled to or in communication with the near-RT RIC 325 (such as via an A1 interface). The near-RT RIC 325 may be configured to include logic functions that enable near-real-time control and optimization of RAN elements and resources via data collection and actions through an interface (such as via an E2 interface) that connects one or more CUs 310, one or more DUs 330, or both, and the O-eNB with the near-RT RIC 325.

[0074] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 325, the non-RT RIC 315 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 325 and may be received from a non-network data source or from a network function at the SMO framework 305 or the non-RT RIC 315. In some examples, the non-RT RIC 315 or the near-RT RIC 325 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 315 may monitor long-term trends and patterns of performance and employ AI / ML models to perform corrective actions through the SMO framework 305 (such as via reconfiguration of the O1 interface) or via the creation of RAN management policies (such as A1 interface policies).

[0075] As indicated above, Figure 3 are provided as examples. Other examples can be found in the Figure 3 The examples described are different.

[0076] Figure 4 4 is a diagram illustrating examples 400, 410, and 420 of a channel state information (CSI) reference signal (CSI-RS) 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 a transmit receive point (TRP), between a mobile terminal node and a control node, between an integrated access and backhaul (IAB) child node and an IAB parent node, between a scheduled node and a scheduling node). In some aspects, UE 120 and network node 110 may be in a connected state (e.g., an RRC connected state).

[0077] like Figure 4 As shown, example 400 may include a network node 110 (e.g., one or more network node devices such as RU, DU and / or CU, etc.) and a UE 120 communicating to perform beam management using CSI-RS. Example 400 depicts a first beam management process (e.g., P1CSI-RS beam management). The first beam management process may be referred to as a beam selection process, an initial beam acquisition process, a beam scanning process, a cell search process, and / or a beam search process. Figure 4 As shown in example 400, the CSI-RS may be configured to be sent from the network node 110 to the UE 120. The CSI-RS may be configured to be periodic (e.g., using RRC signaling), semi-persistent (e.g., using medium access control (MAC) control element (MAC-CE) signaling), and / or aperiodic (e.g., using downlink control information (DCI)).

[0078] The first beam management process may include the network node 110 performing beam scanning on multiple transmit (Tx) beams. The network node 110 may use each transmit beam used for beam management to transmit a CSI-RS. In order to enable the UE 120 to perform receive (Rx) beam scanning, the network node may use the transmit beam to transmit each CSI-RS multiple times (e.g., with repetitions) within the same RS resource set, so that the UE 120 can sweep the receive beam in multiple transmit instances. For example, if the network node 110 has a set of N transmit beams and the UE 120 has a set of M receive beams, the CSI-RS may be transmitted M times on each of the N transmit beams, so that the UE 120 may receive M instances of the CSI-RS per transmit beam. In other words, for each transmit beam of the network node 110, the UE 120 may perform beam scanning on the receive beam of the UE 120. Thus, the first beam management process 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 process may also use synchronization signal blocks (SSBs) to perform beam management in a similar manner as described above.

[0079] like Figure 4As 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). The 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 4 As shown in example 410, the CSI-RS may be configured to be transmitted from the network node 110 to the UE 120. The CSI-RS may be configured to be non-periodic (e.g., using DCI). The second beam management process may include the 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 the network node 110 (e.g., determined at least in part based on measurements reported by the UE 120 in conjunction with the first beam management process). The network node 110 may transmit the CSI-RS using each of the one or more transmit beams used for beam management. The 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 process). The second beam management process may enable the network node 110 to select the best transmit beam based at least in part on measurements of the CSI-RS reported by the UE 120 (e.g., measured by the UE 120 using a single receive beam).

[0080] like Figure 4 As shown, example 420 depicts a third beam management process (e.g., P3 CSI-RS beam management). The 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 4As shown in example 420, one or more CSI-RS may be configured to be sent from the network node 110 to the UE 120. The CSI-RS may be configured to be non-periodic (e.g., using DCI). The third beam management process may include the network node 110 sending the one or more CSI-RS 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 process and / or the second beam management process). In order to enable the UE 120 to perform receive beam scanning, the network node 110 may use the transmit beam to send (e.g., using repetition) the CSI-RS multiple times within the same RS resource set, so that the UE 120 can sweep across 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 process and / or the second beam management process). The third beam management process may enable the network node 110 and / or the UE 120 to select the best receive beam based at least in part on reported measurements received from the UE 120 (e.g., the reported measurements are measurements of the CSI-RS of the transmit beam using the one or more receive beams).

[0081] In some aspects, channel characteristics associated with one or more of the beams described above in connection with the various beam management processes may be directly measured by a wireless communication device, such as by UE 120. However, in some other aspects, channel characteristics associated with one or more of the beams described above in connection with the various beam management processes may be inferred and / or predicted based at least in part on channel characteristics associated with other beams, such as via the use of artificial intelligence (AI) and / or machine learning (ML) models. Figure 5 and Figure 6 Various aspects of using channel characteristics of certain beams to predict channel characteristics associated with other beams are described in greater detail.

[0082] 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 Figure 4 For example, UE 120 and network node 110 may perform a third beam management procedure before performing a second beam management procedure, and / or UE 120 and network node 110 may perform a similar beam management procedure to select a beam for UE 120 to transmit.

[0083] Figure 5is a diagram of an example architecture 500 illustrating a functional framework for RAN intelligence enabled by data collection according to the present disclosure. In some scenarios, the functional framework for RAN intelligence may be implemented by further enhancing data collection through use cases and / or examples. For example, principles or algorithms of RAN intelligence enabled by AI / ML and associated functional frameworks (e.g., AI functionality and / or input / output of components for enabling AI optimization) have been utilized or studied to identify the benefits of an AI-enabled RAN through possible use cases (e.g., beam management, energy saving, load balancing, mobility management and / or coverage optimization, etc.). In one example, as shown in architecture 500, the functional framework for RAN intelligence may include multiple logical entities, such as a model training host 502, a model inference host 504, a data source 506, and participants 508.

[0084] The model inference host 504 may be configured to run an AI / ML model based on the inference data provided by the data source 506, and the model inference host 504 may generate output (e.g., prediction) using the inference data input to the participant 508. The participant 508 may be an element or entity of the core network or RAN. For example, the participant 508 may be a UE 120, a network node 110, a base station (e.g., a gNB), a CU, a DU, and / or a RU, etc. In addition, the participant 508 may also depend on the type of task performed by the model inference host 504, the type of inference data provided to the model inference host 504, and / or the type of output generated by the model inference host 504. For example, if the output from the model inference host 504 is associated with beam management, the participant 508 may be a UE 120, a DU, or a RU, and if the output from the model inference host 504 is associated with transmit and / or receive scheduling, the participant 508 may be a CU or a DU.

[0085] After the participant 508 receives the output from the model inference host 504, the participant 508 may determine whether to take an action based on the output. For example, if the participant 508 is a DU or RU, and the output from the model inference host 504 is associated with beam management, the participant 508 may determine whether to change / modify the transmit and / or receive beams based on the output. If the participant 508 determines an action based on the output, the participant 508 may indicate the action to at least one action subject 510. For example, if the participant 508 determines to change / modify the transmit and / or receive beams used for communication between the participant 508 and the action subject 510 (e.g., UE 120), the participant 508 may send a beam (re) configuration or beam switching indication to the action subject 510. The participant 508 may modify its transmit and / or receive beams based on the beam (re) configuration, such as switching to a new transmit and / or receive beam or applying different parameters to the transmit and / or receive beams, etc. As another example, participant 508 may be UE 120, and the output from model inference host 504 may be associated with beam management. For example, the output may be one or more predicted measurement values ​​for one or more beams. Participant 508 (e.g., UE 120) may determine to send a measurement report (e.g., a layer 1 (L1) RSRP report) to network node 110.

[0086] The data source 506 may also be configured to collect data that is used as training data for training the ML model or as inference data for feeding the ML model inference operation. For example, the data source 506 may collect data from one or more core network and / or RAN entities (which may include an action subject 510) and provide the collected data to the model training host 502 for ML model training. For example, after the action subject 510 (e.g., UE 120) receives the beam configuration from the participant 508, the action subject 510 may provide performance feedback associated with the beam configuration to the data source 506, wherein the performance feedback may be used by the model training host 502 to monitor or evaluate ML model performance, such as whether the output (e.g., prediction) provided to the participant 508 is accurate. In some examples, if the output provided by the participant 508 is inaccurate (or the accuracy is below an accuracy threshold), the model training host 502 may determine to modify or retrain the ML model used by the model inference host, such as via ML model deployment / update.

[0087] As indicated above, Figure 5 are provided as examples. Other examples can be found in the Figure 5 The examples described are different.

[0088] Figure 6 600 is a diagram illustrating an example of an AI / ML-based beam management process according to the present disclosure. Figure 6 As shown, the AI / ML model 610 may be deployed at or on the UE 120. For example, a model inference host (such as the model inference host 504) may be deployed at or on the UE 120. The AI / ML model 610 may enable the UE 120 to determine one or more inferences or predictions based on data input to the AI / ML model 610.

[0089] For example, as shown by reference numeral 615, the input to the AI / ML model 610 may include measurement values ​​associated with the first beam set. For example, the network node 110 may send one or more signals via corresponding beams from the first beam set. The UE 120 may perform measurements (e.g., L1 RSRP measurements or other measurements) on the first beam set or a subset thereof to obtain a first set of measurement values ​​(sometimes referred to as channel characteristics). For example, each beam (or a subset thereof) from the first beam set may be associated with one or more measurements performed by the UE 120. The UE 120 may input the first set of measurement values ​​(e.g., L1 RSRP measurement values) into the AI / ML model 610 together with information associated with the first beam set (or a subset thereof) and / or the second beam set (such as beam direction (e.g., spatial direction), beam width, beam shape, and / or other characteristics of corresponding beams from the first beam set (or a subset thereof) and / or the second beam set).

[0090] As shown by reference numeral 620, the AI / ML model 610 may output one or more predictions. The one or more predictions may include predicted measurement values ​​and / or channel characteristics associated with the second set of beams (e.g., predicted L1 RSRP measurement values). This may reduce the number of beam measurements performed by the UE 120, thereby saving power of the UE 120 and / or network resources that would otherwise be used to measure all beams included in the first set of beams and the second set of beams. This type of prediction may be referred to as codebook-based spatial domain selection or prediction.

[0091] As another example, the output of the AI / ML model 610 may include the point direction, angle of departure (AoD), and / or angle of arrival (AoA) of the beams included in the second beam set. This type of prediction may be referred to as non-codebook based spatial domain selection or prediction. As another example, multiple measurement reports or values ​​collected at different time points may be input to the AI / ML model 610. This may enable the AI / ML model 610 to output codebook based and / or non-codebook based predictions of measurement values, AoD, and / or AoA, etc. for beams at future times. As described herein, the output of the AI / ML model 610 may facilitate an initial access process, a secondary cell group (SCG) setup process, a beam refinement process (e.g., as described above in conjunction with Figure 4The described P2 beam management process or P3 beam management process), link quality or interference adaptation process, beam failure and / or beam blocking prediction, and / or radio link failure prediction, etc.

[0092] In some examples, beam measurement predictions may be made by (e.g., Figure 6 1 and / or by the network node 110 in a manner similar to that described above. For example, the network node 110 may receive one or more measurement values ​​(e.g., from measurements performed by the UE 120) and may use the AI / ML model 610 to predict one or more measurement values ​​(e.g., of other beams) based at least in part on the one or more measurement values ​​associated with the measurements performed by the UE 120. For example, the prediction may be performed by the network node 110 because the network node 110 may have more processing resources and / or greater processing power than the UE 120. In addition, the network node 110 may have access to historical measurement reports and / or measurement reports from other UEs, which may be used as inputs to the AI / ML model 610 (e.g., which may improve the accuracy of the output of the AI / ML model 610). The prediction may be performed by the UE 120 because the UE 120 may have access to filtered measurements of all beams (e.g., not all measurements may be reported to the network node 110). Additionally, the UE 120 may have information about the receive beam used to derive or perform the measurement (e.g., which may be a useful input to the AI / ML model 610). As another example, the measurement information at the UE 120 may be "raw" or non-quantized, thereby providing more information that may be input into the AI / ML model 610. Additionally, the UE 120 may know the orientation or rotational position of the UE 120.

[0093] In some examples, a first set of beams (e.g., measured) may be referred to as set B beams, and a second set of beams (e.g., associated with predicted measurement values) may be referred to as set A beams. In some examples, a first set of beams (e.g., set B beams) may be a subset of a second set of beams (e.g., set A beams). In some other examples, the first set of beams and the second set of beams may be different beams and / or may be mutually exclusive sets. For example, a first set of beams (e.g., set B beams) may include wide beams (e.g., unrefined beams or beams with a beamwidth that satisfies a first threshold), and a second set of beams (e.g., set A beams) may include narrow beams (e.g., refined beams or beams with a beamwidth that satisfies a second threshold), such as Figure 6In one example, the AI / ML model 610 may perform spatial domain downlink beam prediction for beams included in the set A beams based on measurement results of beams included in the set B beams. As another example, the AI / ML model 610 may perform temporal downlink beam prediction for beams included in the set A beams based on historical measurement results of beams included in the set B beams.

[0094] In some examples, UE 120 may be configured with a first set of beams (e.g., set B beams), but UE 120 may use only a subset of the first set of beams to determine predicted channel characteristics associated with a second set of beams (e.g., set A beams). In other words, the measurements of set B beams indicated by reference numeral 615 (which are input into AI / ML model 610 in order to determine predicted channel characteristics associated with set A beams) may be associated with a subset of set B beams. This may be because when AI / ML model 610 is configured as Figure 6 When resident at UE 120 as shown, AI / ML model 610 may be simplified to reduce memory and / or computational resources, and thus AI / ML model 610 may not be able to input all measurements and / or channel characteristics associated with all beams of set B beams. For example, network node 110 may configure UE 120 with 64 SSBs for UE 120 to measure (e.g., set B beams may include 64 beams), but UE 120 may only use measurements and / or channel characteristics associated with eight SSBs as input to AI / ML model 610 (e.g., the number of SSB identifiers and / or associated L1 RSRPs used as input at a given time-domain measurement occasion may be eight). The number of beams associated with the first set of beams (e.g., 64 in the above example) is sometimes referred to as N1, and the number of beams associated with a subset of the first set of beams used as input to AI / ML model 610 is sometimes referred to as N2. Therefore, returning to the above example, in some cases, N2 may be much smaller than N1 (eg, N1 >> N2 ).

[0095] In some cases, the UE 120 may use another AI / ML model (e.g., a different model than the AI / ML model 610) and / or an analysis method to determine which subset of the first set of beams should be used as input to the AI / ML model 610. For example, a previously used SSB (e.g., a previously used beam associated with N2) and / or a most recent or previously predicted L1 RSRP of a second set of beams (e.g., set A beams) may be used as input to the AI / ML model and / or an analysis method to determine a subset of the first set of beams to be used as input to the AI / ML model 610. In some cases, the subset of the first set of beams used as input to the AI / ML model 610 to predict channel characteristics associated with the second set of beams (e.g., set A beams) is referred to as set C beams.

[0096] The AI / ML model and / or analysis method used to determine the set C-beam may vary depending on the specific implementation of the UE 120 and / or may be transparent to the network node 110. Therefore, the set C-beam selected by the UE 120 may result in inaccurate channel characteristic predictions, resulting in the network node 110 and / or the UE 120 using low-quality beams and / or channels for communication. This may result in degradation of communication quality, resulting in error-prone communications, and thus increased power, computation, and network resource consumption for correcting communication errors. Additionally or alternatively, by communicating using low-quality beams and / or channels, the UE 120 and / or the network node 110 may experience increased latency, reduced throughput, and otherwise unreliable communications or even radio link failures.

[0097] Some techniques and apparatus described herein enable signaling from a network node 110 to a UE 120 to configure and / or indicate a technique (e.g., an analysis method and / or an AI / ML model) for determining a set C beam from a set B beam for the purpose of predicting channel characteristics associated with the set A beam. For example, in some aspects, the network node 110 may select a technique (e.g., an analysis method and / or an AI / ML model) for identifying a subset (e.g., a set C beam) of a first set (e.g., a set B beam) of downlink reference signal resources associated with a particular measurement opportunity, and the network node 110 may send an indication of the technique for identifying the subset of the first set of downlink reference signal resources to the UE 120. Based at least in part on the technique indicated by the network node 110, the UE 120 may identify the subset (e.g., the set C beam) of the first set of downlink reference signal resources, and may thus determine, based at least in part on the subset of the first set of downlink reference signal resources, predicted channel characteristics associated with a second set (e.g., the set A beam) of downlink reference signal resources associated with the measurement opportunity. Therefore, the set C beam selected by the UE 120 may result in a more accurate and robust prediction of channel characteristics, thereby causing the network node 110 and / or the UE 120 to communicate using high-quality beams and / or channels. This may result in improved communication quality, resulting in a low error rate communication channel, and thus reducing power, computation, and network resource consumption that would otherwise be used to correct communication errors. Additionally or alternatively, by using high-quality beams and / or channels for communication, the UE 120 and / or the network node 110 may experience reduced latency, increased throughput, and otherwise more reliable communication channels, and thus more efficiently use network resources.

[0098] As indicated above, Figure 6 are provided as examples. Other examples can be found in the Figure 6 The examples described are different.

[0099] FIG. 7A to FIG. 7B is a diagram illustrating an example 700 associated with channel characteristic prediction based at least in part on a subset of downlink reference signal resources in accordance with the present disclosure.

[0100] As shown in reference numeral 702, UE 120 may be configured with a first set of downlink reference signals (e.g., CSI-RS, SSB, or similar reference signals) associated with an input to an AI / ML model (e.g., AI / ML model 610) for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with a measurement opportunity. As described above, the first set of downlink reference signals may be referred to as set B beams, and the second set of downlink reference signals may be referred to as set A beams. In the depicted example 700, set B beams includes 16 downlink reference signals and / or beams, Fig. 7A and Figure 7B In some other aspects, more or fewer downlink reference signals and / or beams may be implemented without departing from the scope of the present disclosure. For example, in some aspects, set B beams may include 64 downlink reference signals and / or beams (e.g., 64 SSBs), as described above in conjunction with Figure 6 as described.

[0101] In some aspects, the UE 120 may be associated with an analysis method or AI / ML model 704 that may be used to determine a subset of the set B beams to be used as an input to an AI / ML model (e.g., AI / ML model 610) for determining predicted channel characteristics associated with the set A beams. In some aspects, the network node 110 may select a particular analysis method or AI / ML model 704 to be used by the UE 120 for a given measurement occasion, the network node 110 may configure the UE 120 with the particular analysis method or AI / ML model 704 to be used by the UE 120 for a given measurement occasion, and / or the network node 110 may indicate to the UE 120 the particular analysis method or AI / ML model 704 to be used by the UE 120 for a given measurement occasion, as indicated by reference numeral 706. Figure 8 Various aspects of configuring and / or signaling a particular analysis method or AI / ML model 704 to be used by the UE 120 for a given measurement occasion are described in greater detail.

[0102] In some aspects, an analytical method or AI / ML model 704 may be used to determine a subset of set B beams to be used as input to an AI / ML model (e.g., AI / ML model 610) for measurement opportunities 708. The subset of set B beams to be used as input to the AI / ML model may be referred to as set C beams, which are illustrated using stippling in the beams indicated by reference numeral 710. More specifically, in the depicted example, the set C beams used for measurement opportunities 708 include four of the sixteen set B beams: beam 8, beam 10, beam 12, and beam 16. While in the depicted example 700, the set C beams include four downlink reference signals and / or beams, in some other aspects, more or fewer downlink reference signals and / or beams may be implemented without departing from the scope of the present disclosure. For example, in some aspects, set B beams may include 64 downlink reference signals and / or beams (e.g., 64 SSBs), and set C beams may include a subset of eight of the 64 downlink reference signals and / or beams, as described above in conjunction with Figure 6 as described.

[0103] In some aspects, the input to the analysis method or AI / ML model 704 may be associated with one or more previously determined subsets (e.g., a certain number of previously determined set C beams) of the first set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion 708, as indicated by reference numeral 712. For example, in Fig. 7A In the illustrated aspects, four previously determined subsets of a first set 714 of downlink reference signal resources (e.g., four previously determined set C-beams) are shown, which are indexed as 714-1 through 714-4. Each of the previously determined subsets of the first set 714 of downlink reference signal resources may correspond to a subset of the first set of downlink reference signal resources (e.g., set C-beams) determined in a corresponding measurement opportunity prior to the measurement opportunity 708. More specifically, the first previously determined subset 714-1 of the first set of downlink reference signal resources may correspond to a first measurement opportunity prior to the measurement opportunity 708 and may include (e.g., using Fig. 7A708 and may include beam 1, beam 6, beam 10, and beam 11. A second previously determined subset 714-2 of the first set of downlink reference signal resources may correspond to a second measurement opportunity before the measurement opportunity 708 and may include beam 1, beam 7, beam 10, and beam 15. A third previously determined subset 714-3 of the first set of downlink reference signal resources may correspond to a third measurement opportunity before the measurement opportunity 708 and may include beam 2, beam 4, beam 10, and beam 13. And a fourth previously determined subset 714-4 of the first set of downlink reference signal resources may correspond to a fourth measurement opportunity before the measurement opportunity 708 and may include beam 1, beam 6, beam 8, and beam 16. Additionally or alternatively, input to the analysis method or AI / ML model 704 may be associated with additional information associated with one or more previously determined subsets of the first set of downlink reference signal resources indicated by reference numeral 712, such as previously measured channel characteristics and / or measurement values ​​associated with each subset of the first set 714 of downlink reference signal resources.

[0104] In some other aspects, the input to the analysis method or AI / ML model 704 can be associated with predicted channel characteristics of a second set of downlink reference signal resources (e.g., set A beams) associated with one or more measurement occasions prior to the measurement occasion 708, as indicated by reference numeral 716. For example, in Fig. 7A The aspects shown show previously determined predicted channel characteristics (at Fig. 7A Each of the sets of previously determined predicted channel characteristics for the second set of downlink reference signal resources 718 may correspond to a set of predicted channel characteristics for the second set of downlink reference signal resources (e.g., set A beams) determined in a corresponding measurement opportunity prior to measurement opportunity 708 (such as by using an AI / ML model (e.g., AI / ML model 610) or similar techniques).

[0105] Based at least in part on one or more previously determined subsets of the first set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion 708 and / or information associated therewith (e.g., channel characteristics associated with each previously determined beam subset) as indicated by reference numeral 712 and / or predicted channel characteristics of the second set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion 708 as indicated by reference numeral 716, the UE 120 may determine the set C-beam to be used for the measurement occasion 708 using the analysis method or AI / ML model 704 as indicated by reference numeral 710. Additionally or alternatively, the analysis method or AI / ML model 704 may utilize additional inputs instead of or in addition to the one or more measurement occasions prior to the measurement occasion 708 as indicated by reference numeral 712 and / or predicted channel characteristics of the second set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion 708 as indicated by reference numeral 716. For example, in some aspects, input to the analysis method or AI / ML model 704 can be based at least in part on channel characteristics of a first set of downlink reference signals (e.g., a set B beam) indicated by reference numeral 702 measured at measurement opportunity 708, and / or channel characteristics of the first set of downlink reference signals measured in one or more measurement opportunities prior to measurement opportunity 708.

[0106] In some aspects, determining a subset of the set B beams (e.g., the set C beams indicated by reference numeral 710) to be used as input to an AI / ML model (e.g., AI / ML model 610) for measurement opportunity 708 can be based at least in part on one or more sets of channel measurement resources (CMRs), such as Figure 7B For example, in some aspects, UE 120 may receive CSI reporting settings 720 from network node 110 that may be used to configure a CSI report for use by UE 120 to report predicted channel characteristics associated with a second set of downlink reference signal resources (e.g., set A beams) associated with measurement opportunity 708 to network node 110. In such aspects, CSI reporting settings 720 may indicate or otherwise be associated with a plurality of CMR sets indicated by reference numeral 722, such as in Figure 7B7. Each CMR set may be associated with a set of candidate beams to be used as a set C-beam for measurement opportunity 708. In other words, the CSI report setting 720 may be associated with multiple CMR sets (e.g., CMR set #0 to CMR set #7 in the depicted example) that are options for a subset from a first set of downlink reference signal resources (e.g., as options for a set C-beam). In such aspects, the network node 110 indicating and / or configuring the analysis method or AI / ML model 704 may output at least a CMR set identifier (sometimes referred to as a CMR-Set-ID) associated with the set C-beam, such as Figure 7B As indicated by the arrow accompanying the reference numeral 724.

[0107] Based at least in part on the network node 110 signaling to the UE 120 an indication of the technique for determining the set C-beam, the UE 120 and / or the network node 110 may save computational, power, network, and / or communication resources that may otherwise be consumed when communicating based at least in part on channel prediction determined at least in part by the set C-beam autonomously selected by the UE 120 and / or determined according to a specific implementation of the UE 120. For example, based at least in part on the network node 110 signaling to the UE 120 an indication of the technique for determining the set C-beam, the UE 120 and the network node 110 may communicate at a reduced error rate, which may save computational, power, network, and / or communication resources that may otherwise be consumed in detecting and / or correcting communication errors. Figure 8 Further details of the network node 110 signaling to the UE 120 an indication of a technique for determining the set C-beam are described in more detail.

[0108] As indicated above, FIG. 7A to FIG. 7B are provided as examples. Other examples can be found in the FIG. 7A to FIG. 7B The examples described are different.

[0109] Figure 8 8 is a diagram of an example 800 associated with channel characteristic prediction based at least in part on a subset of downlink reference signal resources according to the present disclosure. Figure 8 As shown, a network node 110 (e.g., a CU, DU, and / or RU) may communicate with a UE 120. In some aspects, the network node 110 and the UE 120 may be part of a wireless network (e.g., the wireless network 100). The UE 120 and the network node 110 may be in a Figure 8 The operations shown may have previously established a wireless connection. For example, in some aspects, UE 120 and network node 110 may have established a wireless connection via beamforming, such as by using the above combined Figure 4One or more of the described beam management processes.

[0110] As shown by reference numeral 805, the network node 110 may select a technique for identifying a subset of a first set of downlink reference signal (DL-RS) resources associated with a measurement opportunity (e.g., measurement opportunity 708). In some aspects, the first set of downlink reference signal resources may correspond to a set B beam, such as the set B beam described above in conjunction with reference numeral 702. Further, in some aspects, the subset of the first set of downlink reference signal resources may correspond to a set C beam, such as the set C beam described above in conjunction with reference numeral 710. In this regard, the subset of the first set of downlink reference signal resources (e.g., set C beam) may be associated with an input to a model (e.g., AI / ML model 610) for determining predicted channel characteristics associated with a second set of downlink reference signal resources (e.g., set A beams) associated with the measurement opportunity. In some aspects, the second set of downlink reference signal resources (e.g., set A beams) may be associated with real downlink reference signal resources and / or virtual downlink reference signal resources.

[0111] In some aspects, techniques for identifying a subset of the first set of downlink reference signal resources associated with a measurement opportunity may correspond to the above with respect to Fig. 7A and Figure 7B The analysis method or AI / ML model 704 described. At this point, in some aspects, the technique for identifying a subset of the first set of downlink reference signal resources associated with the measurement opportunity may correspond to the analysis method and / or formula for identifying the subset of the first set of downlink reference signal resources, while in some other aspects, the technique for identifying the subset of the first set of downlink reference signal resources associated with the measurement opportunity may correspond to the AI / ML model for identifying the subset of the first set of downlink reference signal resources. Various aspects of the technique for identifying a subset of the first set of downlink reference signal resources associated with the measurement opportunity are described in more detail below in conjunction with reference numeral 820.

[0112] As indicated by reference numeral 810, the network node 110 may send an indication of a technique for identifying a subset of the first set of downlink reference signal resources associated with the measurement opportunity, and the UE 120 may receive the indication. In some aspects, the indication of the technique for identifying the subset of the first set of downlink reference signal resources associated with the measurement opportunity may be indicated via configuration information, such as via an RRC configuration or reconfiguration message sent by the network node 110 to the UE 120. In such aspects, the indication of the technique for identifying the subset of the first set of downlink reference signal resources associated with the measurement opportunity may be sent as part of another configuration communication and / or may be sent with additional configuration information, such as the configuration information described below in conjunction with reference numeral 815. In some other aspects, the network node 110 may send the indication of the technique for identifying the subset of the first set of downlink reference signal resources associated with the measurement opportunity via a MAC-CE communication, via a DCI communication, or via a similar communication.

[0113] As indicated by reference numeral 815, the network node 110 may send configuration information, and the UE 120 may receive the configuration information. In some aspects, the UE 120 may receive the configuration information via one or more of RRC signaling, one or more MAC-CEs, and / or DCI, etc. In some aspects, the configuration information may include an indication of one or more configuration parameters for selection by the UE 120 (e.g., already known to the UE 120 and / or previously indicated by the network node 110 or other network device), and / or explicit configuration information for use by the UE 120 to configure the UE 120, etc.

[0114] In some aspects, the configuration information may include an indication of a technique for identifying a subset of the first set of downlink reference signal resources associated with the measurement opportunity, as described above in conjunction with reference numeral 810. Additionally or alternatively, the configuration information may include additional configuration information, such as an indication of multiple candidate subsets of downlink reference signal resources (e.g., multiple candidate set C beams). More specifically, in some aspects, the configuration information may include multiple CMR sets (e.g., consisting of Figure 7B For example, the network node 110 may send a CSI reporting setting (e.g., CSI reporting setting 720) associated with a CSI report for reporting predicted channel characteristics associated with a second set of downlink reference signal resources associated with a measurement opportunity (this is described in more detail below in conjunction with reference numeral 840). In such aspects, the CSI reporting setting may indicate a plurality of CMR sets.

[0115] UE 120 may configure itself based at least in part on the configuration information.In some aspects, UE 120 may be configured to perform one or more operations described herein based at least in part on the configuration information.

[0116] As shown by reference numeral 820, UE 120 may identify a subset of the first set of downlink reference signal resources (e.g., UE 120 may identify a set C beam) based at least in part on a technique for identifying a subset of the first set of downlink reference signal resources. Fig. 7A and Figure 7B The described analysis method or AI / ML model 704 is used to identify a subset of the first set of downlink reference signal resources. At this point, the technique for identifying the subset of the first set of downlink reference signal resources associated with the measurement opportunity is associated with one of the formula or the machine learning model. More specifically, in some aspects, the technique for identifying the subset of the first set of downlink reference signal resources can be based at least in part on one or more previously determined subsets of the first set of downlink reference signal resources associated with one or more measurement opportunities prior to the measurement opportunity, such as described above in conjunction with reference numeral 712.

[0117] Additionally or alternatively, the technique for identifying a subset of the first set of downlink reference signal resources may be further based at least in part on channel characteristics associated with one or more previously determined subsets of the first set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion. As used herein, a channel characteristic may refer to one or more measurement values ​​or associated information associated with a reference signal (e.g., a beam) measured in a measurement occasion. For example, in some aspects, a channel characteristic associated with a reference signal (e.g., a beam) may include an RSRP measurement associated with the reference signal, a signal-to-noise-plus-interference ratio (SINR) associated with the reference signal, a precoding matrix indicator (PMI) associated with the reference signal, a CQI associated with the reference signal, a CSI-RS resource indicator (CRI) associated with the reference signal, or a rank indication (RI) associated with the reference signal.

[0118] In some aspects, techniques for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity may be based at least in part on a formula (e.g., an analytical method) associated with a channel characteristic associated with one or more previously determined subsets of the first set of downlink reference signal resources. For example, in some aspects, resources having predicted and measured channel characteristics (e.g., predicted and / or measured L1RSRP) associated with the measurement opportunity that are greater than a threshold may be identified as a subset of the first set of downlink reference signal resources (e.g., set C beams). In some other aspects, techniques for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity may be based at least in part on an AI and / or ML model associated with a channel characteristic associated with one or more previously determined subsets of the first set of downlink reference signal resources. For example, UE 120 may be configured with an AI / ML model, the input to which includes: one or more previously determined subsets of a first set of downlink reference signal resources associated with one or more measurement occasions before the measurement occasion (described above in conjunction with reference numeral 712), channel characteristics associated with one or more previously determined subsets of a first set of downlink reference signal resources associated with one or more measurement occasions before the measurement occasion, channel characteristics of the first set of downlink reference signal resources measured at the measurement occasion and / or measurement results associated with the first set of downlink reference signal resources and before the measurement occasion, or predicted channel characteristics of a second set of downlink reference signal resources associated with one or more measurement occasions before the measurement occasion (described above in conjunction with reference numeral 716), and other information.

[0119] In some aspects, identifying a subset of the first set of downlink reference signal resources may include identifying an identifier or similar information associated with each downlink reference signal resource (e.g., with each beam) and / or with a set of downlink reference signal resources (e.g., a set C beam), and / or related information (such as a channel characteristic associated with one or more identifiers). For example, in some aspects, identifying a subset of the first set of downlink reference signal resources may include identifying a resource identifier associated with the subset of the first set of downlink reference signal resources. In some other aspects, identifying a subset of the first set of downlink reference signal resources may include identifying a channel characteristic associated with the subset of the first set of downlink reference signal resources. In some other aspects, and as described above in conjunction with reference numerals 720-724, identifying a subset of the first set of downlink reference signal resources may include identifying an identifier associated with a CMR set associated with the subset of the first set of downlink reference signal resources in a plurality of CMR sets configured for UE 120, which will be described in more detail below in conjunction with reference numeral 840.

[0120] As indicated by reference numeral 825, in some aspects, UE 120 may measure channel characteristics associated with a subset of the first set of downlink reference signal resources (e.g., UE 120 may measure channel characteristics associated with set C beams). In addition, UE 120 may measure channel characteristics associated with the subset of the first set of downlink reference signal resources in measurement opportunity 830 (e.g., measurement opportunity 708), which may be a time domain measurement opportunity at which UE 120 will use an AI / ML model (e.g., AI / ML model 610) or similar techniques to determine predicted channel characteristics associated with a second set of downlink reference signal resources (e.g., set A beams). As described above in conjunction with reference numeral 820, in some aspects, the measured channel characteristics associated with the subset of the first set of downlink reference signal resources may be associated with (e.g., used as input to) an input to a model (e.g., AI / ML model 610) that is used to determine predicted channel characteristics associated with a second set of downlink reference signal resources. In some aspects, the channel characteristic associated with the subset of the first set of downlink reference signal resources may be associated with at least one of an RSRP measurement, a SINR, a PMI, a CQI, a CRI, or a RI.

[0121] As indicated by reference numeral 835, UE 120 may determine predicted channel characteristics associated with a second set of downlink reference signal resources (e.g., set A-beams) associated with measurement opportunity 830 (e.g., measurement opportunity 708) based at least in part on a subset of the first set of downlink reference signal resources (e.g., set C-beams). In some aspects, one or more channel characteristics associated with the first set of downlink reference signal resources, in addition to the subset of the first set of downlink reference signal resources (e.g., set C-beams), may be used as input to a model (e.g., AI / ML model 610) for determining predicted channel characteristics associated with the second set of downlink reference signal resources (e.g., set A-beams).

[0122] As indicated by reference numeral 840, the UE 120 may report to the network node 110 an indication of predicted channel characteristics associated with a second set of downlink reference signal resources (e.g., set A beams) associated with a measurement opportunity 830 (e.g., measurement opportunity 708). In some aspects, the UE 120 may report the indication of predicted channel characteristics associated with the second set of downlink reference signal resources associated with the measurement opportunity 830 via at least one of a CSI report, a MAC-CE communication, or the like. For example, in aspects where the UE 120 receives a CSI reporting setting (e.g., CSI reporting setting 720) from the network node 110, the predicted channel characteristics associated with the second set of downlink reference signal resources (e.g., set A beams) associated with the measurement opportunity 830 may be reported via a CSI report configured by or otherwise associated with the CSI reporting setting.

[0123] Additionally or alternatively, in the operations shown in conjunction with reference numeral 840, the UE 120 may report an indication of a subset of the second set of downlink reference signal resources to the network node 110. For example, the UE 120 may report only the downlink reference signal resources associated with the number (sometimes referred to as K) of the highest strength predicted channel characteristics. More specifically, the UE 120 may report the predicted top K resources based on the corresponding L1-RSRP and / or L1-SINR strength of the resources. In such aspects, the UE 120 may be configured with the number (e.g., K) of downlink reference signal resources to be reported. For example, via the configuration information described above in conjunction with reference numeral 815 and / or via similar signaling (e.g., RRC message, MAC-CE and / or DCI), the network node 110 may indicate (e.g., via CSI report) the number (e.g., K) of downlink reference signal resources to be reported in the operations shown in conjunction with reference numeral 840.

[0124] In addition, in some aspects, the UE 120 may report an indication of a subset of the second set of downlink reference signal resources (e.g., the first K resources) without reporting corresponding channel characteristics for each resource (e.g., without reporting corresponding L1-RSRP and / or L1-SINR measurements for the reported resources). In other words, the operations shown in conjunction with reference numeral 840 may include omitting an indication of predicted channel characteristics associated with a subset of the second set of downlink reference signal resources. By reporting only the predicted first K downlink reference signal resources and / or by omitting measurements associated with the reported predicted downlink reference signal resources, the UE 120 and / or the network node 110 may save power, computation, and network resources that would otherwise be required to report channel characteristics associated with all predicted downlink reference signal resources.

[0125] Based at least in part on the network node 110 signaling to the UE 120 an indication of a technique for determining the set C-beam (as indicated by reference numeral 810), the UE 120 and / or the network node 110 may save computational, power, network, and / or communication resources that may otherwise be consumed when communicating based at least in part on channel prediction determined at least in part based on the set C-beam autonomously selected by the UE 120 and / or determined according to a specific implementation of the UE 120. For example, based at least in part on the network node 110 signaling to the UE 120 an indication of a technique for determining the set C-beam, the UE 120 and the network node 110 may communicate at a reduced error rate, which may save computational, power, network, and / or communication resources that may otherwise be consumed to detect and / or correct communication errors.

[0126] As indicated above, Figure 8 are provided as examples. Other examples can be found in the Figure 8 The examples described are different.

[0127] Fig. 9 is a diagram illustrating an example process 900 performed, for example, by a UE in accordance with the present disclosure. Example process 900 is an example of operations in which a UE (eg, UE 120) performs channel characteristic prediction based at least in part on a subset of downlink reference signal resources.

[0128] like Fig. 9As shown, in some aspects, process 900 may include receiving, from a network node (e.g., network node 110), an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity (block 910). For example, a UE (e.g., using Fig.11 The communication manager 140 and / or receiving component 1102 depicted in the figure can receive from a network node an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity, as described above.

[0129] like Fig. 9 As further shown, in some aspects, process 900 may include identifying a subset of the first set of downlink reference signal resources based at least in part on a technique for identifying a subset of the first set of downlink reference signal resources (block 920). Fig.11 The communication manager 140 and / or identification component 1108 depicted in FIG. 1 may identify a subset of the first set of downlink reference signal resources based at least in part on a technique for identifying a subset of the first set of downlink reference signal resources, as described above.

[0130] Process 900 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.

[0131] In a first aspect, a technique for identifying a subset of a first set of downlink reference signal resources is associated with one of a formula or a machine learning model.

[0132] In a second aspect, either alone or in combination with the first aspect, process 900 includes measuring channel characteristics associated with a subset of a first set of downlink reference signal resources, wherein the channel characteristics associated with the subset of the first set of downlink reference signal resources are associated with inputs to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources.

[0133] In a third aspect, either alone or in combination with one or more of the first and second aspects, a channel characteristic associated with a subset of the first set of downlink reference signal resources is associated with at least one of: a reference signal received power measurement, a signal-to-noise-plus-interference ratio, a precoding matrix indicator, a channel quality indicator, a channel state information reference signal resource indicator, or a rank indicator.

[0134] In a fourth aspect, alone or in combination with one or more of the first to third aspects, a technique for identifying a subset of a first set of downlink reference signal resources is based at least in part on one or more previously determined subsets of the first set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

[0135] In a fifth aspect, alone or in combination with one or more of aspects one to four, the technique for identifying a subset of a first set of downlink reference signal resources is further based at least in part on channel characteristics associated with one or more previously determined subsets of the first set of downlink reference signal resources.

[0136] In a sixth aspect, alone or in combination with one or more of aspects one to five, a technique for identifying a subset of a first set of downlink reference signal resources is based at least in part on at least one of: a formula associated with channel characteristics of one or more previously determined subsets of the first set of downlink reference signal resources, or a machine learning model associated with channel characteristics of one or more previously determined subsets of the first set of downlink reference signal resources.

[0137] In a seventh aspect, alone or in combination with one or more of aspects one to six, a technique for identifying a subset of a first set of downlink reference signal resources is based at least in part on predicted channel characteristics of a second set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

[0138] In the eighth aspect, alone or in combination with one or more aspects of the first to seventh aspects, the technology for identifying a subset of the first set of downlink reference signal resources is further based at least in part on at least one of the following: a formula associated with the predicted channel characteristics of the second set of downlink reference signal resources associated with one or more measurement occasions before the measurement occasion, or a machine learning model associated with the predicted channel characteristics of the second set of downlink reference signal resources associated with one or more measurement occasions before the measurement occasion.

[0139] In a ninth aspect, either alone or in combination with one or more of aspects 1 to 8, process 900 comprises determining predicted channel characteristics associated with a second set of downlink reference signal resources based at least in part on a subset of the first set of downlink reference signal resources.

[0140] In a tenth aspect, either alone or in combination with one or more of aspects one to nine, process 900 comprises reporting to a network node via at least one of a CSI report or a MAC-CE communication an indication of predicted channel characteristics associated with a second set of downlink reference signal resources.

[0141] In the eleventh aspect, alone or in combination with one or more of the first to tenth aspects, predicted channel characteristics associated with a second set of downlink reference signal resources are reported via a CSI report, and an indication of a technique for identifying a subset of the first set of downlink reference signal resources is received via a CSI report setting associated with the CSI report.

[0142] In the twelfth aspect, alone or in combination with one or more aspects of the first to eleventh aspects, identifying a subset of the first set of downlink reference signal resources includes identifying at least one of the following: a resource identifier associated with the subset of the first set of downlink reference signal resources, or a channel characteristic associated with the subset of the first set of downlink reference signal resources.

[0143] In a thirteenth aspect, either alone or in combination with one or more of aspects one to twelfth, process 900 comprises receiving a configuration of a plurality of CMR sets from a network node, wherein a subset of a first set of downlink reference signal resources is associated with a CMR set of the plurality of CMR sets.

[0144] In a fourteenth aspect, either alone or in combination with one or more of aspects one to thirteen, process 900 comprises receiving, from a network node, a CSI report setting associated with a CSI report for reporting predicted channel characteristics associated with a second set of downlink reference signal resources, wherein the CSI report setting indicates a plurality of CMR sets.

[0145] In a fifteenth aspect, alone or in combination with one or more of the first to fourteenth aspects, identifying a subset of the first set of downlink reference signal resources comprises identifying an identifier associated with a CMR set.

[0146] In a sixteenth aspect, alone or in combination with one or more of the first to fifteenth aspects, process 900 includes reporting an indication of a subset of the second set of downlink reference signal resources to a network node.

[0147] In the seventeenth aspect, either alone or in combination with one or more of the first to sixteenth aspects, a subset of the second set of downlink reference signal resources comprises downlink reference signal resources associated with the number of highest strength predicted channel characteristics among the predicted channel characteristics associated with the second set of downlink reference signal resources.

[0148] In an eighteenth aspect, alone or in combination with one or more of the first to seventeenth aspects, process 900 includes receiving configuration information indicating a quantity from a network node.

[0149] In a nineteenth aspect, either alone or in combination with one or more of aspects one to eighteen, reporting an indication of a subset of a second set of downlink reference signal resources comprises omitting an indication of predicted channel characteristics associated with the subset of the second set of downlink reference signal resources.

[0150] although Fig. 9 Example blocks of process 900 are shown, but in some aspects, process 900 may include Fig. 9 Additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 900. Additionally or alternatively, two or more of the blocks in the process 900 may be performed in parallel.

[0151] Fig.10 is a diagram illustrating an example process 1000 performed, for example, by a network node in accordance with the present disclosure. Example process 1000 is an example of operations in which a network node (eg, network node 110) performs operations associated with channel characteristic prediction based at least in part on a subset of downlink reference signal resources.

[0152] like Fig.10 As shown, in some aspects, process 1000 may include selecting a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining a predicted channel characteristic associated with a second set of downlink reference signal resources associated with the measurement opportunity (block 1010). For example, a network node (e.g., using Fig.12 The communication manager 150 and / or selection component 1208 depicted in the figure may select a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity, as described above.

[0153] like Fig.10 As further shown, in some aspects, process 1000 may include sending an indication to a UE (e.g., UE 120) of a technique for identifying a subset of the first set of downlink reference signal resources (block 1020). Fig.12 The communications manager 150 and / or transmitting component 1204 depicted in FIG. 1 may send an indication to the UE of a technique for identifying a subset of the first set of downlink reference signal resources, as described above.

[0154] Process 1000 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.

[0155] In a first aspect, a technique for identifying a subset of a first set of downlink reference signal resources is associated with one of a formula or a machine learning model.

[0156] In a second aspect, alone or in combination with the first aspect, a technique for identifying a subset of a first set of downlink reference signal resources is based at least in part on one or more previously determined subsets of the first set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

[0157] In a third aspect, alone or in combination with one or more of the first and second aspects, the technology for identifying a subset of the first set of downlink reference signal resources is further based at least in part on channel characteristics associated with one or more previously determined subsets of the first set of downlink reference signal resources.

[0158] In a fourth aspect, either alone or in combination with one or more of aspects 1 to 3, channel characteristics associated with a subset of one or more previously determined subsets of a first set of downlink reference signal resources are associated with at least one of: a reference signal received power measurement, a signal to noise plus interference ratio, a precoding matrix indicator, a channel quality indicator, a channel state information reference signal resource indicator, or a rank indicator.

[0159] In a fifth aspect, alone or in combination with one or more of aspects one to four, the technology for identifying a subset of a first set of downlink reference signal resources is further based at least in part on at least one of: a formula associated with channel characteristics of one or more previously determined subsets associated with the first set of downlink reference signal resources, or a machine learning model associated with channel characteristics of one or more previously determined subsets associated with the first set of downlink reference signal resources.

[0160] In a sixth aspect, either alone or in combination with one or more of aspects one to five, a technique for identifying a subset of a first set of downlink reference signal resources is based at least in part on predicted channel characteristics of a second set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

[0161] In the seventh aspect, alone or in combination with one or more aspects of the first to sixth aspects, the technology for identifying a subset of the first set of downlink reference signal resources is further based at least in part on at least one of the following: a formula associated with the predicted channel characteristics of the second set of downlink reference signal resources associated with one or more measurement occasions before the measurement occasion, or a machine learning model associated with the predicted channel characteristics of the second set of downlink reference signal resources associated with one or more measurement occasions before the measurement occasion.

[0162] In an eighth aspect, either alone or in combination with one or more of aspects 1 to 7, process 1000 includes receiving an indication of predicted channel characteristics associated with a second set of downlink reference signal resources from a UE via at least one of a CSI report or a MAC-CE communication.

[0163] In a ninth aspect, alone or in combination with one or more of aspects one to eight, predicted channel characteristics associated with a second set of downlink reference signal resources are received via a CSI report, and an indication of a technique for identifying a subset of the first set of downlink reference signal resources associated with a measurement opportunity is sent via a CSI report setting associated with the CSI report.

[0164] In the tenth aspect, alone or in combination with one or more aspects from the first to the ninth aspects, the technology for identifying a subset of a first set of downlink reference signal resources identifies at least one of the following: a resource identifier associated with the subset of the first set of downlink reference signal resources, or a channel characteristic associated with the subset of the first set of downlink reference signal resources.

[0165] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, process 1000 includes sending a configuration of multiple CMR sets to a UE, wherein a subset of a first set of downlink reference signal resources is associated with a CMR set in the multiple CMR sets.

[0166] In a twelfth aspect, either alone or in combination with one or more of aspects 1 to eleven, process 1000 includes sending to a UE a CSI report setting associated with a CSI report for reporting predicted channel characteristics associated with a second set of downlink reference signal resources, wherein the CSI report setting indicates multiple CMR sets.

[0167] In a thirteenth aspect, alone or in combination with one or more of the first to twelfth aspects, a technique for identifying a subset of the first set of downlink reference signal resources identifies an identifier associated with a CMR set.

[0168] In a fourteenth aspect, alone or in combination with one or more of the first to thirteenth aspects, process 1000 includes receiving an indication of a subset of the second set of downlink reference signal resources from a UE.

[0169] In a fifteenth aspect, either alone or in combination with one or more of aspects one to fourteen, a subset of the second set of downlink reference signal resources comprises downlink reference signal resources associated with a number of highest strength predicted channel characteristics among the predicted channel characteristics associated with the second set of downlink reference signal resources.

[0170] In a sixteenth aspect, alone or in combination with one or more of the first to fifteenth aspects, process 1000 includes receiving configuration information indicating a quantity from a network node.

[0171] In the seventeenth aspect, either alone or in combination with one or more of the first to sixteenth aspects, the indication of a subset of a second set of downlink reference signal resources omits an indication of predicted channel characteristics associated with the subset of the second set of downlink reference signal resources.

[0172] although Fig.10 Example blocks of process 1000 are shown, but in some aspects, process 1000 may include Fig.10 Additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1000. Additionally or alternatively, two or more of the blocks in the process 1000 may be performed in parallel.

[0173] Fig.111 is a diagram of an example apparatus 1100 for wireless communication according to the present disclosure. Apparatus 1100 may be UE 120, or UE 120 may include apparatus 1100. In some aspects, apparatus 1100 includes a receiving component 1102 and a sending component 1104 that may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 1100 may communicate with another apparatus 1106 (such as UE 120, network node 110, or another wireless communication device) using receiving component 1102 and sending component 1104. As further shown, apparatus 1100 may include a communication manager 140. Communication manager 140 may include one or more of an identification component 1108, a measurement component 1110, or a determination component 1112, among others.

[0174] In some aspects, the apparatus 1100 may be configured to perform FIG. 7A to FIG. 8 Additionally or alternatively, the apparatus 1100 may be configured to perform one or more processes described herein, such as Fig. 9 The process 900. In some aspects, Fig.11 The device 1100 and / or one or more components shown may include a combination of Figure 2 One or more components of the described UE 120. Additionally or alternatively, Fig.11 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 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.

[0175] The receiving component 1102 may receive communications from the device 1106, such as reference signals, control information, data communications, or combinations thereof. The receiving component 1102 may provide the received communications to one or more other components of the device 1100. In some aspects, the receiving component 1102 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 1100. In some aspects, the receiving component 1102 may include combining Figure 2 The UE 120 is described as having one or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof.

[0176] The transmitting component 1104 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to the device 1106. In some aspects, one or more other components of the device 1100 may generate communications and may provide the generated communications to the transmitting component 1104 for transmission to the device 1106. In some aspects, the transmitting component 1104 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 1106. In some aspects, the transmitting component 1104 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memory, or combinations thereof of the depicted UE 120. In some aspects, the transmit component 1104 can be co-located with the receive component 1102 in a transceiver.

[0177] Receiving component 1102 may receive, from a network node, an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity. Identifying component 1108 may identify the subset of the first set of downlink reference signal resources based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources.

[0178] The measurement component 1110 can measure channel characteristics associated with a subset of a first set of downlink reference signal resources, wherein the channel characteristics associated with the subset of the first set of downlink reference signal resources are associated with inputs to a model used to determine predicted channel characteristics associated with a second set of downlink reference signal resources.

[0179] Determining component 1112 can determine, based at least in part on a subset of the first set of downlink reference signal resources, predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity.

[0180] Transmitting component 1104 can report an indication of predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity to the network node via at least one of a CSI report or a MAC-CE communication.

[0181] Receiving component 1102 can receive, from a network node, a configuration of a plurality of CMR sets, wherein a subset of the first set of downlink reference signal resources is associated with a CMR set of the plurality of CMR sets.

[0182] Receiving component 1102 may receive, from a network node, a CSI reporting setting associated with a CSI report for reporting predicted channel characteristics associated with a second set of downlink reference signal resources associated with a measurement opportunity, wherein the CSI reporting setting indicates a plurality of CMR sets.

[0183] Transmitting component 1104 can report an indication of the subset of the second set of downlink reference signal resources to the network node.

[0184] Receiving component 1102 can receive configuration information from a network node indicating a number of highest strength predicted channel characteristics among the predicted channel characteristics associated with the second set of downlink reference signal resources.

[0185] Identifying component 1108 can omit indication of predicted channel characteristics associated with a subset of the second set of downlink reference signal resources.

[0186] Fig.11 The number and arrangement of components shown are provided as examples. In practice, there may be Fig.11 Additional components, fewer components, different components, or components arranged in a different manner than those shown. Fig.11 Two or more components shown may be implemented in a single component, or Fig.11 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Fig.11 The illustrated set of component(s) may be described as being executable by Fig.11 Another collection of components shown performs one or more functions.

[0187] Fig.12 1 is a diagram of an example apparatus 1200 for wireless communication according to the present disclosure. Apparatus 1200 may be network node 110, or network node 110 may include apparatus 1200. In some aspects, apparatus 1200 includes a receiving component 1202 and a sending component 1204 that may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 1200 may communicate with another apparatus 1206 (such as UE 120, network node 110, or another wireless communication device) using receiving component 1202 and sending component 1204. As further shown, apparatus 1200 may include communication manager 150. Communication manager 150 may include selection component 1208, etc.

[0188] In some aspects, the apparatus 1200 may be configured to perform FIG. 7A to FIG. 8 Additionally or alternatively, the apparatus 1200 may be configured to perform one or more of the processes described herein, such as Fig.10 The process 1000. In some aspects, Fig.12 The device 1200 and / or one or more components shown may include a combination of Figure 2 One or more components of the described network node 110. Additionally or alternatively, Fig.12 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 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.

[0189] The receiving component 1202 may receive communications, such as reference signals, control information, data communications, or combinations thereof, from the device 1206. The receiving component 1202 may provide the received communications to one or more other components of the device 1200. In some aspects, the receiving component 1202 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 1200. In some aspects, the receiving component 1202 may include combining Figure 2 The network node 110 may be described as having one or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof.

[0190] The transmitting component 1204 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to the device 1206. In some aspects, one or more other components of the device 1200 may generate communications and may provide the generated communications to the transmitting component 1204 for transmission to the device 1206. In some aspects, the transmitting component 1204 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 1206. In some aspects, the transmitting component 1204 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the depicted network node 110. In some aspects, the transmit component 1204 can be co-located with the receive component 1202 in a transceiver.

[0191] The selecting component 1208 may select a technique for identifying a subset of the first set of downlink reference signal resources associated with the measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity. The transmitting component 1204 may transmit to the UE an indication of the technique for identifying the subset of the first set of downlink reference signal resources associated with the measurement opportunity.

[0192] Receiving component 1202 can receive, from the UE via at least one of a CSI report or a MAC-CE communication, an indication of predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity.

[0193] Transmitting component 1204 can transmit a configuration of a plurality of CMR sets to the UE, wherein a subset of the first set of downlink reference signal resources is associated with a CMR set of the plurality of CMR sets.

[0194] Transmitting component 1204 may transmit, to the UE, a CSI reporting configuration associated with a CSI report for reporting predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity, wherein the CSI reporting configuration indicates a plurality of CMR sets.

[0195] Receiving component 1202 can receive, from a UE, an indication of a subset of a second set of downlink reference signal resources.

[0196] Transmitting component 1204 can transmit configuration information to the UE indicating a number of highest strength predicted channel characteristics among the predicted channel characteristics associated with the second set of downlink reference signal resources.

[0197] Fig.12 The number and arrangement of components shown are provided as examples. In practice, there may be Fig.12 Additional components, fewer components, different components, or components arranged in a different manner than those shown. Fig.12 Two or more components shown may be implemented in a single component, or Fig.12 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Fig.12 The illustrated set of component(s) may be described as being executable by Fig.12 Another collection of components shown performs one or more functions.

[0198] The following provides an overview of some aspects of the disclosure:

[0199] Aspect 1: A method of wireless communication performed by a UE, the method comprising: receiving an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity from a network node, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model, the model being used to determine predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; and identifying the subset of the first set of downlink reference signal resources based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources.

[0200] Aspect 2: The method according to aspect 1, wherein the technique for identifying the subset of the first set of downlink reference signal resources is associated with one of a formula or a machine learning model.

[0201] Aspect 3: According to the method described in any one of Aspects 1 to 2, the method also includes: measuring channel characteristics associated with the subset of the first set of downlink reference signal resources, wherein the channel characteristics associated with the subset of the first set of downlink reference signal resources are associated with the input to the model, and the model is used to determine the predicted channel characteristics associated with the second set of downlink reference signal resources.

[0202] Aspect 4: A method according to Aspect 3, wherein the channel characteristics associated with the subset of the first set of downlink reference signal resources are associated with at least one of the following: a reference signal received power measurement, a signal to noise plus interference ratio, a precoding matrix indicator, a channel quality indicator, a channel state information reference signal resource indicator or a rank indicator.

[0203] Aspect 5: A method according to any one of Aspects 1 to 4, wherein the technology for identifying the subset of the first set of downlink reference signal resources is at least partially based on one or more previously determined subsets of the first set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

[0204] Aspect 6: A method according to Aspect 5, wherein the technology for identifying the subset of the first set of downlink reference signal resources is further based at least in part on channel characteristics associated with the one or more previously determined subsets of the first set of downlink reference signal resources.

[0205] Aspect 7: A method according to Aspect 6, wherein the technology for identifying the subset of the first set of downlink reference signal resources is at least partially based on at least one of the following: a formula associated with the channel characteristics of the one or more previously determined subsets of the first set of downlink reference signal resources, or a machine learning model associated with the channel characteristics of the one or more previously determined subsets of the first set of downlink reference signal resources.

[0206] Aspect 8: A method according to any one of Aspects 1 to 7, wherein the technology for identifying the subset of the first set of downlink reference signal resources is at least partially based on predicted channel characteristics of the second set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

[0207] Aspect 9: A method according to Aspect 8, wherein the technology for identifying the subset of the first set of downlink reference signal resources is further based at least in part on at least one of the following: a formula associated with the predicted channel characteristics of the second set of downlink reference signal resources associated with the one or more measurement occasions before the measurement occasion, or a machine learning model associated with the predicted channel characteristics of the second set of downlink reference signal resources associated with the one or more measurement occasions before the measurement occasion.

[0208] Aspect 10: According to any one of Aspects 1 to 9, the method also includes: determining the predicted channel characteristics associated with the second set of downlink reference signal resources at least in part based on the subset of the first set of downlink reference signal resources.

[0209] Aspect 11: According to any one of Aspects 1 to 10, the method also includes: reporting an indication of the predicted channel characteristics associated with the second set of downlink reference signal resources to the network node via at least one of CSI reporting or MAC-CE communication.

[0210] Aspect 12: A method according to Aspect 9, wherein the predicted channel characteristics associated with the second set of downlink reference signal resources are reported via the CSI report, and wherein the indication of the technology for identifying the subset of the first set of downlink reference signal resources is received via a CSI report setting associated with the CSI report.

[0211] Aspect 13: A method according to any one of Aspects 1 to 12, wherein identifying the subset of the first set of downlink reference signal resources includes identifying at least one of the following: a resource identifier associated with the subset of the first set of downlink reference signal resources, or a channel characteristic associated with the subset of the first set of downlink reference signal resources.

[0212] Aspect 14: According to any one of Aspects 1 to 13, the method further includes: receiving a configuration of multiple CMR sets from the network node, wherein the subset of the first set of downlink reference signal resources is associated with a CMR set in the multiple CMR sets.

[0213] Aspect 15: According to the method according to Aspect 14, the method also includes: receiving from the network node a CSI report setting associated with a CSI report for reporting the predicted channel characteristics associated with the second set of downlink reference signal resources, wherein the CSI report setting indicates the multiple CMR sets.

[0214] Aspect 16: The method according to aspect 15, wherein identifying the subset of the first set of downlink reference signal resources includes identifying an identifier associated with the CMR set.

[0215] Aspect 17: The method according to any one of aspects 1 to 16, the method further comprising: reporting an indication of a subset of the second set of downlink reference signal resources to the network node.

[0216] Aspect 18: A method according to Aspect 17, wherein the subset of the second set of downlink reference signal resources includes downlink reference signal resources associated with the number of highest strength predicted channel characteristics among the predicted channel characteristics associated with the second set of downlink reference signal resources.

[0217] Aspect 19: The method according to aspect 18, the method further comprising: receiving configuration information indicating the number from the network node.

[0218] Aspect 20: A method according to any one of Aspects 17 to 19, wherein reporting the indication of the subset of the second set of downlink reference signal resources includes omitting the indication of the predicted channel characteristics associated with the subset of the second set of downlink reference signal resources.

[0219] Aspect 21: A method of wireless communication performed by a network node, the method comprising: selecting a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model, the model being used to determine predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; and sending an indication of the technique for identifying the subset of the first set of downlink reference signal resources to a UE.

[0220] Aspect 22: The method according to Aspect 21, wherein the technique for identifying the subset of the first set of downlink reference signal resources is associated with one of a formula or a machine learning model.

[0221] Aspect 23: A method according to any one of Aspects 21 to 22, wherein the technology for identifying the subset of the first set of downlink reference signal resources is at least partially based on one or more previously determined subsets of the first set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

[0222] Aspect 24: A method according to Aspect 23, wherein the technology for identifying the subset of the first set of downlink reference signal resources is further based at least in part on channel characteristics associated with the one or more previously determined subsets of the first set of downlink reference signal resources.

[0223] Aspect 25: A method according to Aspect 24, wherein the channel characteristics associated with the one or more previously determined subsets of the first set of downlink reference signal resources are associated with at least one of the following: a reference signal received power measurement, a signal-to-noise plus interference ratio, a precoding matrix indicator, a channel quality indicator, a channel state information reference signal resource indicator, or a rank indicator.

[0224] Aspect 26: A method according to any one of Aspects 23 to 25, wherein the technology for identifying the subset of the first set of downlink reference signal resources is further based at least in part on at least one of the following: a formula associated with channel characteristics of the one or more previously determined subsets of the first set of downlink reference signal resources, or a machine learning model associated with the channel characteristics of one or more previously determined subsets of the first set of downlink reference signal resources.

[0225] Aspect 27: A method according to any one of Aspects 21 to 26, wherein the technology for identifying the subset of the first set of downlink reference signal resources is at least partially based on predicted channel characteristics of the second set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

[0226] Aspect 28: A method according to Aspect 27, wherein the technology for identifying the subset of the first set of downlink reference signal resources is further based at least in part on at least one of the following: a formula associated with the predicted channel characteristics of the second set of downlink reference signal resources associated with the one or more measurement occasions before the measurement occasion, or a machine learning model associated with the predicted channel characteristics of the second set of downlink reference signal resources associated with the one or more measurement occasions before the measurement occasion.

[0227] Aspect 29: According to any one of Aspects 21 to 28, the method also includes: receiving an indication of the predicted channel characteristics associated with the second set of downlink reference signal resources from the UE via at least one of a CSI report or a MAC-CE communication.

[0228] Aspect 30: A method according to Aspect 29, wherein the predicted channel characteristics associated with the second set of downlink reference signal resources associated with the measurement opportunity are received via the CSI report, and wherein the indication of the technology for identifying the subset of the first set of downlink reference signal resources associated with the measurement opportunity is sent via a CSI report setting associated with the CSI report.

[0229] Aspect 31: A method according to any one of Aspects 21 to 30, wherein the technology used to identify the subset of the first set of downlink reference signal resources identifies at least one of the following: a resource identifier associated with the subset of the first set of downlink reference signal resources, or a channel characteristic associated with the subset of the first set of downlink reference signal resources.

[0230] Aspect 32: According to the method described in any one of Aspects 21 to 31, the method further includes: sending a configuration of multiple CMR sets to the UE, wherein the subset of the first set of downlink reference signal resources is associated with a CMR set in the multiple CMR sets.

[0231] Aspect 33: According to the method according to Aspect 32, the method also includes: sending a CSI report setting associated with a CSI report for reporting the predicted channel characteristics associated with the second set of downlink reference signal resources to the UE, wherein the CSI report setting indicates the multiple CMR sets.

[0232] Aspect 34: The method according to aspect 33, wherein the technique for identifying the subset of the first set of downlink reference signal resources identifies an identifier associated with the CMR set.

[0233] Aspect 35: The method according to any one of aspects 21 to 34, the method further comprising: receiving an indication of a subset of the second set of downlink reference signal resources from the UE.

[0234] Aspect 36: A method according to Aspect 35, wherein the subset of the second set of downlink reference signal resources includes downlink reference signal resources associated with the number of highest strength predicted channel characteristics among the predicted channel characteristics associated with the second set of downlink reference signal resources.

[0235] Aspect 37: According to the method according to Aspect 36, the method also includes: sending configuration information indicating the number to the UE.

[0236] Aspect 38: A method according to any one of Aspects 35 to 37, wherein the indication of the subset of downlink reference signal resources omits an indication of the predicted channel characteristic associated with the subset of the second set of downlink reference signal resources.

[0237] Aspect 39: An apparatus for performing wireless communications at a device, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more of the methods described in Aspects 1 to 20.

[0238] Aspect 40: 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 20.

[0239] Aspect 41: 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 20.

[0240] Aspect 42: 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 20.

[0241] Aspect 43: 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 20.

[0242] Aspect 44: An apparatus for performing wireless communications at a device, the apparatus comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more of the methods described in Aspects 21 to 38.

[0243] Aspect 45: 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 21 to 38.

[0244] Aspect 46: An apparatus for wireless communication, the apparatus comprising at least one component for performing the method according to one or more of aspects 21 to 38.

[0245] Aspect 47: 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 21 to 38.

[0246] Aspect 48: 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 21 to 38.

[0247] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the aspects to the precise forms disclosed. Modifications and variations may be made in light of the above disclosure or may be acquired from practice of the various aspects.

[0248] 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 as meaning instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, processes and / or functions, etc. As used herein, "processors" are 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 the various aspects. Therefore, no reference is made to specific software codes to describe the operation and behavior of the systems and / or methods herein, because those skilled in the art will understand that software and hardware can be designed to implement the systems and / or methods based at least in part on the descriptions herein.

[0249] As used herein, "satisfying a threshold" may refer to a value being greater than a threshold, greater than or equal to a threshold, less than a threshold, less than or equal to a threshold, equal to a threshold, not equal to a threshold, etc., depending on the context.

[0250] Although specific combinations of features are set forth in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. Many of these features can be combined in a manner that is not specifically described in the claims and / or is not disclosed in the specification. The disclosure of various aspects includes each dependent claim combined with each other claim in the claim set. As used herein, the phrase "at least one of the list of items" refers to any combination of these items (which includes a single member). As an example, "at least one of a, b or c" is intended to cover a, b, c, a+b, a+c, b+c and a+b+c, and any combination with multiple identical elements (e.g., a+a, a+a+a, a+a+b, a+a+c, a+b+b, a+c+c, b+b, b+b+b, b+b+c, c+c and c+c+c, or any other ordering of a, b and c).

[0251] Any element, action or instruction used herein should not be interpreted as critical or necessary unless clearly stated. In addition, as used herein, the article "one" is intended to include one or more items and can be used interchangeably with "one or more". In addition, as used herein, the article "said" is intended to include one or more items connected to the article "said", and can be used interchangeably with "one or more". In addition, as used herein, the terms "set" and "group" are intended to include one or more items and can be used interchangeably with "one or more". If you only want to refer to one item, the phrase "only one" or similar terms will be used. Moreover, as used herein, the term "having" etc. is intended to be an open term, which does not limit the elements they modify (for example, "an element having" A may also have B). In addition, the phrase "based on" is intended to mean "based at least in part 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: one or more memories; and one or more processors coupled to the one or more memories and configured to: receiving, from a network node, an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; as well as The subset of the first set of downlink reference signal resources is identified based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources.

2. The UE of claim 1, wherein the one or more processors are further configured to report an indication of the subset of the second set of downlink reference signal resources to the network node.

3. The UE of claim 2, wherein the subset of the second set of downlink reference signal resources comprises downlink reference signal resources associated with a number of highest strength predicted channel characteristics among the predicted channel characteristics associated with the second set of downlink reference signal resources. 4 . The UE of claim 3 , wherein the one or more processors are further configured to: receive configuration information indicating the number from the network node.

5. The UE according to claim 2, wherein: To report the indication of the subset of the second set of downlink reference signal resources, the one or more processors are further configured to omit an indication of the predicted channel characteristic associated with the subset of the second set of downlink reference signal resources.

6. The UE of claim 1, wherein the technique for identifying the subset of the first set of downlink reference signal resources is associated with one of a formula or a machine learning model.

7. A UE according to claim 1, wherein the one or more processors are further configured to: measure channel characteristics associated with the subset of the first set of downlink reference signal resources, wherein the channel characteristics associated with the subset of the first set of downlink reference signal resources are associated with the input to the model, and the model is used to determine the predicted channel characteristics associated with the second set of downlink reference signal resources.

8. The UE of claim 7, wherein the channel characteristic associated with the subset of the first set of downlink reference signal resources is associated with at least one of: Reference signal received power measurement, Signal to noise and interference ratio, Precoding matrix indicator, Channel Quality Indicator, A channel state information reference signal resource indicator, or Rank indicator.

9. The UE of claim 1 , wherein the technique for identifying the subset of the first set of downlink reference signal resources is based at least in part on one or more previously determined subsets of the first set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

10. The UE of claim 9, wherein the technique for identifying the subset of the first set of downlink reference signal resources is further based at least in part on channel characteristics associated with the one or more previously determined subsets of the first set of downlink reference signal resources.

11. The UE of claim 10, wherein the technique for identifying the subset of the first set of downlink reference signal resources is based at least in part on at least one of: a formula associated with the channel characteristic of the one or more previously determined subsets of the first set of downlink reference signal resources, or A machine learning model associated with the channel characteristics of the one or more previously determined subsets of the first set associated with downlink reference signal resources.

12. The UE of claim 1, wherein the technique for identifying the subset of the first set of downlink reference signal resources is based at least in part on predicted channel characteristics of the second set of downlink reference signal resources associated with one or more measurement occasions prior to the measurement occasion.

13. The UE of claim 12, wherein the technique for identifying the subset of the first set of downlink reference signal resources is further based at least in part on at least one of: a formula associated with the predicted channel characteristic of the second set of downlink reference signal resources associated with the one or more measurement occasions prior to the measurement occasion, or A machine learning model associated with the predicted channel characteristics of the second set of downlink reference signal resources associated with the one or more measurement occasions prior to the measurement occasion.

14. The UE of claim 1, wherein the one or more processors are further configured to determine the predicted channel characteristic associated with the second set of downlink reference signal resources based at least in part on the subset of the first set of downlink reference signal resources.

15. The UE of claim 1, wherein the one or more processors are further configured to report an indication of the predicted channel characteristics associated with the second set of downlink reference signal resources to the network node via at least one of a channel state information (CSI) report or a medium access control (MAC) control element (MAC-CE) communication.

16. The UE according to claim 15, wherein: To report the indication of the predicted channel characteristic associated with the second set of downlink reference signal resources, the one or more processors are further configured to report the indication of the predicted channel characteristic associated with the second set of downlink reference signal resources via the CSI report, and Wherein, in order to receive the indication of the technology for identifying the subset of the first set of downlink reference signal resources, the one or more processors are further configured to receive the indication of the technology for identifying the subset of the first set of downlink reference signal resources via a CSI report setting associated with the CSI report.

17. The UE of claim 1, wherein to identify the subset of the first set of downlink reference signal resources, the one or more processors are configured to identify at least one of: a resource identifier associated with said subset of said first set of downlink reference signal resources, or Channel characteristics associated with the subset of the first set of downlink reference signal resources.

18. The UE of claim 1, wherein the one or more processors are further configured to: receive a configuration of multiple channel measurement resource (CMR) sets from the network node, wherein the subset of the first set of downlink reference signal resources is associated with a CMR set in the multiple CMR sets.

19. The UE of claim 18, wherein the one or more processors are further configured to: receive from the network node a channel state information (CSI) report setting associated with a CSI report for reporting the predicted channel characteristics associated with the second set of downlink reference signal resources, wherein the CSI report setting indicates the multiple CMR sets.

20. The UE of claim 19, wherein to identify the subset of the first set of downlink reference signal resources, the one or more processors are configured to identify an identifier associated with the CMR set.

21. A network node for wireless communication, the network node comprising: one or more memories; and one or more processors coupled to the one or more memories and configured to: selecting a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; as well as An indication of the technique for identifying the subset of the first set of downlink reference signal resources is sent to a user equipment (UE).

22. The network node of claim 21, wherein the one or more processors are further configured to receive an indication of a subset of the second set of downlink reference signal resources from the UE.

23. The network node of claim 22, wherein the subset of the second set of downlink reference signal resources comprises downlink reference signal resources associated with a number of highest strength predicted channel characteristics among the predicted channel characteristics associated with the second set of downlink reference signal resources.

24. The network node of claim 23, wherein the one or more processors are further configured to send configuration information indicating the number to the UE.

25. The network node of claim 22, wherein the indication of the subset of downlink reference signal resources omits an indication of the predicted channel characteristic associated with the subset of the second set of downlink reference signal resources.

26. The network node of claim 21, wherein the technique for identifying the subset of the first set of downlink reference signal resources is associated with one of a formula or a machine learning model.

27. A method of wireless communication performed by a user equipment (UE), the method comprising: receiving, from a network node, an indication of a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; as well as The subset of the first set of downlink reference signal resources is identified based at least in part on the technique for identifying the subset of the first set of downlink reference signal resources.

28. The method according to claim 27, further comprising: reporting an indication of a subset of the second set of downlink reference signal resources to the network node, wherein said subset of said second set of downlink reference signal resources comprises downlink reference signal resources associated with a number of highest strength predicted channel characteristics among said predicted channel characteristics associated with said second set of downlink reference signal resources, and Wherein reporting the indication of the subset of downlink reference signal resources comprises omitting an indication of the predicted channel characteristic associated with the subset of the second set of downlink reference signal resources.

29. A method of wireless communication performed by a network node, the method comprising: selecting a technique for identifying a subset of a first set of downlink reference signal resources associated with a measurement opportunity, wherein the subset of the first set of downlink reference signal resources is associated with an input to a model for determining predicted channel characteristics associated with a second set of downlink reference signal resources associated with the measurement opportunity; as well as An indication of the technique for identifying the subset of the first set of downlink reference signal resources is sent to a user equipment (UE).

30. The method according to claim 29, further comprising: receiving, from the UE, an indication of a subset of the second set of downlink reference signal resources, wherein said subset of said second set of downlink reference signal resources comprises downlink reference signal resources associated with a number of highest strength predicted channel characteristics among said predicted channel characteristics associated with said second set of downlink reference signal resources, and Wherein reporting the indication of the subset of downlink reference signal resources comprises omitting an indication of the predicted channel characteristic associated with the subset of the second set of downlink reference signal resources.

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