Predictive resource management using user equipment information in machine learning models

Abstract

Methods, systems, and devices are described for wireless communication at a user equipment (UE). The UE may enhance Channel State Information (CSI)-Reference Signal (RS) reporting to allow receive beam information and associated transmit beam channel characteristics to be sent back to a network entity for training a machine learning model. In some examples, a UE may perform implicit reporting of receive beams to associate with different receive beam options by using additional channel measurement resources or sounding reference signal (SRS) resources, such that the UE may avoid public antenna or beam enforcement or avoid causing a network entity to train a machine learning model too diverse. Additionally or alternatively, the UE may explicitly report the number of received beams. In some examples, the network entity may transmit a machine learning model to the UE such that the UE may also perform receive beam prediction.

Description

Technical Field

[0001] The following relates to wireless communications at a user equipment (UE), including predictive resource management using UE information in a machine learning model. Background Art

[0002] Wireless communication systems are widely deployed to provide various types of communication content such as voice, video, packet data, message transmission and reception, broadcasting, etc. These systems can support communication with multiple users by sharing available system resources (e.g., time, frequency and power). Examples of such multiple access systems include fourth generation (4G) systems such as long term evolution (LTE) systems, advanced LTE (LTE-A) systems or LTE-A Pro systems, and fifth generation (5G) systems that may be referred to as new radio (NR) systems. These systems may use techniques such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA) or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple access communication system may include one or more base stations, each of which supports wireless communication of communication devices, which may be referred to as user equipment (UE).

[0003] A wireless communication system may support a beam scanning process for selecting a beam for communication between a UE and a network entity (e.g., a base station or one of multiple components arranged in a decomposed architecture). The UE may select one or more beams to receive or transmit information by measuring and comparing channel characteristics using reference signal resources for each beam, such as a synchronization signal block (SSB) or a channel state information reference signal (CSI-RS). The network entity and the UE may employ a machine learning model when managing reference signal resources, but the UE is limited by the information that the UE can use when managing reference signal resources. Summary of the invention

[0004] The systems, methods and devices of the present disclosure each have several innovative aspects, no single one of which is solely responsible for the desirable attributes disclosed herein.

[0005] A method for wireless communication at a user equipment (UE) is described. The method may include: sending a set of multiple reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for the communication; based on sending the set of multiple reference signals, receiving signaling indicating a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources, the machine learning model being based on the receive beam at the UE; inputting inputs to the machine learning model for obtaining the channel characteristic prediction; and receiving signaling based on obtaining the channel characteristic prediction associated with the first set of channel measurement resources.

[0006] Some examples of the methods, devices (apparatus) and non-transitory computer-readable media described herein may also include operations, features, apparatuses or instructions for: determining a receive beam corresponding to the receive direction for the communication for the UE based on one or more channel measurement resource identifiers corresponding to the second group of channel measurement resources and one or more first channel characteristics associated with the second group of channel measurement resources, wherein the information includes the one or more first channel characteristics; and sending the one or more channel measurement resource identifiers in a signal that is the same as a subset of the set of multiple reference signals associated with the second group of channel measurement resources, wherein receiving the signaling can be based on sending the one or more channel measurement resource identifiers.

[0007] In some examples of the methods, devices (apparatus), and non-transitory computer-readable media described herein, the one or more channel measurement resource identifiers, one or more second channel characteristics associated with the first group of channel measurement resources, or both are input into the machine learning model based on sending the set of multiple reference signals; and a prediction of the channel characteristics associated with the first group of channel measurement resources is obtained based on inputting the one or more channel measurement resource identifiers, the one or more second channel characteristics associated with the first group of channel measurement resources, or both.

[0008] A method for wireless communication at a network entity is described. The method may include: receiving a set of multiple reference signals indicating information corresponding to one or more channel characteristics associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receiving direction for communication, the information corresponding to a receiving beam at a UE corresponding to the receiving direction for the communication; training a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources based on inputting the one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, and the receiving direction for the communication to obtain the channel characteristic prediction associated with the first set of channel measurement resources, the one or more channel characteristics being based on the receiving beam at the UE; and sending signaling indicative of the machine learning model based on training the machine learning model.

[0009] Some examples of the methods, devices (apparatus), and non-transitory computer-readable media described herein may also include operations, features, apparatus, or instructions for: receiving one or more channel measurement resource identifiers associated with the second group of channel measurement resources in a signal that is the same as a subset of the set of multiple reference signals associated with the second group of channel measurement resources; and determining the receive beam at the UE for measuring the first group of channel measurement resources based on the one or more channel measurement resource identifiers.

[0010] A method for wireless communication at a UE is described. The method may include: sending information associated with one or more channel measurement resources and a receive direction for communication; receiving signaling indicating a machine learning model for obtaining a channel characteristic prediction associated with the one or more channel measurement resources based on sending the information; inputting an input to the machine learning model for obtaining the channel characteristic prediction associated with the one or more channel measurement resources; and receiving signaling based on obtaining the channel characteristic prediction associated with the one or more channel measurement resources.

[0011] In some examples of the methods, devices, and non-transitory computer-readable media described herein, one or more channel characteristics, transmitted information, or both associated with the one or more channel measurement resources are input into the machine learning model; and based on the input, a prediction of the channel characteristics, transmitted information, or both associated with the one or more channel measurement resources is obtained.

[0012] A method for wireless communication at a network entity is described. The method may include: receiving, at a UE, information associated with one or more channel measurement resources and a receive direction for communication; training a machine learning model for obtaining a channel characteristic prediction based on inputting one or more channel characteristics and the information into the machine learning model to obtain the channel characteristic prediction; and sending signaling indicative of the machine learning model based on training the machine learning model.

[0013] An apparatus for wireless communication at a network entity is described. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executed by the processor to cause the apparatus to: receive information associated with one or more channel measurement resources and a receive direction for communication at a UE; train a machine learning model for obtaining a channel characteristic prediction based on inputting one or more channel characteristics and the information to the machine learning model to obtain the channel characteristic prediction; and send signaling indicative of the machine learning model based on training the machine learning model. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 An example of a wireless communication system supporting predictive resource management using user equipment (UE) information in a machine learning model according to one or more aspects of the present disclosure is illustrated.

[0015] Figure 2 An example of a wireless communication system supporting predictive resource management using UE information in a machine learning model according to one or more aspects of the present disclosure is illustrated.

[0016] Figure 3 An example of a process flow for supporting predictive resource management using UE information in a machine learning model according to one or more aspects of the present disclosure is illustrated.

[0017] Figure 4 An example of a process flow for supporting predictive resource management using UE information in a machine learning model according to one or more aspects of the present disclosure is illustrated.

[0018] Figures 5 to 7 A block diagram is shown that supports predictive resource management using UE information in a machine learning model according to one or more aspects of the present disclosure.

[0019] Figure 8 A diagram of a system including a device supporting predictive resource management using UE information in a machine learning model is shown in accordance with one or more aspects of the present disclosure.

[0020] Figures 9 to 11A block diagram is shown that supports predictive resource management using UE information in a machine learning model according to one or more aspects of the present disclosure.

[0021] Fig.12 A diagram of a system including a device supporting predictive resource management using UE information in a machine learning model is shown in accordance with one or more aspects of the present disclosure.

[0022] Figures 13 to 20 A flow chart illustrating a method for supporting predictive resource management using UE information in a machine learning model according to one or more aspects of the present disclosure is shown. DETAILED DESCRIPTION

[0023] A wireless communication system may support a beam scanning process for selecting one or more beams for communication between a user equipment (UE) and a network entity (e.g., a base station or one of multiple components arranged in a decomposed architecture). The UE may select one or more beams to receive or transmit information by measuring and comparing channel characteristics using reference signal resources for each beam (such as synchronization signal blocks (SSBs) or channel state information reference signals (CSI-RS), etc.). The network entity may employ a machine learning model to predict whether a top beam index (e.g., the strongest beam index in a set of beam indices) may change based on different inputs (e.g., measured channel characteristics). In some examples, in examples where the associated UE receive beam is included in the training information input to the machine learning model, the machine learning model is able to predict the downlink beam with greater accuracy. For example, a UE rotation or a maximum permitted exposure event may gradually change the UE receive beam, in which case the network entity transmit beam may also be gradually changed. However, other different wireless systems including UEs may not be configured to provide (e.g., send) UE receive beam information to a network entity. Additionally or alternatively, in some examples, fully disclosing aspects of a UE's receive beams to a network entity may expose confidential or secure information about the UE.

[0024] Various aspects generally involve a UE providing UE receive beam information to a network entity so that the network entity can use the provided beam information to train a machine learning model that is used to generate channel characteristic predictions at the UE. In some examples, receive beam information (e.g., identifiers of one or more reference signals for implicit receive beam information or beamforming predecoders, antenna panel identifiers, antenna panel orientations, angles of arrival, zenith angles of arrival, or any combination thereof for explicit receive beam information) is associated with a receive beam selected by the UE based on a beam scanning process. In some examples, the UE provides the receive beam information to the network entity in conjunction with a channel characteristic report associated with a set of channel measurement reports (e.g., CSI reports) (e.g., as part of the channel measurement report or separate from the channel measurement report but in addition to the channel measurement report). In some examples, the UE may implicitly report the UE receive beam information such that a UE specific implementation for selecting a receive beam is not disclosed to the network entity. The UE may implicitly report UE receive beam information based on sending multiple reference signals indicating beam information, and the beam information may include information for one or more groups of channel measurement resources and a receive direction (e.g., beam direction) for communication. In some other examples, such as publicly acceptable examples in which the UE is specifically implemented, the UE may explicitly report receive beam information. Thus, the network entity may use implicit UE receive beam information, or explicit UE receive beam information, or both to train a machine learning model.

[0025] In some examples, the network entity may then send signaling including an indication of the trained machine learning model to the UE, the signaling enabling the UE to download or otherwise access the trained machine learning model from the server. In some cases, the UE may input channel characteristics downsampled in the spatial domain or the time domain to the trained machine learning model. The trained machine learning model may output one or more predictions of channel characteristics (e.g., reference signal received power (RSRP), signal to interference plus noise ratio (SINR), rank indicator (RI), precoding matrix indicator (PMI), layer indicator (LI), channel quality indicator (CQI), or any combination thereof) of a set of channel measurement resources for uplink or downlink communication with the network entity. For example, the UE and the network entity may use one or more channel characteristic predictions to select a beam (such as a receive beam or a transmit beam) for communicating with the network entity to predict the RSRP for communication with the network entity, and the prediction may be used to select a beam that may, for example, maximize the RSRP.

[0026] Certain aspects of the subject matter described in the present disclosure may be implemented to achieve one or more of the following potential advantages. The techniques employed by the described UE and the described network entity provide improved reliability and accuracy of channel characteristic predictions based on training a machine learning model using UE receive beam information. Using a machine learning model that has been trained on UE receive beam information to obtain more reliable and accurate channel characteristic predictions can improve the beam scanning process by reducing or eliminating inefficient downlink beam selections (e.g., selecting beams that are not associated with maximum reference signal received power (RSRP)), which can also reduce resources used for beam management between the UE and the network entity. In some specific implementations, the operations performed by the described UE and the described network entity also support improvements in power consumption, reliability of uplink communications, spectral efficiency, higher data rates, and in some examples, low latency for uplink communications based on better channel characteristic predictions, which are in turn based on training a machine learning model using receive beam information.

[0027] Various aspects of the present disclosure are initially described in the context of a wireless communication system. Additional wireless communication systems and process flows are then provided to describe various aspects of the present disclosure. Various aspects of the present disclosure are further illustrated and described by and with reference to apparatus diagrams, system diagrams, and flow diagrams relating to predictive resource management using UE information in a machine learning model.

[0028] Figure 1 An example of a wireless communication system 100 that supports predictive resource management using UE information in a machine learning model according to one or more aspects of the present disclosure is illustrated. The wireless communication system 100 may include one or more network entities 105, one or more UEs 115, and a core network 130. In some examples, the wireless communication system 100 may be a long term evolution (LTE) network, an advanced LTE (LTE-A) network, an LTE-A Pro network, a new radio (NR) network, or a network operating according to other systems and radio technologies (including future systems and radio technologies not explicitly mentioned herein).

[0029] The network entities 105 may be dispersed throughout a geographic area to form the wireless communication system 100, and may include devices in different forms or with different capabilities. In different examples, the network entities 105 may be referred to as network elements, mobility elements, radio access network (RAN) nodes, or network equipment, etc. In some examples, the network entities 105 and the UE 115 may communicate wirelessly via one or more communication links 125 (e.g., radio frequency (RF) access links). For example, the network entity 105 may support a coverage area 110 (e.g., a geographic coverage area) over which the UE 115 and the network entity 105 may establish one or more communication links 125. The coverage area 110 may be an example of a geographic area over which the network entity 105 and the UE 115 may support signal communications according to one or more radio access technologies (RATs).

[0030] The UEs 115 may be dispersed throughout the coverage area 110 of the wireless communication system 100, and each UE 115 may be stationary or mobile, or stationary or mobile at different times. The UEs 115 may be devices in different forms or with different capabilities. Figure 1 Some example UEs 115 are illustrated in FIG. 1. The UEs 115 described herein can communicate with various types of devices (such as Figure 1 Communicate with other UEs 115 or network entities 105) as shown.

[0031] A node of the wireless communication system 100 (which may be referred to as a network node or a wireless node) may be a network entity 105 (e.g., any network entity described herein), a UE 115 (e.g., any UE described herein), a network controller, an apparatus, a device, a computing system, one or more components, or another suitable processing entity configured to perform any of the techniques described herein. For example, the node may be a UE 115. As another example, the node may be a network entity 105. As another example, a first node may be configured to communicate with a second node or a third node. In one aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a UE 115. In another aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a network entity 105. In yet other aspects of this example, the first node, the second node, and the third node may be different relative to these examples. Similarly, references to UE 115, network entity 105, apparatus, device, computing system, etc. may include disclosure of UE 115, network entity 105, apparatus, device, computing system, etc. as nodes. For example, disclosure that UE 115 is configured to receive information from network entity 105 also discloses that the first node is configured to receive information from the second node.

[0032] In some examples, the network entities 105 may communicate with the core network 130, or with each other, or both. For example, the network entities 105 may communicate with the core network 130 via one or more backhaul communication links 120 (e.g., according to S1, N2, N3, or other interface protocols). In some examples, the network entities 105 may communicate with each other directly (e.g., directly between the network entities 105) or indirectly (e.g., via the core network 130) via the backhaul communication links 120 (e.g., according to X2, Xn, or other interface protocols). In some examples, the network entities 105 may communicate with each other via midhaul communication links 162 (e.g., according to a midhaul interface protocol) or fronthaul communication links 168 (e.g., according to a fronthaul interface protocol) or any combination thereof. The backhaul communication links 120, midhaul communication links 162, or fronthaul communication links 168 may be or include one or more wired links (e.g., electrical links, optical fiber links), one or more wireless links (e.g., radio links, wireless optical links), and other examples or various combinations thereof. UE 115 may communicate with core network 130 via communication link 155 .

[0033] One or more of the network entities 105 described herein may include or may be referred to as a base station 140 (e.g., a transceiver base station, a radio base station, an NR base station, an access point, a radio transceiver, a Node B, an evolved Node B (eNB), a next generation Node B, or a Gigabit Node B (any of which may be referred to as a gNB), a 5G NB, a next generation eNB (ng-eNB), a Home Node B, a Home Evolved Node B, or other suitable terms). In some examples, the network entity 105 (e.g., a base station 140) may be implemented in an aggregated (e.g., monolithic, stand-alone) base station architecture that may be configured to utilize a protocol stack physically or logically integrated within a single network entity 105 (e.g., a single RAN node, such as a base station 140).

[0034] In some examples, the network entity 105 may be implemented in a disaggregated architecture (e.g., a disaggregated base station architecture, a disaggregated RAN architecture) that may be configured to utilize a protocol stack that is physically or logically distributed between two or more network entities 105, such as an integrated access backhaul (IAB) network, an open RAN (O-RAN) (e.g., a network configuration initiated by the O-RAN Alliance), or a virtualized RAN (vRAN) (e.g., a cloud RAN (C-RAN)). For example, the network entity 105 may include one or more of the following: a central unit (CU) 160, a distributed unit (DU) 165, a radio unit (RU) 170, a RAN intelligent controller (RIC) 175 (e.g., a near real-time RIC (near RT RIC), a non-real-time RIC (non-RT RIC)), a service management and orchestration (SMO) 180 system, or any combination thereof. RU 170 may also be referred to as a radio head, a smart radio head, a remote radio head (RRH), a remote radio unit (RRU), or a transmit receive point (TRP). One or more components of the network entity 105 in the decomposed RAN architecture may be co-located, or one or more components of the network entity 105 may be located in distributed locations (e.g., separate physical locations). In some examples, one or more network entities 105 of the decomposed RAN architecture may be implemented as virtual units (e.g., virtual CU (VCU), virtual DU (VDU), virtual RU (VRU)).

[0035] The functional split between CU 160, DU 165, and RU 175 is flexible and may support different functionalities depending on which functions (e.g., network layer functions, protocol layer functions, baseband functions, RF functions, and any combination thereof) are performed at CU 160, DU 165, and RU 175. For example, a functional split of a protocol stack may be employed between CU 160 and DU 165 such that CU 160 may support one or more layers of a protocol stack and DU 165 may support one or more different layers of a protocol stack. In some examples, CU 160 may host higher protocol layer (e.g., layer 3 (L3), layer 2 (L2)) functionality and signaling (e.g., radio resource control (RRC), service data adaptation protocol (SDAP), packet data convergence protocol (PDCP)). The CU 160 may be connected to one or more DUs 165 or RUs 170, and the one or more DUs 165 or RUs 170 may host lower protocol layers, such as layer 1 (L1) (e.g., physical (PHY) layer) or L2 (e.g., radio link control (RLC) layer, medium access control (MAC) layer) functionality and signaling, and may each be at least partially controlled by the CU 160. Additionally or alternatively, a functional split of the protocol stack may be employed between the DU 165 and the RU 170, such that the DU 165 may support one or more layers of the protocol stack, and the RU 170 may support one or more different layers of the protocol stack. The DU 165 may support one or more different cells (e.g., via one or more RUs 170). In some cases, the functional split between CU 160 and DU 165 or between DU 165 and RU 170 may be within the protocol layer (e.g., some functions of the protocol layer may be performed by one of CU 160, DU 165, or RU 170, while other functions of the protocol layer are performed by the other of CU 160, DU 165, or RU 170). CU 160 may be further functionally split into CU control plane (CU-CP) functions and CU user plane (CU-UP) functions. CU 160 may be connected to one or more DUs 165 via midhaul communication links 162 (e.g., F1, F1-c, F1-u), and DU 165 may be connected to one or more RUs 170 via fronthaul communication links 168 (e.g., open fronthaul (FH) interface). In some examples, midhaul communication link 162 or fronthaul communication link 168 may be implemented based on interfaces (eg, channels) between layers of a protocol stack supported by respective network entities 105 that communicate over these communication links.

[0036] In a wireless communication system (e.g., wireless communication system 100), infrastructure and spectrum resources for radio access may support wireless backhaul link capabilities to supplement wired backhaul connections, thereby providing an IAB network architecture (e.g., to a core network 130). In some cases, in an IAB network, one or more network entities 105 (e.g., IAB nodes 104) may be partially controlled by each other. One or more IAB nodes 104 may be referred to as donor entities or IAB donors. One or more DUs 165 or one or more RUs 170 may be controlled in part by one or more CUs 160 associated with a donor network entity 105 (e.g., a donor base station 140). One or more donor network entities 105 (e.g., IAB donors) may communicate with one or more additional network entities 105 (e.g., IAB nodes 104) via supported access and backhaul links (e.g., backhaul communication links 120). The IAB node 104 may include an IAB mobile terminal (IAB-MT) controlled (e.g., scheduled) by a DU 165 of a coupled IAB donor. The IAB-MT may include an independent set of antennas for relaying communications with the UE 115, or may share the same antennas of the IAB node 104 (e.g., of the RU 170) for access via the DU 165 of the IAB node 104 (e.g., referred to as a virtual IAB-MT (vIAB-MT)). In some examples, the IAB node 104 may include a DU 165 that supports communication links with additional entities (e.g., IAB node 104, UE 115) within a relay chain or configuration (e.g., downstream) of the access network. In such cases, one or more components of the decomposed RAN architecture (e.g., one or more IAB nodes 104 or components of the IAB node 104) may be configured to operate according to the techniques described herein.

[0037] Where the techniques described herein are applied to the context of a disaggregated RAN architecture, one or more components of the disaggregated RAN architecture may be configured to support predictive resource management using UE information in a machine learning model. For example, some operations described as being performed by a UE 115 or a network entity 105 (e.g., a base station 140) may additionally or alternatively be performed by one or more components of the disaggregated RAN architecture (e.g., an IAB node 104, a DU 165, a CU 160, a RU 170, a RIC 175, a SMO 180).

[0038] UE 115 may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable terminology, where a "device" may also be referred to as a unit, a station, a terminal, or a client, etc. UE 115 may also include or may be referred to as a personal electronic device, such as a cellular phone, a personal digital assistant (PDA), a tablet computer, a laptop computer, or a personal computer. In some examples, UE 115 may include or may be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communication (MTC) device, etc., which may be implemented in various objects such as appliances or vehicles, meters, etc.

[0039] The UE 115 described herein is capable of communicating with various types of devices, such as other UEs 115 that may sometimes act as relays, as well as network entities 105 and network equipment including macro eNBs or gNBs, small cell eNBs or gNBs, relay base stations, etc. Figure 1 as shown in .

[0040] The UE 115 and the network entity 105 may communicate wirelessly with each other on one or more carriers via one or more communication links 125 (e.g., access links). The term "carrier" may refer to a set of RF spectrum resources having a defined physical layer structure to support the communication link 125. For example, a carrier for the communication link 125 may include a portion of an RF spectrum band (e.g., a bandwidth portion (BWP)) that operates according to one or more physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each physical layer channel may carry acquisition signaling (e.g., synchronization signals, system information), control signaling for coordinating carrier operations, user data, or other signaling. The wireless communication system 100 may support communication with the UE 115 using carrier aggregation or multi-carrier operation. Depending on the carrier aggregation configuration, the UE 115 may be configured to have multiple downlink component carriers and one or more uplink component carriers. Carrier aggregation may be used for both frequency division duplex (FDD) component carriers and time division duplex (TDD) component carriers. Communication between the network entity 105 and other devices may refer to communication between these devices and any part (e.g., entity, sub-entity) of the network entity 105. For example, when referring to the network entity 105, the terms "sending", "receiving" or "communicating" may refer to any part of the network entity 105 (e.g., base station 140, CU 160, DU 165, RU 170) of the RAN communicating with another device (e.g., directly or via one or more other network entities 105).

[0041] The signal waveform sent on the carrier may be composed of multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques, such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM)). In a system employing MCM techniques, a resource element may refer to a resource of one symbol period (e.g., the duration of one modulation symbol) and one subcarrier, in which case the symbol period and subcarrier spacing may be inversely related. The number of bits carried by each resource element may depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both), so that the more resource elements a device receives and the higher the order of the modulation scheme, the higher the data rate of the device may be. Wireless communication resources may refer to a combination of RF spectrum resources, time resources, and spatial resources (e.g., spatial layers, beams), and the use of multiple spatial resources may increase the data rate or data integrity used for communication with UE 115.

[0042] The time interval of the network entity 105 or the UE 115 may be expressed in multiples of a basic time unit, which may be, for example, T s =1 / (Δf max ·N f ) seconds sampling period, where Δf max It can represent the maximum supported subcarrier spacing, and N f The maximum supported discrete Fourier transform (DFT) size may be indicated. The time intervals of the communication resources may be organized according to radio frames each having a specific duration (e.g., 10 milliseconds (ms)). Each radio frame may be identified by a system frame number (SFN) (e.g., in the range of 0 to 1023).

[0043] Each frame may include a plurality of consecutively numbered subframes or time slots, and each subframe or time slot may have the same duration. In some examples, a frame may be divided into subframes (e.g., in the time domain), and each subframe may be further divided into a certain number of time slots. Alternatively, each frame may include a variable number of time slots, and the number of time slots may depend on the subcarrier spacing. Each time slot may include a certain number of symbol periods (e.g., depending on the length of the cyclic prefix added before each symbol period). In some wireless communication systems 100, a time slot may be further divided into a plurality of micro-time slots containing one or more symbols. In addition to the cyclic prefix, each symbol period may contain one or more (e.g., N f The duration of a symbol period may depend on the subcarrier spacing or the operating frequency band.

[0044] A subframe, a time slot, a mini-time slot, or a symbol may be the smallest scheduling unit (e.g., in the time domain) of the wireless communication system 100 and may be referred to as a transmission time interval (TTI). In some examples, the TTI duration (e.g., the number of symbol periods in a TTI) may be variable. Additionally or alternatively, the smallest scheduling unit of the wireless communication system 100 may be dynamically selected (e.g., in bursts of shortened TTIs (sTTIs)).

[0045] Physical channels may be multiplexed on a carrier according to various techniques. For example, physical control channels and physical data channels may be multiplexed on a downlink carrier using one or more of a time division multiplexing (TDM) technique, a frequency division multiplexing (FDM) technique, or a hybrid TDM-FDM technique. A control region (e.g., a control resource set (CORESET)) for a physical control channel may be defined by a set of symbol periods and may extend across a system bandwidth or a subset of a system bandwidth of a carrier. One or more control regions (e.g., CORESETs) may be configured for a group of UEs 115. For example, one or more UEs in UE 115 may monitor or search a control region to obtain control information according to one or more search space sets, and each search space set may include one or more control channel candidates in one or more aggregation levels arranged in a cascaded manner. The aggregation level for a control channel candidate may refer to the amount of control channel resources (e.g., control channel elements (CCEs)) associated with the coded information for a control information format having a given payload size. The search space sets may include a common search space set configured for transmitting control information to a plurality of UEs 115 , and a UE-specific search space set for transmitting control information to a specific UE 115 .

[0046] In some examples, the network entities 105 (e.g., base stations 140, RUs 170) may be mobile and may provide communication coverage for mobile coverage areas 110. In some examples, different coverage areas 110 associated with different technologies may overlap, but the different coverage areas 110 may be supported by the same network entity 105. In some other examples, overlapping coverage areas 110 associated with different technologies may be supported by different network entities 105. The wireless communication system 100 may include, for example, a heterogeneous network in which different types of network entities 105 provide coverage for various coverage areas 110 using the same or different radio access technologies.

[0047] The wireless communication system 100 may be configured to support ultra-reliable communication or low-latency communication or various combinations thereof. For example, the wireless communication system 100 may be configured to support ultra-reliable low-latency communication (URLLC). UE 115 may be designed to support ultra-reliable, low-latency or critical functions. Ultra-reliable communication may include private or group communication and may be supported by one or more services such as push-to-talk, video or data. Support for ultra-reliable, low-latency functions may include prioritization of services, and such services may be used for public safety or general commercial applications. The terms ultra-reliable, low-latency and ultra-reliable low-latency may be used interchangeably herein.

[0048] In some examples, a UE 115 can communicate directly with other UEs 115 via a device-to-device (D2D) communication link 135 (e.g., according to a peer-to-peer (P2P), D2D, or sidelink protocol). In some examples, one or more UEs 115 in a group that are performing D2D communication may be within a coverage area 110 of a network entity 105 (e.g., a base station 140, a RU 170), which may support aspects of such D2D communication configured or scheduled by the network entity 105. In some examples, one or more UEs 115 in such a group may be outside the coverage area 110 of the network entity 105, or may be unable or not configured to receive transmissions from the network entity 105 for other reasons. In some examples, a group of UEs 115 communicating via D2D communication may support a one-to-many (1:M) system, in which each UE 115 transmits to each of the other UEs 115 in the group. In some examples, the network entity 105 may facilitate scheduling of resources for D2D communication. In some other examples, D2D communications may be performed between UEs 115 without involving network entity 105 .

[0049] The core network 130 may provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. The core network 130 may be an evolved packet core (EPC) or a 5G core (5GC), which may include at least one control plane entity (e.g., a mobility management entity (MME), an access and mobility management function (AMF)) for managing access and mobility, and at least one user plane entity (e.g., a serving gateway (S-GW), a packet data network (PDN) gateway (P-GW), or a user plane function (UPF)) for routing packets or interconnecting to an external network. The control plane entity may manage non-access stratum (NAS) functions, such as mobility, authentication, and bearer management of UE 115 served by a network entity 105 (e.g., a base station 140) associated with the core network 130. User IP packets may be delivered through a user plane entity, which may provide IP address allocation and other functions. The user plane entity may be connected to an IP service 150 for one or more network operators. IP services 150 may include access to the Internet, an intranet, an IP Multimedia Subsystem (IMS), or packet-switched streaming services.

[0050] The wireless communication system 100 may operate using one or more frequency bands that may be in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). Generally speaking, the region of 300 MHz to 3 GHz is referred to as the ultra-high frequency (UHF) region or decimeter band because the wavelength range is approximately one decimeter to one meter. UHF waves may be blocked or redirected by buildings and environmental features (which may be referred to as clusters), but these waves may be sufficient to penetrate structures so that macro cells provide service to UEs 115 located indoors. Transmission of UHF waves may be associated with smaller antennas and shorter ranges (e.g., less than 100 kilometers) compared to transmission using lower frequencies and longer waves in the high frequency (HF) or very high frequency (VHF) portions of the spectrum below 300 MHz.

[0051] The wireless communication system 100 can utilize both licensed RF spectrum bands and unlicensed RF spectrum bands. For example, the wireless communication system 100 can use licensed assisted access (LAA), LTE-unlicensed (LTE-U) radio access technology, or NR technology in unlicensed bands such as the 5 GHz industrial, scientific, and medical (ISM) band. When operating in an unlicensed RF spectrum band, devices such as network entities 105 and UE 115 can use carrier sensing for conflict detection and avoidance. In some examples, operations in unlicensed bands can be based on a carrier aggregation configuration combined with component carriers operating in a licensed band (e.g., LAA). Operations in the unlicensed spectrum may include downlink transmissions, uplink transmissions, P2P transmissions, or D2D transmissions, and the like.

[0052] The network entity 105 (e.g., base station 140, RU 170) or UE 115 may be equipped with multiple antennas that can be used to employ technologies such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communication, or beamforming. The antennas of the network entity 105 or UE 115 may be located in one or more antenna arrays or antenna panels that can support MIMO operations or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be co-located at an antenna assembly (such as an antenna tower). In some examples, antennas or antenna arrays associated with the network entity 105 may be located at different geographical locations. The network entity 105 may have an antenna array having a set of rows and columns of antenna ports that the network entity 105 can use to support beamforming for communication with the UE 115. Similarly, the UE 115 may have one or more antenna arrays that can support various MIMO or beamforming operations. Additionally or alternatively, the antenna panel may support RF beamforming for signals sent via the antenna ports.

[0053] Beamforming (which may also be referred to as spatial filtering, directional transmission, or directional reception) is a signal processing technique that may be used at a transmitting device or a receiving device (e.g., a network entity 105, a UE 115) to shape or direct an antenna beam (e.g., a transmit beam, a receive beam) along a spatial path between the transmitting device and the receiving device. Beamforming may be achieved by combining signals conveyed via antenna elements of an antenna array so that some signals propagating at a particular orientation relative to the antenna array experience constructive interference, while other signals experience destructive interference. Adjustments to signals conveyed via antenna elements may include the transmitting device or the receiving device applying an amplitude offset, a phase offset, or both to signals carried via antenna elements associated with the device. Adjustments associated with each antenna element may be defined by a set of beamforming weights associated with a particular orientation (e.g., for an antenna array of a transmitting device or a receiving device, or for some other orientation).

[0054] The network entity 105 or UE 115 may use beam scanning techniques as part of a beamforming operation. For example, the network entity 105 (e.g., base station 140, RU 170) may use multiple antennas or antenna arrays (e.g., antenna panels) to perform beamforming operations for directional communication with UE 115. Some signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signals) may be sent multiple times by the network entity 105 along different directions. For example, the network entity 105 may send signals according to different sets of beamforming weights associated with different transmission directions. Transmissions along different beam directions may be used to identify (e.g., by a transmitting device (such as network entity 105), or by a receiving device (such as UE 115)) beam directions for later transmission or reception by the network entity 105.

[0055] Some signals (such as data signals associated with a particular receiving device) may be sent by a transmitting device (e.g., transmitting network entity 105, transmitting UE 115) along a single beam direction (e.g., a direction associated with a receiving device such as receiving network entity 105 or receiving UE 115). In some examples, a beam direction associated with transmission along a single beam direction may be determined based on signals sent along one or more beam directions. For example, UE 115 may receive one or more of the signals sent by network entity 105 along different directions, and may report to network entity 105 an indication of the signal received by UE 115 with the highest signal quality or other acceptable signal quality.

[0056] In some examples, transmission by a device (e.g., by network entity 105 or UE 115) may be performed using multiple beam directions, and the device may use a combination of digital precoding or beamforming to generate a combined beam for transmission (e.g., from network entity 105 to UE 115). UE 115 may report feedback indicating precoding weights for one or more beam directions, and the feedback may correspond to a configured beam set across a system bandwidth or one or more subbands. Network entity 105 may send reference signals (e.g., cell-specific reference signals (CRS), CSI-RS) that may or may not be precoded. UE 115 may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., a multi-panel codebook, a linear combination codebook, a port selection codebook). Although these techniques are described with reference to signals sent along one or more directions by a network entity 105 (e.g., base station 140, RU 170), UE 115 may use similar techniques to send signals multiple times along different directions (e.g., to identify a beam direction for subsequent transmission or reception by UE 115), or to send signals along a single direction (e.g., to send data to a receiving device).

[0057] A receiving device (e.g., UE 115) may perform receiving operations according to multiple receiving configurations (e.g., directional listening) when receiving various signals (such as synchronization signals, reference signals, beam selection signals, or other control signals) from a receiving device (e.g., network entity 105). For example, the receiving device may perform reception according to multiple receiving directions by: receiving via different antenna subarrays, processing received signals according to different antenna subarrays, receiving according to different receiving beamforming weight sets (e.g., different directional listening weight sets) applied to signals received at multiple antenna elements of an antenna array, or processing received signals according to different receiving beamforming weight sets applied to signals received at multiple antenna elements of an antenna array, any of which may be referred to as "listening" according to different receiving configurations or receiving directions. In some examples, the receiving device may use a single receiving configuration to receive along a single beam direction (e.g., when receiving a data signal). A single receive configuration may be aligned along a beam direction determined based on listening according to different receive configuration directions (e.g., a beam direction determined to have the highest signal strength, highest signal-to-noise ratio (SNR), or other acceptable signal quality based on listening according to multiple beam directions).

[0058] The machine learning model may improve the beam selection process performed between the network entity 105 and the UE 115 during beam management. For example, the machine learning model may enhance channel state information (CSI) feedback, which may result in improved accuracy and predictions. In examples where the network entity 105 experiences non-line-of-sight (NLoS) conditions, such as where the line of sight (LoS) between the network entity 105 and the UE is blocked by an object, the machine learning model may also provide positioning accuracy enhancements. However, other different wireless communication systems may not provide different data sets for training the machine learning model, which may cause the machine learning model to provide inefficient predictions.

[0059] The network entity 105 and the UE 115 may jointly determine a beam pair for efficient communication. The network entity 105 may perform an SSB beam scanning process for a serving cell that may be operating on a primary frequency. The UE 115 may then measure the received power for each beam of the beam scanning and report to the network entity 105 which beam may have the highest received power. The network entity 105 may refine the transmit beam by performing CSI-RS beam scanning using a beam narrower than the beam used in the SSB beam scanning. Similarly, the UE 115 may measure the received power for each transmitted beam and report to the network entity 105 which of the narrow beams may have the highest received power. The network entity 105 may repeatedly send CSI-RS to the UE 115 while the UE 115 performs receive beam scanning to find the receive beam that may produce the highest received power associated with the transmit beam.

[0060] In some cases, the network entity 105 may employ a machine learning model to predict the transmit beam that may produce the highest receive power at the UE 115. In some examples, the network entity 105 may train the machine learning model using historically measured channel characteristics of the transmit beam. However, in examples where the network entity 105 uses receive beam information from the UE 115 to train the machine learning model, the machine learning model prediction may be more accurate. For example, the UE 115 may rotate or experience a maximum permitted exposure event, which may cause the receive beam at the UE 115 to be gradually changed. The transmit beam at the network entity 105 may also be gradually changed to enable efficient communication between the UE 115 and the network entity 105. Thus, in order for the machine learning model to accurately predict an efficient transmit beam for the network entity 105, the network entity 105 may train the machine learning model using historically measured channel characteristics and receive beam information. For example, the network entity 105 may train the machine learning model with channel characteristic data that may be specific to a given scenario, but in order for the machine learning model to be useful, the network entity 105 may train the machine learning model with different training data to support the performance of various tasks between the network entity and the UE. However, in some examples, the UE 115 receive beam information may not be available at the network entity 105, which may limit the accuracy of the predictions made by the machine learning model. Additionally or alternatively, fully disclosing the characteristics of the receive beam may expose secret or security information.

[0061] According to the techniques, methods, and apparatus described herein, UE 115 may enhance CSI-RS reporting to send receive beam information and associated transmit beam channel characteristics back to network entity 105. In some examples, UE 115 may perform implicit reporting of receive beams by using additional channel measurement resources or sounding reference signal (SRS) resources to associate with different receive beam options, so that UE 115 may avoid disclosing antenna or beam implementations or avoid overly diverse training of machine learning models for network entity 105. That is, UE 115 may avoid limiting machine learning models to one UE implementation, model, or device. Additionally or alternatively, in examples where disclosure of UE 115 implementation is not confidential or secure, UE 115 may explicitly report the number of receive beams. In some examples, network entity 105 may send a machine learning model to UE 115 so that UE 115 may also perform receive beam prediction.

[0062] Figure 2An example of a wireless communication system 200 that supports predictive resource management using UE receive beam information in a machine learning model according to one or more aspects of the present disclosure is illustrated. The wireless communication system 200 may be implemented or may be implemented to implement aspects of the wireless communication system 100. For example, the wireless communication system 200 illustrates communication between a UE 115-a and a network entity 105-a, which may be as described herein (including references to Figure 1 ) are examples of corresponding devices described herein. In some cases, UE 115-a may communicate with network entity 105-a. For example, network entity 105-a may send control information, data, or both to UE 115-a via downlink communication link 245, while UE 115-a may communicate control information, data, or both with network entity 105-a via uplink communication link 240.

[0063] In some examples, to establish communication between the network entity 105-a and the UE 115-a, the network entity 105-a and the UE 115-a may perform one or more beam management (BM) procedures. For example, the network entity 105-a and the UE 115-a may perform the following procedures during initial access: Figure 1 The described beam scanning process, performing beam measurement and determination processes during connected mode, performing beam reporting processes (e.g., L1 reporting for beam refinement) during connected mode, and performing beam recovery processes for beam failure recovery (BFR) or radio link failure (RLF). During the initial access process, the network entity 105-a may transmit in multiple directions (e.g., beams) to synchronize communications with the UE 115-a. For example, the network entity 105-a may use supported beams to transmit reference signals such as SSB, CSI-RS, or both in a set of directions (e.g., may scan through multiple SSB resources). In some examples, the network entity 105-a and the UE 115-a may use a wider beam, such as an L1 beam, for the initial access process. The UE 115-a may receive one or more reference signals on the corresponding beams, and may select or report one or more preferred beams based on signal metrics. For example, UE 115-a may transmit a report to network entity 105-a indicating the SSB with the largest RSRP value for a random access channel (RACH) procedure. The described process may also be performed by network entity 105-a for selecting a transmit beam for UE 115-a and for fine tuning a receive beam at network entity 105-a.

[0064] In some examples, there may be one or more different types of BM processes, such as a first process type (P1) for a downlink beam, a second process type (P2) for a downlink beam, and a third process type (P3) for a downlink beam, a first process type (U1) for an uplink beam, a second process type (U2) for an uplink beam, and a third process type (U3) for an uplink beam.

[0065] In some examples, the network entity 105-a and the UE 115-a may use hierarchical beam refinement to select a narrower beam pair for communication (e.g., using P1, P2, P3, or any combination thereof). For example, for P1, the network entity 105-a may scan through multiple wider beams, and the UE 115-a may select a beam and report it to the network entity 105-a. For P2, the network entity 105-a may transmit in multiple relatively narrow directions (e.g., may scan through multiple narrower beams in a narrower range), where these narrow directions may be based on the direction of the selected wide beam pair. The UE 115-a may receive a reference signal on the wide beam and may report one of the narrow beams for transmission, thereby refining the transmit beam. For P3, the network entity 105-a may repeatedly transmit the selected beam (e.g., may fix the beam), and the UE 115-a may refine the receive beam based on the transmitted beam (e.g., select a narrower receive beam). In some examples, the P1, P2, and P3 procedures may be used for downlink BM. In some examples, the network entity 105-a and the UE 115-a may employ an uplink BM procedure to select a wide uplink beam pair, refine an uplink receive beam at the network entity 105-a, and refine an uplink transmit beam at the UE 115-a, which procedures may be examples of U1, U2, and U3 procedures, respectively. In some cases, the UE 115-a may report beams using a physical layer (e.g., using L1 reporting). In some examples, the UE 115-a and the network entity 105-a may be in a connected mode, where the connection is successfully made through the selected beam pair.

[0066] In some examples, network entity 105-a and UE 115-a may experience a beam failure. For example, UE 115-a may lose connection with network entity 105-a through the selected beam pair. In some examples, UE 115-a may perform BFR to select a new suitable beam pair through an additional beam scanning process. In some examples, UE 115-a may not be able to find another suitable beam and may experience RLF, resulting in loss of connection with network entity 105-a.

[0067] In some examples, the beam scanning process may exhibit inefficiencies in communications. For example, the network entity 105-a and the UE 115-a may perform excessive beam scanning before selecting an appropriate beam. Excessive beam scanning may result in excessive latency, overhead, and power usage at the UE 115-a (e.g., by changing a phase shift component for transmitting in a new direction).

[0068] In some examples, the network entity 105-a and the UE 115-a may use machine learning-based beam change prediction to mitigate shortcomings and improve the beam scanning process. For example, the network entity 105-a may implement a machine learning model to predict channel characteristics of the communication. In some examples, the machine learning model may be an example of a deep learning machine learning model, where the deep learning machine learning model may include multiple layers of operations between input and output. For example, the machine learning model may represent a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, a generative adversarial network (GAN) model, or any other deep learning or other neural network model. In some examples, the machine learning model may represent a subset of the RNN model (such as an LSTM model), where the LSTM model may involve learning and memorizing long-term dependencies over time to make predictions based on time series data. For example, the machine learning model may include an LSTM unit with a time series input, and the output from the LSTM unit may be passed to additional instances of the unit over time for selectively updating the machine learning model value for prediction. In some examples, the machine learning model may predict whether the preferred reference signal beam will remain preferred compared to the last received beam based on historical measurements. For example, the machine learning model may predict whether the SSB beam with the current highest RSRP will have the highest RSRP at the next measurement opportunity.

[0069] In some examples, the network entity 105-a may use a learning method to train a machine learning model. For example, the network entity 105-a may use supervised learning, semi-supervised learning, or unsupervised learning to train a machine learning model. Supervised learning may involve training a machine learning model based on labeled training data, which may include example input-output pairs, while unsupervised learning may involve training a machine learning model based on unlabeled training data, which consists of data without example input-output pairs. Semi-supervised learning may involve a small amount of labeled training data and a large amount of unlabeled training data. In some cases, a machine learning model may use supervised learning for prediction as described herein.

[0070] UE 115-a may support machine learning model training at network entity 105-a by reporting receive beam information together with channel characteristics, where the channel characteristics may include L1 reference signal received power (L1-RSRP), L1 signal to interference and noise ratio (L1-SINR), rank indicator (RI), channel quality indicator (CQI), PMI, or any combination thereof. In some examples, network entity 105-a may configure UE 115-a using one or more reference signals (e.g., by sending CSI report setting 235). In some other cases, one or more parameters of the reference signal used for the beam scanning process may be defined (e.g., preconfigured) in other ways at UE 115-a. For example, during an SSB beam scanning process, network entity 105 may transmit using one or more wideband transmit beams (such as wideband transmit beam 210-a, wideband transmit beam 210-b, or both) that may have a first set of channel measurement resources. UE 115-a may use the reference signal to measure the receive power of the corresponding wideband transmit beam 210-a and wideband transmit beam 210-b. In some examples, UE 115-a may determine that receive beam 205-a has a higher receive power than receive beam 205-b. Thus, UE 115-a may send a report 220 via an uplink communication link 240, which may include channel characteristics of a set of channel measurement resources (e.g., CSI-RS or SSB resources of wideband transmit beam 210-a and wideband transmit beam 210-b) and information for receive beam 205-a.

[0071] In some examples, the network entity 105-a may also perform additional CSI-RS beam scanning using one or more of narrowband transmit beam 215-a, narrowband transmit beam 215-b, narrowband transmit beam 215-c, or narrowband transmit beam 215-d, which may have a second set of channel measurement resources. For example, UE 115-a may determine that both receive beam 205-a and receive beam 205-b have similar receive power as wideband transmit beam 210-a. In some examples, UE 115-a may determine the highest receive power of the second set of channel measurement resources (e.g., narrowband transmit beam 215-a to narrowband transmit beam 215-d). In some examples, UE 115-a may determine that the received power of narrowband transmit beam 215-a is higher than the received power of narrowband transmit beam 215-b, and receive beam 205-a may have a higher received power than receive beam 205-b associated with narrowband transmit beam 215-a. As a result, UE 115-a may send a report 220 to network entity 105-a, indicating an identifier of a second set of channel measurement resources (e.g., an identifier for narrowband transmit beam 215-a). In some cases, UE 115-a may send report 220 to network entity 105-a in uplink control information. Additionally or alternatively, UE 115-a may send an identifier of narrowband transmit beam 215-b. In some examples, UE 115-a may send multiple identifiers of both narrowband transmit beam 215-a and narrowband transmit beam 215-b. In some examples, the identifier may be a channel measurement resource identifier, while in some other cases, the identifier may be an SRS identifier. In this manner, UE 115-a may determine receive beam 205-a or receive beam 205-b for measuring the first set of channel measurement resources based on the reported one or more channel measurement resources in the second set of channel measurement resources.

[0072] In some implementations, UE 115-a may explicitly report receive beam information, such as by sending beamforming predecoder information for the receive beam (e.g., transmit predecoder matrix indicator (TPMI), phase or amplitude coefficients of corresponding RF chains or phase shifters), antenna panel identification of the receive beamforming predecoder, antenna panel orientation of the receive beamforming, or angle information (e.g., target arrival angle or arrival zenith angle). Sending such information may cause UE 115-a to disclose its antenna and beamforming implementation. Additionally or alternatively, explicitly disclosing the receive beam may limit the machine learning model to the UE 115-a implementation, and different UEs (e.g., brands or models of UEs) may not be able to share the same machine learning model.

[0073] UE 115-a may periodically transmit a report 220 to network entity 105-a having updated implicit or explicit receive beam information for receive beam 205-a, receive beam 205-b, or both. UE 115-a may send a capability message indicating the type of capability to support receive beam information reporting. For example, a manufacturer may not want to expose a specific implementation of UE 115-a to competitors, so UE 115-a may report implicit beamforming information so that the specific implementation can remain secret even in examples where the machine learning model can be used by other UEs. In some other examples, UE 115-a may not be able to determine the orientation of UE 115-a. UE 115-a may transmit a report 220 to network entity 105-a, indicating that UE 115-a may not support providing the orientation to network entity 105-a. In some examples, it may be beneficial to disclose UE 115-a beamforming parameters (such as detailed angle information) in order to avoid sending or measuring additional channel measurement resources.

[0074] In some examples, the network entity 105-a may train a machine learning model using spatial and time domain downsampled channel characteristics of the first set of channel measurement resources and receive beam information as received in report 220. In some examples, the receive beam information may be related to UE 115-a position or location information that may also be included as an input to the machine learning model. For example, at one location, the receive beam that maximizes the CSI-RS resource L1-RSRP among the second set of CSI-RS resources may be determined as a receive beam fingerprint. The fingerprint may refer to a transmitted attribute, such as the location and configuration of the signal, so that another device can identify the UE 115-a from the receive beam.

[0075] The machine learning model may output spatial domain and time domain channel characteristic predictions for at least a first set of channel measurement resources. Additionally or alternatively, the machine learning model may output receive beamforming information of the predicted channel characteristics (e.g., based on implicit or explicit reports 220). The network entity 105-a may use the predicted receive beamforming information to send a physical downlink shared channel (PDSCH) 225 via a downlink communication link 245. In some examples, the machine model may be used for one or more time domain beam predictions, one or more spatial domain beam predictions, one or more frequency domain beam predictions, or a combination thereof. In some cases, one or more time domain, spatial domain, or frequency domain beam predictions may include one or more channel characteristic predictions for different beams (e.g., may include predictions for RSRP, SINR, RI, PMI, LI, CQI, or any combination thereof). In some examples, one or more time domain predictions may include predicting future channel characteristics based on a channel measurement history associated with multiple reference signal resources. For example, one or more time domain predictions may be based on a history of measurements, or based on measurements made at UE 115-a of one or more channel measurement resources (such as SSB or CSI-RS resources).

[0076] In some examples, one or more spatial domain predictions may include predicting channel characteristics of unmeasured reference signal resources (e.g., SSB or CSI-RS resources) based on the measured multiple reference signal resources. In some cases, one or more spatial domain predictions may include predicting a departure angle for downlink precoding based on the measured multiple reference signal resources, or may include predicting a linear combination of the measured multiple reference signal resources as a preferred downlink precoding.

[0077] In some examples, the network entity 105-a may send the machine learning model configuration 230 to the UE 115-a, other UEs, or both (e.g., UEs that have not trained the machine learning model). The UE 115-a may perform additional training on the machine learning model using the spatial and time domain downsampled channel characteristics of the first set of channel measurement resources. Additionally or alternatively, the UE 115-a may input implicit or explicit receive beam information to the machine learning model. The machine learning model may output spatial and time domain channel characteristic predictions for at least the first set of channel measurement resources. Additionally or alternatively, the machine learning model may output implicit or explicit receive beam information.

[0078] Figure 3An example of a process flow 300 for supporting predictive resource management using UE receive beam information in a machine learning model according to one or more aspects of the present disclosure is illustrated. In some examples, the process flow 300 can implement aspects of the wireless communication system 100 and the wireless communication system 200. The process flow 300 can illustrate an example of a UE 115-b that can implement the machine learning model 305-a and a network entity 105-b that can implement the machine learning model 305-b. The network entity 105-b and the UE 115-b can be as described in reference Figure 1 and Figure 2 The examples of the network entity 105 and the UE 115 described above. The following alternative examples may be implemented, where some of the processes are performed in a different order than described or are not performed. In some cases, each process may include additional features not mentioned below, or further processes may be added.

[0079] In some specific implementations, UE 115-b may communicate with network entity 105-b. Network entity 105-b may train machine learning model 305-b. In some examples, UE 115-b may determine a receive beam of one or more channel measurement resource identifiers based on comparing one or more first channel characteristics of a second set of channel measurement resources. For example, network entity 105-b may configure UE 115-b with one or more reference signals via CSI report settings. In some examples, network entity 105-b may transmit via a first set of channel measurement resources and a second set of channel measurement resources (e.g., CSI-RS resources). Network entity 105-b may use the first set of CSI-RS resources to schedule PDSCH transmissions. UE 115-b may periodically transmit multiple reference signals of the first set of CSI-RS resources to help network entity 105-b determine an appropriate transmit beam. For example, as shown in process flow 300, UE 115-b may send a report to network entity 105-b every 20ms. In some examples, the report may include a CSI-RS resource indicator (cri) RSRP. In some examples, receive beam information may be reported in a carrier of the second part of a two-part CSI report due to a lower priority. For example, because machine learning model training may be a low priority task, a CSI report including receive beam information may have a lower priority than other CSI reports.

[0080] The network entity 105-b may further configure one or more second groups of CSI-RS resources so that each CSI-RS resource in the second group of CSI-RS resources may overlap with a CSI-RS resource within the first group of CSI-RS resources. In some examples, the angular spread of a CSI-RS resource within the second group of CSI-RS resources may be less than the angular spread of a CSI-RS resource within the first group of CSI-RS resources. The UE 115-b may determine a receive beam so that the reference signal received power may be maximized for the CSI-RS resources among the CSI-RS resources within the second group of CSI-RS resources. For example, the UE 115-b may identify the CSI-RS resources within the first group of CSI-RS resources. The UE 115-b may further identify the CSI-RS resources in the second group of CSI-RS resources that overlap with the first group of CSI-RS resources. Thus, the UE 115-b may determine a receive beam for the first group of CSI-RS resources based on the reported one or more channel measurement resources in the second group of channel measurement resources. In some examples, UE 115-b may report a CSI-RS resource identifier for a receive beam (e.g., a CSI-RS resource identifier within a second set of CSI-RS resources) as receive beam information. The identifier may be included with the cri-RSRP reported every 20 ms in process flow 300 for the first set of CSI-RS resources.

[0081] In some examples, the network entity 105-b may train the machine learning model 305-b using spatial and temporal down-sampled channel characteristics of the first set of channel measurement resources. The network entity 105-b may train the machine learning model 305-b using additional data, such as reports periodically received from the UE 115-b that include a channel measurement resource identifier within the second set of channel measurement resources for receiving beam information. For example, in the process flow 300, the network entity 105-b may input the UE 115-b report data to the trained machine learning model 305-b every 80 ms.

[0082] The machine learning model 305-b may periodically output spatial domain and time domain channel characteristic predictions for at least a first set of channel measurement resources. In some examples, the machine learning model 305-b may also output the cri-RSRP of the first set of CSI-RS resources and the CSI-RS resource identifiers of the second set of channel measurement resources in the predicted channel characteristics (e.g., predicted UE receive beam information). For example, the machine learning model 305-b may output predictions every 20ms before the next available 80ms input sample arrives. In some examples, the predicted receive beam information may be used as a label for calculating a loss function (e.g., maximum permitted exposure or cross entropy) when training the machine learning model 305-b. For example, at times 0ms, 20ms, and 40ms in the process flow 300, the UE 115-b may transmit a report that may not be directly input into the machine learning model 305-b but may be used in the calculation of the loss function.

[0083] In another specific implementation, the network entity 105-b may transmit the configuration of the machine learning model 305-a to the UE 115-b before time 0ms in the process flow 300, so that both the network entity 105-b and the UE 115-b can access the machine learning model 305-a or the machine learning model 305-b. Network entity machine learning model reasoning can be facilitated in the same manner as previously discussed (e.g., extending the reporting period for time domain prediction or reducing the spatially measured beams for spatial domain prediction). The UE 115-b may periodically use and train the machine learning model 305-a with additional inputs. For example, in the process flow 300, the UE 115-b may input the spatial domain and time domain downsampled channel characteristics of the first group of channel measurement resources (e.g., cri-RSRP of the first group of CSI-RS resources) and the channel measurement resource identifier of the second group of CSI-RS resources (e.g., CSI-RS resource identifier) ​​to the machine learning model 305-a every 80ms. The input may be determined based on a set of CSI-RS resources sent by the network entity 105-b at 310 and 320. The machine learning model 305-a may periodically output a predicted receive beam (e.g., a cri-RSRP for a first set of CSI-RS resources and a CSI-RS resource identifier for a second set of CSI-RS resources) every 20 ms before the next available 80 ms input sample. The UE 115-b may periodically report the spatial and time domain predicted cri-RSRP to the network entity 105-b. In some examples, the UE 115-b may use the machine learning model 305-a to obtain channel characteristic predictions based on multiple reference signals sent in a report to the network entity 105-b every 20 ms.

[0084] At 315 and 325, network entity 105-b may transmit multiple PDSCH messages using the transmit beam predicted by machine learning model 305-b. UE 115-b may receive these transmissions using the receive beam reported to network entity 105-b. In some examples, UE 115-b may receive PDSCH messages using the receive beam predicted by machine learning model 305-a.

[0085] Figure 4 An example of a process flow 400 for supporting predictive resource management using UE receive beam information in a machine learning model according to one or more aspects of the present disclosure is illustrated. In some examples, the process flow 400 can implement aspects of the wireless communication system 100, the wireless communication system 200, and the process flow 300. The process flow 400 can illustrate an example of a UE 115-c that can implement the machine learning model 405-a and a network entity 105-c that can implement the machine learning model 405-b. The network entity 105-c and the UE 115-c can be as described in reference Figure 1 and Figure 2 The examples of the network entity 105 and the UE 115 described above. The following alternative examples may be implemented, where some of the processes are performed in a different order than described or are not performed. In some cases, each process may include additional features not mentioned below, or further processes may be added.

[0086] Figure 4 A process for implicitly reporting a receive beam based on SRS and other channel measurement resources is shown. For example, a UE 115-c may report one or more SRS resource identifiers of an SRS resource set of a CSI report setting. In some examples, a transmit spatial filter for an SRS resource may be within an SRS resource set determined by channel measurement resources within a second set of channel measurement resources also configured by a first CSI report setting. Thus, the UE 115-c may determine a receive beam for measuring a first set of channel measurement resources based on a transmit filter of the reported SRS resource identifier. The UE 115-c may periodically transmit a report to the network entity 105-c as an SRS resource indicator (SRI). In some examples, a report may be sent every 20 ms as shown in process flow 400.

[0087] In some implementations, the network entity 105-c may train the machine learning model 405-b using input received from the UE 115-c in periodic reports. For example, the network entity 105-c may input an associated SRS resource identifier for a UE receive beam. Additionally or alternatively, the network entity 105-c may input spatial and time domain downsampled channel characteristics for a first set of channel measurement resources. At 415, 425, and 435, the UE 115-c may periodically send the determined SRS resource identifier for the receive beam to the network entity 105-c. The network entity 105-c may use the identifier to further train the machine learning model 405-b.

[0088] The machine learning model 405-b may periodically provide outputs including predictions of spatial and time domain channel characteristics of at least a first set of channel measurement resources. Additionally or alternatively, the machine learning model 405-b may output a predicted UE receive beam (e.g., an SRS resource identifier within an SRS resource set of the predicted channel characteristics of the first set of channel measurement resources). In the example shown in process flow 400, the machine learning model 405-b may provide output data every 80 ms.

[0089] In another specific implementation, the network entity 105-c may send a machine learning model configuration to the UE 115-c before the time 0ms shown in the process flow 400. Thereby, the UE 115-c may use the machine learning model 405-a to predict the spatial domain and time domain channel characteristics of the first set of channel measurement resources. For example, at 410 and 430, the network entity 105-c may periodically send a CSI-RS resource set to the UE 115-c. The UE 115-c may determine a receive beam based on the received CSI-RS resource set. Thereby, the UE 115-c may input the spatial domain and time domain downsampled channel characteristics of the first set of channel measurement resources and the determined receive beam information (e.g., an SRS resource identifier or an associated channel measurement resource identifier within the second set of channel measurement resources of the determined receive beam) into the machine learning model 405-a.

[0090] In some examples, the transmit spatial filter for the SRS resources within the SRS resource set may be determined by the channel measurement resources within the second set of channel measurement resources. Thus, the receive beam from the UE measuring the first set of channel measurement resources may be determined based on the transmit filter of the reported SRS resources.

[0091] The machine learning model 405-a may output predictions of spatial and time domain channel characteristics for at least a first set of channel measurement resources. Additionally or alternatively, the machine learning model 405-a may output a UE-predicted receive beam (e.g., an SRS resource identifier within an SRS resource set in the predicted channel characteristics or a CSI-RS resource identifier within a second set of channel measurement resources).

[0092] At 420 and 440, the network entity 105-c may periodically transmit PRSCH messages using the transmit beam predicted by the machine learning model 405-b. The UE 115-c may receive these transmissions using the receive beam that helped train the machine learning model 405-b. In some examples, the UE 115-c may use the receive beam predicted by the machine learning model 405-a.

[0093] Figure 5 An apparatus for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The apparatus may be an example of aspects of a UE 115 as described herein. The apparatus may include a receiver, a transmitter, and a communication manager. The communication manager may be implemented at least in part by one or both of a modem and a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0094] The receiver may provide means for receiving information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels related to predictive resource management using user equipment information in machine learning models, data channels, information channels). The information may be passed to other components of the device. The receiver may utilize a single antenna or a set of multiple antennas.

[0095] The transmitter may provide means for transmitting signals generated by other components of the device. For example, the transmitter may transmit information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels, data channels, information channels related to predictive resource management using user equipment information in machine learning models). In some examples, the transmitter may be co-located with the receiver in a transceiver module. The transmitter may utilize a single antenna or a set of multiple antennas.

[0096] The communication manager, receiver, transmitter, or various combinations thereof or various components thereof may be examples of means for performing various aspects of predictive resource management using user equipment information in a machine learning model as described herein. For example, the communication manager, receiver, transmitter, or various combinations thereof or components thereof may support methods for performing one or more of the functions described herein.

[0097] In some examples, the communication manager, receiver, transmitter, or various combinations or components thereof may be implemented in hardware (e.g., in a communication management circuit). The hardware may include a processor, a digital signal processor (DSP), a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a microcontroller, discrete gate or transistor logic, discrete hardware components, or any combination thereof that is configured to or otherwise supports means for performing the functions described herein. In some examples, a processor and a memory coupled to the processor may be configured to perform one or more of the functions described herein (e.g., by executing instructions stored in the memory by the processor).

[0098] Additionally or alternatively, in some examples, the communication manager, receiver, transmitter, or various combinations or components thereof may be implemented in code executed by a processor (e.g., implemented as communication management software or firmware). If implemented in code executed by a processor, the functionality of the communication manager, receiver, transmitter, or various combinations or components thereof may be performed by a general purpose processor, DSP, CPU, ASIC, FPGA, or any combination of these or other programmable logic devices (e.g., configured as or otherwise supporting means for performing the functionality described in the present disclosure).

[0099] In some examples, the communication manager can be configured to perform various operations (e.g., receive, acquire, monitor, output, send) using or otherwise cooperating with the receiver, the transmitter, or both. For example, the communication manager can receive information from the receiver, transmit information to the transmitter, or integrate with the receiver, the transmitter, or both to acquire information, output information, or perform various other operations as described herein.

[0100] According to an example as disclosed herein, a communication manager may support wireless communications at a UE. For example, the communication manager may be configured to or otherwise support a device for sending a set of multiple reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for communication. The communication manager may be configured to or otherwise support a device for receiving, based on sending the set of multiple reference signals, a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources, the machine learning model being based on the receive beam at the UE. The communication manager may be configured to or otherwise support a device for inputting an input to the machine learning model for obtaining a channel characteristic prediction. The communication manager may be configured to or otherwise support a device for receiving signaling based on obtaining a channel characteristic prediction associated with the first set of channel measurement resources.

[0101] Additionally or alternatively, according to examples as disclosed herein, the communication manager may support wireless communications at the UE. For example, the communication manager may be configured to or otherwise support a device for sending information associated with one or more channel measurement resources and a receive direction for communication. The communication manager may be configured to or otherwise support a device for receiving, based on sending the information, a signaling indicating a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources. The communication manager may be configured to or otherwise support a device for inputting input to a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources. The communication manager may be configured to or otherwise support a device for receiving signaling based on obtaining channel characteristic predictions associated with one or more channel measurement resources.

[0102] By including or configuring a communication manager according to examples as described herein, a device (e.g., a processor controlling a receiver, a transmitter, a communication manager, or a combination thereof or otherwise coupled thereto) can support techniques for more efficiently utilizing communication resources.

[0103] Figure 6 An apparatus for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The apparatus may be an example of aspects of a device or UE 115 as described herein. The apparatus may include a receiver, a transmitter, and a communication manager. The communication manager may be implemented at least in part by one or both of a modem and a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0104] The receiver may provide means for receiving information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels related to predictive resource management using user equipment information in machine learning models, data channels, information channels). The information may be passed to other components of the device. The receiver may utilize a single antenna or a set of multiple antennas.

[0105] The transmitter may provide means for transmitting signals generated by other components of the device. For example, the transmitter may transmit information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels, data channels, information channels related to predictive resource management using user equipment information in machine learning models). In some examples, the transmitter may be co-located with the receiver in a transceiver module. The transmitter may utilize a single antenna or a set of multiple antennas.

[0106] The device or its various components may be examples of apparatuses for performing various aspects of predictive resource management using user equipment information in a machine learning model as described herein. For example, a communication manager may include a reference signal transmitter component, a machine learning model receiver component, a machine learning model input component, a signal receiver component, a channel measurement resource transmitter component, or any combination thereof. The communication manager may be an example of various aspects of a communication manager as described herein. In some examples, the communication manager or its various components may be configured to perform various operations (e.g., receive, acquire, monitor, output, send) using or otherwise in conjunction with a receiver, a transmitter, or both. For example, the communication manager may receive information from a receiver, transmit information to a transmitter, or integrate with a receiver, a transmitter, or both in combination to acquire information, output information, or perform various other operations as described herein.

[0107] According to an example as disclosed herein, a communication manager may support wireless communication at a UE. A reference signal transmitter component may be configured as or otherwise support a device for sending a set of multiple reference signals, the set of multiple reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for communication. A machine learning model receiver component may be configured as or otherwise support a device for receiving signaling indicating a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources based on sending the set of multiple reference signals, the machine learning model being based on a receive beam at the UE. A machine learning model input component may be configured as or otherwise support a device for inputting an input to a machine learning model for obtaining a channel characteristic prediction. A signal receiver component may be configured as or otherwise support a device for receiving signaling based on obtaining a channel characteristic prediction associated with the first set of channel measurement resources.

[0108] Additionally or alternatively, according to examples as disclosed herein, the communication manager may support wireless communications at the UE. The channel measurement resource transmitter component may be configured as or otherwise support a device for sending information associated with one or more channel measurement resources and a receive direction for communication. The machine learning model receiver component may be configured as or otherwise support a device for receiving signaling indicating a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources based on sending the information. The machine learning model input component may be configured as or otherwise support a device for inputting input to the machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources. The signal receiver component may be configured as or otherwise support a device for receiving signaling based on obtaining channel characteristic predictions associated with one or more channel measurement resources.

[0109] Figure 7A communication manager that supports predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The communication manager can be an example of a communication manager as described herein, a communication manager, or aspects of both. The communication manager or its various components can be examples of devices for performing various aspects of predictive resource management using user equipment information in a machine learning model as described herein. For example, the communication manager may include a reference signal transmitter component, a machine learning model receiver component, a machine learning model input component, a signal receiver component, a channel measurement resource transmitter component, a receive beam determination component, a resource identifier transmitter component, a control signaling receiver component, a capability message transmitter component, a first channel state report transmitter component, a second channel state report transmitter component, a prediction acquisition component, or any combination thereof. Each of these components can communicate directly or indirectly with each other (e.g., via one or more buses).

[0110] According to an example as disclosed herein, a communication manager may support wireless communication at a UE. A reference signal transmitter component may be configured as or otherwise support a device for sending a set of multiple reference signals, the set of multiple reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for communication. A machine learning model receiver component may be configured as or otherwise support a device for receiving signaling indicating a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources based on sending the set of multiple reference signals, the machine learning model being based on a receive beam at the UE. A machine learning model input component may be configured as or otherwise support a device for inputting an input to a machine learning model for obtaining a channel characteristic prediction. A signal receiver component may be configured as or otherwise support a device for receiving signaling based on obtaining a channel characteristic prediction associated with the first set of channel measurement resources.

[0111] In some examples, the receive beam determination component may be configured to or otherwise support means for determining a receive beam corresponding to a receive direction for communication for the UE based on one or more channel measurement resource identifiers corresponding to the second set of channel measurement resources and one or more first channel characteristics associated with the second set of channel measurement resources, wherein the information includes the one or more first channel characteristics. In some examples, the resource identifier transmitter component may be configured to or otherwise support means for transmitting the one or more channel measurement resource identifiers in the same signal as a subset of the set of multiple reference signals associated with the second set of channel measurement resources, wherein the receive signaling is based on transmitting the one or more channel measurement resource identifiers.

[0112] In some examples, the machine learning model input component may be configured to or otherwise support means for inputting one or more channel measurement resource identifiers, one or more second channel characteristics associated with the first set of channel measurement resources, or both to the machine learning model based on sending the set of multiple reference signals. In some examples, the prediction acquisition component may be configured to or otherwise support means for acquiring channel characteristic predictions associated with the first set of channel measurement resources based on inputting one or more channel measurement resource identifiers, one or more second channel characteristics associated with the first set of channel measurement resources, or both.

[0113] In some examples, the reference signal transmitter component may be configured to or otherwise support means for transmitting an indication of reference signal received power associated with the first set of channel measurement resources in the same signal as a subset of the set of multiple reference signals.

[0114] In some examples, the one or more first channel characteristics include one or more reference signal received power values ​​for a subset of the set of multiple reference signals.

[0115] In some examples, the receive beam determination component may be configured as or otherwise support a device for determining a spatial filter corresponding to a receive beam based on one or more sounding reference signal resource identifiers corresponding to a second set of channel measurement resources, wherein the one or more channel measurement resource identifiers include one or more sounding reference signal resource identifiers.

[0116] In some examples, the control signaling receiver component may be configured to or otherwise support means for receiving control signaling that indicates to the UE that a set of multiple channel characteristics associated with the first set of channel measurement resources is to be sent together with the set of multiple reference signals. In some examples, the reference signal transmitter component may be configured to or otherwise support means for sending the set of multiple channel characteristics in the same signal as the set of multiple reference signals, the set of multiple channel characteristics including one or more of reference signal received power, signal to interference and noise ratio, rank indicator, channel quality indicator, or precoding matrix indicator.

[0117] In some examples, a capability message transmitter component may be configured as or otherwise support an apparatus for sending a message indicating that the UE supports the ability to report information associated with a first set of channel measurement resources, a second set of channel measurement resources, or a receive direction for communication, wherein sending the set of multiple reference signals indicating the information is based on the capability.

[0118] In some examples, the reference signal transmitter component may be configured or otherwise support means for transmitting the set of multiple reference signals according to a periodicity. In some examples, the signal receiver component may be configured or otherwise support means for receiving additional signaling indicating an update to the machine learning model based on transmitting the set of multiple reference signals according to a periodicity.

[0119] In some examples, the first channel state report transmitter component may be configured or otherwise support means for sending a first channel state information report having a first priority. In some examples, the second channel state report transmitter component may be configured or otherwise support means for sending a second channel state information report having a second priority, the second channel state information report including information, wherein the first priority is greater than the second priority.

[0120] In some examples, a first angular spread of a first reference signal in the set of multiple reference signals associated with the second set of channel measurement resources is less than a second angular spread of a second reference signal in the set of multiple reference signals associated with the first set of channel measurement resources.

[0121] In some examples, the set of multiple reference signals includes a channel state information reference signal, a synchronization signal block, or both.

[0122] In some examples, channel characteristics associated with the first set of channel measurement resources predict time domain channel characteristics for the set of multiple reference signals, spatial domain channel characteristics for the set of multiple reference signals, or both.

[0123] Additionally or alternatively, according to examples as disclosed herein, the communication manager may support wireless communications at the UE. The channel measurement resource transmitter component may be configured as or otherwise support a device for sending information associated with one or more channel measurement resources and a receive direction for communication. In some examples, the machine learning model receiver component may be configured as or otherwise support a device for receiving signaling indicating a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources based on sending the information. In some examples, the machine learning model input component may be configured as or otherwise support a device for inputting input to the machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources. In some examples, the signal receiver component may be configured as or otherwise support a device for receiving signaling based on obtaining channel characteristic predictions associated with one or more channel measurement resources.

[0124] In some examples, the machine learning model input component may be configured or otherwise support means for inputting one or more channel characteristics, transmitted information, or both associated with one or more channel measurement resources to the machine learning model. In some examples, the prediction acquisition component may be configured or otherwise support means for acquiring channel characteristic predictions, transmitted information, or both associated with one or more channel measurement resources based on the input.

[0125] In some examples, the capability message sender component may be configured to or otherwise support means for sending a message indicating that the UE supports the capability to send information, where the sending information is based on the capability.

[0126] In some examples, the channel measurement resource transmitter component may be configured or otherwise support means for sending information according to a periodicity. In some examples, the signal receiver component may be configured or otherwise support means for receiving additional signaling indicating updates to the machine learning model based on sending the information according to a periodicity.

[0127] In some examples, the first channel state report transmitter component may be configured or otherwise support means for sending a first channel state information report having a first priority. In some examples, the second channel state report transmitter component may be configured or otherwise support means for sending a second channel state information report having a second priority, the second channel state information report including information, wherein the first priority is greater than the second priority.

[0128] In some examples, the information includes a phase coefficient associated with the RF chain, an amplitude coefficient associated with the RF chain, a phase coefficient associated with a phase shifter, an amplitude associated with a phase shifter, an antenna panel identifier associated with a receive direction, an antenna panel orientation associated with a receive direction, a target angle of arrival for the communication, or a zenith angle of arrival for the communication.

[0129] Figure 8A system 800 including a device that supports predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The device can be an example of a device, a device, or a UE 115 as described herein, or a component thereof. The device can communicate with one or more network entities 105, one or more UEs 115, or any combination thereof (e.g., wirelessly). The device may include components for two-way voice and data communications, including components for sending and receiving communications, such as a communication manager, an input / output (I / O) controller, a transceiver, an antenna, a memory, code, and a processor. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus).

[0130] An I / O controller can manage input signals and output signals for a device. An I / O controller can also manage peripheral devices that are not integrated into a device. In some cases, an I / O controller can represent a physical connection or port to an external peripheral device. In some cases, an I / O controller can utilize operating system functions such as Or another known operating system. Additionally or alternatively, the I / O controller may represent or interact with a modem, keyboard, mouse, touch screen, or similar device. In some cases, the I / O controller may be implemented as part of a processor (such as a processor). In some cases, a user may interact with the device via the I / O controller or via hardware components controlled by the I / O controller.

[0131] In some cases, the device may include a single antenna. However, in some other cases, the device may have more than one antenna, which can send or receive multiple wireless transmissions concurrently. The transceiver can communicate bidirectionally via one or more antennas, wired links, or wireless links as described herein. For example, a transceiver may represent a wireless transceiver and can communicate bidirectionally with another wireless transceiver. The transceiver may also include a modem for modulating packets, providing the modulated packets to one or more antennas for transmission, and demodulating packets received from one or more antennas. A transceiver or a transceiver and one or more antennas can be an example of a transmitter, a transmitter, a receiver, a receiver, or any combination or component thereof as described herein.

[0132] The memory may include random access memory (RAM) and read-only memory (ROM). The memory may store computer-readable, computer-executable code including instructions that, when executed by the processor, cause the device to perform the various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, the code may not be directly executable by the processor, but may cause the computer (e.g., when compiled and executed) to perform the functions described herein. In some cases, among other things, the memory may also include a basic I / O system (BIOS), which may control basic hardware or software operations, such as interaction with peripheral components or devices.

[0133] The processor may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated into the processor. The processor may be configured to execute computer-readable instructions stored in a memory (e.g., a memory) to enable the device to perform various functions (e.g., functions or tasks that support predictive resource management using user equipment information in a machine learning model). For example, a device or a component of a device may include a processor and a memory coupled to or coupled to the processor, the processor and the memory being configured to perform the various functions described herein.

[0134] According to an example as disclosed herein, a communication manager may support wireless communications at a UE. For example, the communication manager may be configured to or otherwise support a device for sending a set of multiple reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for communication. The communication manager may be configured to or otherwise support a device for receiving, based on sending the set of multiple reference signals, a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources, the machine learning model being based on the receive beam at the UE. The communication manager may be configured to or otherwise support a device for inputting an input to the machine learning model for obtaining a channel characteristic prediction. The communication manager may be configured to or otherwise support a device for receiving signaling based on obtaining a channel characteristic prediction associated with the first set of channel measurement resources.

[0135] Additionally or alternatively, according to examples as disclosed herein, the communication manager may support wireless communications at the UE. For example, the communication manager may be configured to or otherwise support a device for sending information associated with one or more channel measurement resources and a receive direction for communication. The communication manager may be configured to or otherwise support a device for receiving, based on sending the information, a signaling indicating a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources. The communication manager may be configured to or otherwise support a device for inputting input to a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources. The communication manager may be configured to or otherwise support a device for receiving signaling based on obtaining channel characteristic predictions associated with one or more channel measurement resources.

[0136] By including or configuring a communication manager according to examples as described herein, a device may support techniques for improving communication reliability, more efficiently utilizing communication resources, and improving coordination between devices.

[0137] In some examples, the communication manager may be configured to perform various operations (e.g., receive, monitor, transmit) using or otherwise cooperating with a transceiver, one or more antennas, or any combination thereof. Although the communication manager is illustrated as a separate component, in some examples, one or more functions described with reference to the communication manager may be supported or performed by a processor, memory, code, or any combination thereof. For example, the code may include instructions that may be executed by a processor to cause the device to perform various aspects of predictive resource management using user equipment information in a machine learning model as described herein, or the processor and memory may be otherwise configured to perform or support such operations.

[0138] Fig. 9 An apparatus for predictive resource management using user equipment information in a machine learning model in accordance with one or more aspects of the present disclosure is shown. The apparatus may be an example of aspects of a network entity 105 as described herein. The apparatus may include a receiver, a transmitter, and a communication manager. The communication manager may be implemented at least in part by one or both of a modem and a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0139] The receiver may provide means for acquiring (e.g., receiving, determining, identifying) information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). The information may be passed to other components of the device. In some examples, the receiver may support acquiring information by receiving signals via one or more antennas. Additionally or alternatively, the receiver may support acquiring information by receiving signals via one or more wired interfaces (e.g., electrical interfaces, optical fiber interfaces), wireless interfaces, or any combination thereof.

[0140] The transmitter may provide a device for outputting (e.g., sending, providing, communicating, transmitting) information generated by other components of the device. For example, the transmitter may output information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). In some examples, the transmitter may support outputting information by sending signals via one or more antennas. Additionally or alternatively, the transmitter may support outputting information by sending signals via one or more wired interfaces (e.g., electrical interfaces, optical fiber interfaces), wireless interfaces, or any combination thereof. In some examples, the transmitter and the receiver may be co-located in a transceiver, which may include a modem or be coupled to a modem.

[0141] The communication manager, receiver, transmitter, or various combinations thereof or various components thereof may be examples of means for performing various aspects of predictive resource management using user equipment information in a machine learning model as described herein. For example, the communication manager, receiver, transmitter, or various combinations thereof or components thereof may support methods for performing one or more of the functions described herein.

[0142] In some examples, the communication manager, receiver, transmitter, or various combinations or components thereof may be implemented in hardware (e.g., in a communication management circuit). The hardware may include a processor, DSP, CPU, ASIC, FPGA or other programmable logic device, microcontroller, discrete gate or transistor logic, discrete hardware components, or any combination thereof configured to or otherwise support means for performing the functions described herein. In some examples, a processor and a memory coupled to the processor may be configured to perform one or more of the functions described herein (e.g., by executing instructions stored in the memory by the processor).

[0143] Additionally or alternatively, in some examples, the communication manager, receiver, transmitter, or various combinations or components thereof may be implemented in code executed by a processor (e.g., implemented as communication management software or firmware). If implemented in code executed by a processor, the functionality of the communication manager, receiver, transmitter, or various combinations or components thereof may be performed by a general purpose processor, DSP, CPU, ASIC, FPGA, or any combination of these or other programmable logic devices (e.g., configured as or otherwise supporting means for performing the functionality described in the present disclosure).

[0144] In some examples, the communication manager can be configured to perform various operations (e.g., receive, acquire, monitor, output, send) using or otherwise cooperating with the receiver, the transmitter, or both. For example, the communication manager can receive information from the receiver, transmit information to the transmitter, or integrate with the receiver, the transmitter, or both to acquire information, output information, or perform various other operations as described herein.

[0145] According to an example as disclosed herein, a communication manager may support wireless communications at a network entity. For example, the communication manager may be configured to or otherwise support a device for receiving a set of multiple reference signals, the set of multiple reference signals indicating information corresponding to one or more channel characteristics associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receiving direction for communication, the information corresponding to a receiving beam at the UE corresponding to the receiving direction for communication. The communication manager may be configured to or otherwise support a device for training a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources based on inputting one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, and a receiving direction for communication to the machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources, the one or more channel characteristics being based on the receiving beam at the UE. The communication manager may be configured to or otherwise support a device for sending signaling indicative of a machine learning model based on training the machine learning model.

[0146] Additionally or alternatively, according to examples as disclosed herein, the communication manager may support wireless communications at a network entity. For example, the communication manager may be configured to or otherwise support means for receiving, at a UE, information associated with one or more channel measurement resources and a receive direction for communication. The communication manager may be configured to or otherwise support means for training a machine learning model for obtaining channel characteristic predictions based on inputting one or more channel characteristics and the information to the machine learning model for obtaining channel characteristic predictions. The communication manager may be configured to or otherwise support means for sending signaling indicative of a machine learning model based on training the machine learning model.

[0147] By including or configuring a communication manager according to examples as described herein, a device (e.g., a processor controlling a receiver, a transmitter, a communication manager, or a combination thereof or otherwise coupled thereto) can support techniques for more efficiently utilizing communication resources.

[0148] Fig.10 An apparatus for predictive resource management using user equipment information in a machine learning model in accordance with one or more aspects of the present disclosure is shown. The apparatus may be an example of aspects of a device or network entity 105 as described herein. The apparatus may include a receiver, a transmitter, and a communication manager. The communication manager may be implemented at least in part by one or both of a modem and a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0149] The receiver may provide means for acquiring (e.g., receiving, determining, identifying) information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). The information may be passed to other components of the device. In some examples, the receiver may support acquiring information by receiving signals via one or more antennas. Additionally or alternatively, the receiver may support acquiring information by receiving signals via one or more wired interfaces (e.g., electrical interfaces, optical fiber interfaces), wireless interfaces, or any combination thereof.

[0150] The transmitter may provide a device for outputting (e.g., sending, providing, communicating, transmitting) information generated by other components of the device. For example, the transmitter may output information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). In some examples, the transmitter may support outputting information by sending signals via one or more antennas. Additionally or alternatively, the transmitter may support outputting information by sending signals via one or more wired interfaces (e.g., electrical interfaces, optical fiber interfaces), wireless interfaces, or any combination thereof. In some examples, the transmitter and the receiver may be co-located in a transceiver, which may include a modem or be coupled to a modem.

[0151] The device or its various components may be examples of apparatus for performing various aspects of predictive resource management using user equipment information in a machine learning model as described herein. For example, a communication manager may include a reference signal receiver component, a machine learning model training component, a machine learning model transmitter component, a channel measurement resource receiver component, or any combination thereof. The communication manager may be an example of various aspects of a communication manager as described herein. In some examples, the communication manager or its various components may be configured to perform various operations (e.g., receive, acquire, monitor, output, send) using or otherwise in conjunction with a receiver, a transmitter, or both. For example, the communication manager may receive information from a receiver, transmit information to a transmitter, or integrate in combination with a receiver, a transmitter, or both to acquire information, output information, or perform various other operations as described herein.

[0152] According to an example as disclosed herein, a communication manager may support wireless communications at a network entity. A reference signal receiver component may be configured to or otherwise support a device for receiving a set of multiple reference signals, the set of multiple reference signals indicating information corresponding to one or more channel characteristics associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receiving direction for communication, the information corresponding to a receiving beam at a UE corresponding to the receiving direction for communication. A machine learning model training component may be configured to or otherwise support a device for training a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources based on inputting one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, and a receiving direction for communication to the machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources, the one or more channel characteristics being based on a receiving beam at the UE. A machine learning model transmitter component may be configured to or otherwise support a device for sending signaling indicative of a machine learning model based on training the machine learning model.

[0153] Additionally or alternatively, according to examples as disclosed herein, the communication manager may support wireless communications at a network entity. The channel measurement resource receiver component may be configured as or otherwise support means for receiving information associated with one or more channel measurement resources and a receive direction for communication at a UE. The machine learning model training component may be configured as or otherwise support means for training a machine learning model for obtaining channel characteristic predictions based on inputting one or more channel characteristics and the information to the machine learning model for obtaining channel characteristic predictions. The machine learning model transmitter component may be configured as or otherwise support means for sending signaling indicative of a machine learning model based on training the machine learning model.

[0154] Fig.11A communication manager supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The communication manager may be an example of a communication manager, a communication manager, or aspects of both as described herein. The communication manager or its various components may be examples of devices for performing various aspects of predictive resource management using user equipment information in a machine learning model as described herein. For example, the communication manager may include a reference signal receiver component, a machine learning model training component, a machine learning model transmitter component, a channel measurement resource receiver component, a resource identifier transmitter component, a receive beam determination component, a control signaling transmitter, a capability message receiver component, a first channel state report receiver component, a second channel state report transmitter component, a machine learning model input component, a loss function calculator component, or any combination thereof. Each of these components may communicate directly or indirectly with each other (e.g., via one or more buses), which communication may include communication within a protocol layer of a protocol stack, communication associated with a logical channel of a protocol stack (e.g., between protocol layers of a protocol stack, within a device, component, or virtualization component associated with a network entity 105, between devices, components, or virtualization components associated with a network entity 105), or any combination thereof.

[0155] According to an example as disclosed herein, a communication manager may support wireless communications at a network entity. A reference signal receiver component may be configured to or otherwise support a device for receiving a set of multiple reference signals, the set of multiple reference signals indicating information corresponding to one or more channel characteristics associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receiving direction for communication, the information corresponding to a receiving beam at a UE corresponding to the receiving direction for communication. A machine learning model training component may be configured to or otherwise support a device for training a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources based on inputting one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, and a receiving direction for communication to the machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources, the one or more channel characteristics being based on a receiving beam at the UE. A machine learning model transmitter component may be configured to or otherwise support a device for sending signaling indicative of a machine learning model based on training the machine learning model.

[0156] In some examples, the resource identifier transmitter component may be configured to or otherwise support means for receiving one or more channel measurement resource identifiers associated with the second set of channel measurement resources in the same signal as a subset of the set of multiple reference signals associated with the second set of channel measurement resources. In some examples, the receive beam determination component may be configured to or otherwise support means for determining a receive beam at the UE for measuring the first set of channel measurement resources based on the one or more channel measurement resource identifiers.

[0157] In some examples, the reference signal receiver component may be configured as or otherwise support a device for receiving the set of multiple reference signals according to a periodicity. In some examples, the machine learning model input component may be configured as or otherwise support a device for inputting one or more channel characteristics and one or more channel measurement resource identifiers to the machine learning model according to a periodicity to obtain a channel characteristic prediction, wherein the channel characteristic prediction includes a reference signal received power associated with a first set of channel measurement resources, a channel measurement resource identifier associated with a second set of channel measurement resources, or both. In some examples, the machine learning model transmitter component may be configured as or otherwise support a device for sending additional signaling indicating an updated machine learning model based on the input of one or more channel characteristics and one or more channel measurement resource identifiers according to a periodicity.

[0158] In some examples, the loss function calculator component may be configured to or otherwise support a device for calculating a loss function for a machine learning model using a reference signal received power associated with a first set of channel measurement resources, a channel measurement resource identifier associated with a second set of channel measurement resources, or both.

[0159] In some examples, the reference signal receiver component may be configured to or otherwise support means for receiving an indication of reference signal received power associated with the first set of channel measurement resources in the same signal as a subset of the set of multiple reference signals.

[0160] In some examples, the receive beam determination component may be configured as or otherwise support a device for determining a spatial filter corresponding to a receive beam based on one or more sounding reference signal resource identifiers corresponding to a second set of channel measurement resources, wherein the one or more channel measurement resource identifiers include one or more sounding reference signal resource identifiers.

[0161] In some examples, the control signaling transmitter may be configured to or otherwise support a device for sending control signaling that indicates to the UE that a set of multiple channel characteristics associated with the first set of channel measurement resources is to be sent together with the set of multiple reference signals. In some examples, the reference signal receiver component may be configured to or otherwise support a device for receiving the set of multiple channel characteristics in the same signal as the set of multiple reference signals, the set of multiple channel characteristics including one or more of reference signal received power, signal to interference and noise ratio, rank indicator, channel quality indicator, or precoding matrix indicator.

[0162] In some examples, a capability message receiver component may be configured as or otherwise support a device for receiving a message indicating that the UE supports the ability to report information corresponding to one or more channel characteristics associated with a first set of channel measurement resources, a second set of channel measurement resources, or a receive direction for communication, wherein receiving the set of multiple reference signals indicating the information is based on the capability.

[0163] In some examples, the first channel state report receiver component may be configured or otherwise support means for receiving a first channel state information report having a first priority. In some examples, the second channel state report transmitter component may be configured or otherwise support means for receiving a second channel state information report having a second priority, the second channel state information report including the set of multiple reference signals, wherein the first priority is greater than the second priority.

[0164] In some examples, a first angular spread of a first reference signal in the set of multiple reference signals associated with the second set of channel measurement resources is less than a second angular spread of a second reference signal in the set of multiple reference signals associated with the first set of channel measurement resources.

[0165] In some examples, the set of multiple reference signals includes a channel state information reference signal, a synchronization signal block, or both.

[0166] In some examples, one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, and the receive direction for communication include one or more of: time domain channel characteristics of the set of multiple reference signals, spatial domain channel characteristics of the set of multiple reference signals, or both.

[0167] Additionally or alternatively, according to examples as disclosed herein, the communication manager may support wireless communications at a network entity. The channel measurement resource receiver component may be configured as or otherwise support a device for receiving information associated with one or more channel measurement resources and a receive direction for communication at a UE. In some examples, the machine learning model training component may be configured as or otherwise support a device for training a machine learning model to obtain channel characteristic predictions based on inputting one or more channel characteristics and the information into the machine learning model for obtaining channel characteristic predictions. In some examples, the machine learning model transmitter component may be configured as or otherwise support a device for sending signaling indicative of a machine learning model based on training the machine learning model.

[0168] In some examples, the channel measurement resource receiver component may be configured as or otherwise support means for receiving information according to a periodicity. In some examples, the machine learning model input component may be configured as or otherwise support means for inputting one or more channel characteristics and the information to the machine learning model according to a periodicity to obtain a channel characteristic prediction, wherein the channel characteristic prediction includes a reference signal received power associated with one or more channel measurement resources, a channel measurement resource identifier, or both. In some examples, the machine learning model training component may be configured as or otherwise support means for sending additional signaling indicating an updated machine learning model based on the input of one or more channel characteristics and the information to the machine learning model according to a periodicity.

[0169] In some examples, the capability message receiver component may be configured as or otherwise support means for receiving a message indicating that the UE supports the capability to send information, where the received information is based on the capability.

[0170] In some examples, the first channel state report receiver component may be configured or otherwise support means for receiving a first channel state information report having a first priority. In some examples, the second channel state report transmitter component may be configured or otherwise support means for receiving a second channel state information report having a second priority, the second channel state information report including information, wherein the first priority is greater than the second priority.

[0171] In some examples, the information includes a phase coefficient associated with the RF chain, an amplitude coefficient associated with the RF chain, a phase coefficient associated with a phase shifter, an amplitude associated with a phase shifter, an antenna panel identifier associated with a receive direction, an antenna panel orientation associated with a receive direction, a target angle of arrival for the communication, or a zenith angle of arrival for the communication.

[0172] Fig.12A diagram of a system 1200 including a device supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The device can be an example of a device, a device, or a network entity 105 as described herein, or a component thereof. The device can communicate with one or more network entities 105, one or more UEs 115, or any combination thereof, and the communication may include communication through one or more wired interfaces, through one or more wireless interfaces, or any combination thereof. The device may include components that support output and acquisition of communications, such as a communication manager, a transceiver, an antenna, a memory, code, and a processor. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus).

[0173] The transceiver may support bidirectional communication via a wired link, a wireless link, or both as described herein. In some examples, the transceiver may include a wired transceiver and may communicate bidirectionally with another wired transceiver. Additionally or alternatively, in some examples, the transceiver may include a wireless transceiver and may communicate bidirectionally with another wireless transceiver. In some examples, the device may include one or more antennas capable of (e.g., concurrently) sending or receiving wireless transmissions. The transceiver may also include a modem for modulating a signal, providing a modulated signal for transmission (e.g., through one or more antennas, through a wired transmitter), receiving a modulated signal (e.g., from one or more antennas, from a wired receiver), and demodulating a signal. A transceiver or a transceiver and one or more antennas or a wired interface may be an example of a transmitter, a transmitter, a receiver, a receiver, or any combination or component thereof as described herein, where applicable. In some examples, the transceiver may be operable to support communications via one or more communication links (eg, communication link 125, backhaul communication link 120, midhaul communication link 162, fronthaul communication link 168).

[0174] The memory may include RAM and ROM. The memory may store computer-readable, computer-executable code including instructions that, when executed by the processor, cause the device to perform the various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, the code may not be directly executable by the processor, but may cause the computer (e.g., when compiled and executed) to perform the functions described herein. In some cases, the memory may also include, among other things, a BIOS that may control basic hardware or software operations, such as interaction with peripheral components or devices.

[0175] The processor may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, an ASIC, a CPU, an FPGA, a microcontroller, a programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or any combination thereof). In some cases, the processor may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated into the processor. The processor may be configured to execute computer-readable instructions stored in a memory (e.g., a memory) so that the device performs various functions (e.g., functions or tasks that support predictive resource management using user equipment information in a machine learning model). For example, a device or a component of a device may include a processor and a memory coupled to the processor, the processor and the memory being configured to perform the various functions described herein. The processor may be an example of a cloud computing platform (e.g., one or more physical nodes and supporting software such as an operating system, a virtual machine, or a container instance), which may host functions for performing functions of the device (e.g., by executing code).

[0176] In some examples, the bus may support communications within a protocol layer (e.g., within a protocol layer) of a protocol stack. In some examples, the bus may support communications associated with a logical channel of a protocol stack (e.g., between protocol layers of a protocol stack), which may include communications performed within a component of a device, or communications performed between different components of a device that may be co-located or located in different locations (e.g., where a device may refer to a system in which one or more of a communication manager, a transceiver, a memory, a code, and a processor may be located in one of these different components or divided between different components).

[0177] In some examples, the communication manager may manage aspects of communications with the core network 130 (e.g., via one or more wired or wireless backhaul links). For example, the communication manager may manage the delivery of data communications for client devices such as one or more UEs 115. In some examples, the communication manager may manage communications with other network entities 105 and may include a controller or scheduler for controlling communications with UEs 115 in coordination with other network entities 105. In some examples, the communication manager may support an X2 interface within an LTE / LTE-A wireless communication network technology to provide communications between network entities 105.

[0178] According to an example as disclosed herein, a communication manager may support wireless communications at a network entity. For example, the communication manager may be configured to or otherwise support a device for receiving a set of multiple reference signals, the set of multiple reference signals indicating information corresponding to one or more channel characteristics associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receiving direction for communication, the information corresponding to a receiving beam at the UE corresponding to the receiving direction for communication. The communication manager may be configured to or otherwise support a device for training a machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources based on inputting one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, and a receiving direction for communication to the machine learning model for obtaining a channel characteristic prediction associated with the first set of channel measurement resources, the one or more channel characteristics being based on the receiving beam at the UE. The communication manager may be configured to or otherwise support a device for sending signaling indicative of a machine learning model based on training the machine learning model.

[0179] Additionally or alternatively, according to examples as disclosed herein, the communication manager may support wireless communications at a network entity. For example, the communication manager may be configured to or otherwise support means for receiving, at a UE, information associated with one or more channel measurement resources and a receive direction for communication. The communication manager may be configured to or otherwise support means for training a machine learning model for obtaining channel characteristic predictions based on inputting one or more channel characteristics and the information to the machine learning model for obtaining channel characteristic predictions. The communication manager may be configured to or otherwise support means for sending signaling indicative of a machine learning model based on training the machine learning model.

[0180] By including or configuring a communication manager according to examples as described herein, a device may support techniques for improving communication reliability, more efficiently utilizing communication resources, and improving coordination between devices.

[0181] In some examples, the communication manager may be configured to perform various operations (e.g., receive, acquire, monitor, output, transmit) using or otherwise cooperating with a transceiver, one or more antennas (e.g., where applicable), or any combination thereof. Although the communication manager is illustrated as a separate component, in some examples, one or more functions described with reference to the communication manager may be supported or performed by a processor, memory, code, transceiver, or any combination thereof. For example, the code may include instructions that may be executed by a processor to cause the device to perform various aspects of predictive resource management using user equipment information in a machine learning model as described herein, or the processor and memory may be otherwise configured to perform or support such operations.

[0182] Fig.13 A flow chart illustrating a method 1300 for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 1300 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1300 may be implemented by a UE or a component thereof as described in reference to Figures 1 to 8 The UE 115 described above may be executed. In some examples, the UE may execute an instruction set to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0183] At 1305, the method may include sending a set of multiple reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for communication. The operations of 1305 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1305 may be performed by Figure 7 The reference signal transmitter component is used to perform the above operation.

[0184] At 1310, the method may include, based on sending the set of multiple reference signals, receiving signaling indicating a machine learning model for obtaining channel characteristic predictions associated with the first set of channel measurement resources, the machine learning model being based on receive beams at the UE. The operations of 1310 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1310 may be performed by Figure 7 The machine learning model receiver component is used to execute.

[0185] At 1315, the method may include inputting an input to the machine learning model for obtaining a channel characteristic prediction. The operations of 1315 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1315 may be performed as described in reference Figure 7 The machine learning model input component is executed.

[0186] At 1320, the method may include receiving signaling based on obtaining a channel characteristic prediction associated with the first set of channel measurement resources. The operations of 1320 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1320 may be performed as described in reference to Figure 7 The signal receiver component is used to perform the above operation.

[0187] Fig.14A flow chart illustrating a method 1400 for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 1400 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1400 may be implemented by a UE or a component thereof as described in reference to Figures 1 to 8 The UE 115 described above may be executed. In some examples, the UE may execute an instruction set to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0188] At 1405, the method may include sending a set of multiple reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for communication. The operations of 1405 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1405 may be performed by Figure 7 The reference signal transmitter component is used to perform the above operation.

[0189] At 1410, the method may include determining a receive beam corresponding to a receive direction for communication for the UE based on one or more channel measurement resource identifiers corresponding to a second set of channel measurement resources and one or more first channel characteristics associated with the second set of channel measurement resources, wherein the information includes the one or more first channel characteristics. The operations of 1410 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1410 may be performed as described in reference to Figure 7 The receiving beam determination component is used to perform.

[0190] At 1415, the method may include sending one or more channel measurement resource identifiers in the same signal as a subset of the set of multiple reference signals associated with the second set of channel measurement resources, wherein receiving the signaling is based on sending the one or more channel measurement resource identifiers. The operations of 1415 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1415 may be performed by Figure 7 The resource identifier transmitter component is executed.

[0191] At 1420, the method may include receiving signaling indicating a machine learning model for obtaining channel characteristic predictions associated with the first set of channel measurement resources based on sending the set of multiple reference signals, the machine learning model being based on receive beams at the UE. The operations of 1420 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1420 may be performed by Figure 7 The machine learning model receiver component is used to execute.

[0192] At 1425, the method may include inputting an input to the machine learning model for obtaining a channel characteristic prediction. The operations of 1425 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1425 may be performed as described in reference Figure 7 The machine learning model input component is executed.

[0193] At 1430, the method may include receiving signaling based on obtaining a channel characteristic prediction associated with the first set of channel measurement resources. The operations of 1430 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1430 may be performed as described in reference to Figure 7 The signal receiver component is used to perform the above operation.

[0194] Fig.15 A flow chart illustrating a method 1500 for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 1500 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 1500 may be implemented by a network entity or a component thereof as described herein. Figures 1 to 4 as well as Figures 9 to 12 In some examples, the network entity may execute an instruction set to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0195] At 1505, the method may include receiving a set of multiple reference signals indicating information corresponding to one or more channel characteristics associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for communication. The operations of 1505 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1505 may be performed by Fig.11 The reference signal receiver component is used to perform.

[0196] At 1510, the method may include training a machine learning model for obtaining channel characteristic predictions associated with the first set of channel measurement resources based on inputting one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, and a receive direction for communication to the machine learning model for obtaining channel characteristic predictions associated with the first set of channel measurement resources, the one or more channel characteristics being based on a receive beam at the UE. The operations of 1510 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1510 may be performed as described in reference to Fig.11 The machine learning model training component is used to perform the above operation.

[0197] At 1515, the method may include sending a signal indicating the machine learning model based on training the machine learning model. The operations of 1515 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1515 may be performed as described in reference to Fig.11 The machine learning model transmitter component is used to perform the above operation.

[0198] Fig.16 A flow chart illustrating a method 1600 for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 1600 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 1600 may be implemented by a network entity or a component thereof as described herein. Figures 1 to 4 as well as Figures 9 to 12 In some examples, the network entity may execute an instruction set to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0199] At 1605, the method may include receiving a set of multiple reference signals indicating information corresponding to one or more channel characteristics associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for communication. The operations of 1605 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1605 may be performed by Fig.11 The reference signal receiver component is used to perform.

[0200] At 1610, the method may include receiving one or more channel measurement resource identifiers associated with a second set of channel measurement resources in the same signal as a subset of the set of multiple reference signals associated with the second set of channel measurement resources. The operations of 1610 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1610 may be performed by Fig.11 The resource identifier transmitter component is executed.

[0201] At 1615, the method may include determining a receive beam at the UE for measuring a first set of channel measurement resources based on one or more channel measurement resource identifiers. The operations of 1615 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1615 may be performed as described in reference to Fig.11 The receiving beam determination component is used to perform.

[0202] At 1620, the method may include training a machine learning model for obtaining channel characteristic predictions associated with the first set of channel measurement resources based on inputting one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, and a receive direction for communication to the machine learning model for obtaining channel characteristic predictions associated with the first set of channel measurement resources, the one or more channel characteristics being based on a receive beam at the UE. The operations of 1620 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1620 may be performed as described in reference to Fig.11 The machine learning model training component is used to perform the above operation.

[0203] At 1625, the method may include sending a signal indicating the machine learning model based on training the machine learning model. The operations of 1625 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1625 may be performed as described in reference to Fig.11 The machine learning model transmitter component is used to perform the above operation.

[0204] Fig.17 A flow chart illustrating a method 1700 for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 1700 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1700 may be implemented by a UE or a component thereof as described in reference to Figures 1 to 8 The UE 115 described above may be executed. In some examples, the UE may execute an instruction set to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0205] At 1705, the method may include sending information associated with one or more channel measurement resources and a receive direction for communication. The operations of 1705 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1705 may be performed as described in reference to Figure 7 The channel measurement resource transmitter component is used to perform.

[0206] At 1710, the method may include receiving signaling indicating a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources based on sending the information. The operations of 1710 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1710 may be performed as described in reference to Figure 7 The machine learning model receiver component is used to execute.

[0207] At 1715, the method may include inputting an input to a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources. The operations of 1715 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1715 may be performed as described in reference to Figure 7 The machine learning model input component is executed.

[0208] At 1720, the method may include receiving signaling based on obtaining a channel characteristic prediction associated with one or more channel measurement resources. The operations of 1720 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1720 may be performed as described in reference to Figure 7 The signal receiver component is used to perform the above operation.

[0209] Fig.18 A flow chart illustrating a method 1800 for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 1800 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1800 may be implemented by a UE or a component thereof as described in reference to Figures 1 to 8 The UE 115 described above may be executed. In some examples, the UE may execute an instruction set to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0210] At 1805, the method may include sending information associated with one or more channel measurement resources and a receive direction for communication. The operations of 1805 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1805 may be performed as described in reference to Figure 7 The channel measurement resource transmitter component is used to perform.

[0211] At 1810, the method may include receiving signaling indicating a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources based on sending the information. The operations of 1810 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1810 may be performed as described in reference to Figure 7 The machine learning model receiver component is used to execute.

[0212] At 1815, the method may include inputting an input to a machine learning model for obtaining channel characteristic predictions associated with one or more channel measurement resources. The operations of 1815 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1815 may be performed as described in reference to Figure 7 The machine learning model input component is executed.

[0213] At 1820, the method may include inputting one or more channel characteristics associated with one or more channel measurement resources, the transmitted information, or both into the machine learning model. The operations of 1820 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1820 may be performed as described in reference to Figure 7 The machine learning model input component is executed.

[0214] At 1825, the method may include obtaining channel characteristic predictions, transmitted information, or both associated with one or more channel measurement resources based on the input. The operations of 1825 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1825 may be performed as described in reference to Figure 7 The prediction acquisition component is used to perform the above operation.

[0215] At 1830, the method may include receiving signaling based on obtaining a channel characteristic prediction associated with one or more channel measurement resources. The operations of 1830 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1830 may be performed as described in reference to Figure 7 The signal receiver component is used to perform the above operation.

[0216] Fig.19 A flowchart illustrating a method 1900 for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 1900 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 1900 may be implemented by a network entity or a component thereof as described herein. Figures 1 to 4 as well as Figures 9 to 12 In some examples, the network entity may execute an instruction set to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0217] At 1905, the method may include receiving, at the UE, information associated with one or more channel measurement resources and a receive direction for communication. The operations of 1905 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1905 may be performed as described in reference to Fig.11 The channel measurement resource receiver component is used to perform.

[0218] At 1910, the method may include training a machine learning model to obtain channel characteristic predictions based on inputting one or more channel characteristics and the information into the machine learning model for obtaining channel characteristic predictions. The operations of 1910 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1910 may be performed as described in reference to Fig.11The machine learning model training component is used to perform the above operation.

[0219] At 1915, the method may include sending a signal indicating the machine learning model based on training the machine learning model. The operations of 1915 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1915 may be performed as described in reference to Fig.11 The machine learning model transmitter component is used to perform the above operation.

[0220] Fig. 20 A flow chart illustrating a method 2000 for supporting predictive resource management using user equipment information in a machine learning model according to one or more aspects of the present disclosure is shown. The operations of the method 2000 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 2000 may be implemented by a network entity or a component thereof as described herein. Figures 1 to 4 as well as Figures 9 to 12 In some examples, the network entity may execute an instruction set to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0221] At 2005, the method may include receiving, at the UE, information associated with one or more channel measurement resources and a receive direction for communication. The operations of 2005 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2005 may be performed as described in reference to Fig.11 The channel measurement resource receiver component is used to perform.

[0222] At 2010, the method may include receiving information according to a periodicity. The operations of 2010 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 2010 may be performed as described in reference to Fig.11 The channel measurement resource receiver component is used to perform.

[0223] At 2015, the method may include training a machine learning model to obtain channel characteristic predictions based on inputting one or more channel characteristics and the information into the machine learning model for obtaining channel characteristic predictions. The operations of 2015 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2015 may be performed as described in reference to Fig.11 The machine learning model training component is used to perform the above operation.

[0224] At 2020, the method may include inputting one or more channel characteristics and the information to a machine learning model according to a periodicity to obtain a channel characteristic prediction, wherein the channel characteristic prediction includes a reference signal received power associated with one or more channel measurement resources, a channel measurement resource identifier, or both. The operations of 2020 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2020 may be performed by Fig.11 The machine learning model input component is executed.

[0225] At 2025, the method may include sending a signal indicating the machine learning model based on training the machine learning model. The operations of 2025 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2025 may be performed as described in reference to Fig.11 The machine learning model transmitter component is used to perform the above operation.

[0226] At 2030, the method may include sending additional signaling indicating an updated machine learning model based on periodically inputting one or more channel characteristics and the information to the machine learning model. The operations of 2030 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 2030 may be performed as described in reference to Fig.11 The machine learning model training component is used to perform the above operation.

[0227] The following provides an overview of various aspects of the disclosure:

[0228] Aspect 1: A method for performing wireless communications at a UE, comprising: sending multiple reference signals, the multiple reference signals indicating information associated with a first group of channel measurement resources, a second group of channel measurement resources, and a receiving direction for communication, the information corresponding to a receiving beam at the UE corresponding to the receiving direction for the communication; receiving signaling indicating a machine learning model for obtaining a channel characteristic prediction associated with the first group of channel measurement resources based at least in part on sending the multiple reference signals, the machine learning model being at least in part based on the receiving beam at the UE; inputting inputs for obtaining the channel characteristic predictions into the machine learning model; and receiving signaling based at least in part on obtaining the channel characteristic predictions associated with the first group of channel measurement resources.

[0229] Aspect 2: The method according to Aspect 1 also includes: determining the receiving beam corresponding to the receiving direction for the communication for the UE based at least in part on one or more channel measurement resource identifiers corresponding to the second group of channel measurement resources and one or more first channel characteristics associated with the second group of channel measurement resources, wherein the information includes the one or more first channel characteristics; and sending the one or more channel measurement resource identifiers in a signal that is the same as a subset of the multiple reference signals associated with the second group of channel measurement resources, wherein receiving the signaling is at least in part based on sending the one or more channel measurement resource identifiers.

[0230] Aspect 3: The method according to Aspect 2 also includes: inputting the one or more channel measurement resource identifiers, one or more second channel characteristics associated with the first group of channel measurement resources, or both into the machine learning model based at least in part on sending the multiple reference signals; and obtaining the channel characteristic prediction associated with the first group of channel measurement resources based at least in part on inputting the one or more channel measurement resource identifiers, the one or more second channel characteristics associated with the first group of channel measurement resources, or both.

[0231] Aspect 4: The method according to any one of aspects 2 to 3 further includes: sending an indication of the reference signal received power associated with the first set of channel measurement resources in the same signal as the subset of the plurality of reference signals.

[0232] Aspect 5: The method according to any one of aspects 2 to 4, wherein the one or more first channel characteristics include one or more reference signal received power values ​​for the subset of the plurality of reference signals.

[0233] Aspect 6: The method according to any one of Aspects 2 to 5 further includes: determining the spatial filter corresponding to the received beam based at least in part on one or more sounding reference signal resource identifiers corresponding to the second group of channel measurement resources, wherein the one or more channel measurement resource identifiers include the one or more sounding reference signal resource identifiers.

[0234] Aspect 7: The method according to any one of Aspects 1 to 6 also includes: receiving control signaling, the control signaling indicating to the UE that multiple channel characteristics associated with the first group of channel measurement resources are to be sent together with the multiple reference signals; and sending the multiple channel characteristics in the same signal as the multiple reference signals, the multiple channel characteristics including one or more of reference signal received power, signal-to-interference-plus-noise ratio, rank indicator, channel quality indicator or precoding matrix indicator.

[0235] Aspect 8: The method according to any one of Aspects 1 to 7 further includes: sending a message indicating that the UE supports the ability to report the information associated with the first set of channel measurement resources, the second set of channel measurement resources, or the receiving direction used for the communication, wherein sending the multiple reference signals indicating the information is at least partially based on the capability.

[0236] Aspect 9: The method according to any one of Aspects 1 to 8 further includes: sending the multiple reference signals according to a periodicity; and receiving additional signaling indicating an update to the machine learning model based at least in part on sending the multiple reference signals according to the periodicity.

[0237] Aspect 10: The method according to any one of Aspects 1 to 9 further includes: sending a first channel state information report with a first priority; and sending a second channel state information report with a second priority, wherein the second channel state information report includes the information, wherein the first priority is greater than the second priority.

[0238] Aspect 11: A method according to any one of Aspects 1 to 10, wherein a first angular spread of a first reference signal among the multiple reference signals associated with the second group of channel measurement resources is smaller than a second angular spread of a second reference signal among the multiple reference signals associated with the first group of channel measurement resources.

[0239] Aspect 12: The method according to any one of aspects 1 to 11, wherein the multiple reference signals include channel state information reference signals, synchronization signal blocks, or both.

[0240] Aspect 13: A method according to any one of Aspects 1 to 12, wherein the channel characteristics associated with the first set of channel measurement resources predict time domain channel characteristics for the multiple reference signals, spatial domain channel characteristics for the multiple reference signals, or both.

[0241] Aspect 14: A method for performing wireless communications at a network entity, comprising: receiving multiple reference signals, the multiple reference signals indicating information corresponding to one or more channel characteristics associated with a first group of channel measurement resources, a second group of channel measurement resources, and a receiving direction for communication, the information corresponding to a receiving beam at a UE corresponding to the receiving direction for the communication; training the machine learning model to obtain a channel characteristic prediction associated with the first group of channel measurement resources based at least in part on inputting the one or more channel characteristics associated with the first group of channel measurement resources, the second group of channel measurement resources, and the receiving direction for communication into a machine learning model for obtaining a channel characteristic prediction associated with the first group of channel measurement resources, the one or more channel characteristics being at least partially based on the receiving beam at the UE; and sending signaling indicative of the machine learning model based at least in part on training the machine learning model.

[0242] Aspect 15: The method according to Aspect 14 further includes: receiving one or more channel measurement resource identifiers associated with the second group of channel measurement resources in a signal that is the same as a subset of the multiple reference signals associated with the second group of channel measurement resources; and determining the receiving beam at the UE for measuring the first group of channel measurement resources based at least in part on the one or more channel measurement resource identifiers.

[0243] Aspect 16: The method according to Aspect 15 also includes: receiving the multiple reference signals according to a periodicity; according to the periodicity, inputting the one or more channel characteristics and the one or more channel measurement resource identifiers into the machine learning model to obtain the channel characteristic prediction, wherein the channel characteristic prediction includes the reference signal received power associated with the first group of channel measurement resources, the channel measurement resource identifier associated with the second group of channel measurement resources, or both; and according to the periodicity, sending additional signaling indicating an updated machine learning model based at least in part on the input of the one or more channel characteristics and the one or more channel measurement resource identifiers.

[0244] Aspect 17: The method according to Aspect 16 also includes: using the reference signal received power associated with the first set of channel measurement resources, the channel measurement resource identifier associated with the second set of channel measurement resources, or both to calculate the loss function for the machine learning model.

[0245] Aspect 18: The method according to any one of Aspects 15 to 17 further comprises: receiving an indication of a reference signal received power associated with the first set of channel measurement resources in the same signal as the subset of the plurality of reference signals.

[0246] Aspect 19: The method according to any one of Aspects 15 to 18 further includes: determining a spatial filter corresponding to the receive beam based at least in part on one or more sounding reference signal resource identifiers corresponding to the second group of channel measurement resources, wherein the one or more channel measurement resource identifiers include the one or more sounding reference signal resource identifiers.

[0247] Aspect 20: The method according to any one of Aspects 14 to 19 further includes: sending control signaling, wherein the control signaling indicates to the UE that multiple channel characteristics associated with the first group of channel measurement resources are to be sent together with the multiple reference signals; and receiving the multiple channel characteristics in the same signal as the multiple reference signals, wherein the multiple channel characteristics include one or more of a reference signal received power, a signal-to-interference-plus-noise ratio, a rank indicator, a channel quality indicator, or a precoding matrix indicator.

[0248] Aspect 21: The method according to any one of Aspects 14 to 20 further includes: receiving a message indicating that the UE supports the ability to report the information corresponding to the one or more channel characteristics associated with the first group of channel measurement resources, the second group of channel measurement resources, or the receiving direction used for the communication, wherein receiving the multiple reference signals indicating the information is at least partially based on the capability.

[0249] Aspect 22: The method according to any one of Aspects 14 to 21 further includes: receiving a first channel state information report having a first priority; and receiving a second channel state information report having a second priority, wherein the second channel state information report includes the multiple reference signals, wherein the first priority is greater than the second priority.

[0250] Aspect 23: A method according to any one of Aspects 14 to 22, wherein a first angular spread of a first reference signal among the multiple reference signals associated with the second group of channel measurement resources is smaller than a second angular spread of a second reference signal among the multiple reference signals associated with the first group of channel measurement resources.

[0251] Aspect 24: The method according to any one of aspects 14 to 23, wherein the plurality of reference signals comprises a channel state information reference signal, a synchronization signal block, or both.

[0252] Aspect 25: A method according to any one of Aspects 14 to 24, wherein the one or more channel characteristics associated with the first group of channel measurement resources, the second group of channel measurement resources, and the receiving direction for communication include one or more of the following: time domain channel characteristics of the multiple reference signals, spatial domain channel characteristics of the multiple reference signals, or both.

[0253] Aspect 26: A method for performing wireless communications at a UE, comprising: sending information associated with one or more channel measurement resources and a receiving direction for communication; receiving signaling indicating a machine learning model for obtaining channel characteristic predictions associated with the one or more channel measurement resources based at least in part on sending the information; inputting input into the machine learning model for obtaining the channel characteristic predictions associated with the one or more channel measurement resources; and receiving signaling based at least in part on obtaining the channel characteristic predictions associated with the one or more channel measurement resources.

[0254] Aspect 27: The method according to Aspect 26 also includes: inputting one or more channel characteristics, transmitted information, or both associated with the one or more channel measurement resources into the machine learning model; and obtaining the channel characteristic predictions, transmitted information, or both associated with the one or more channel measurement resources based at least in part on the input.

[0255] Aspect 28: The method according to any one of aspects 26 to 27 further includes: sending a message indicating that the UE supports a capability to send the information, wherein sending the information is based at least in part on the capability.

[0256] Aspect 29: The method according to any one of Aspects 26 to 28 further includes: sending the information according to a periodicity; and receiving additional signaling indicating an update to the machine learning model based at least in part on sending the information according to the periodicity.

[0257] Aspect 30: The method according to any one of Aspects 26 to 29 further includes: sending a first channel state information report having a first priority; and sending a second channel state information report having a second priority, wherein the second channel state information report includes the information, wherein the first priority is greater than the second priority.

[0258] Aspect 31: A method according to any one of Aspects 26 to 30, wherein the information includes a phase coefficient associated with a radio frequency chain, an amplitude coefficient associated with the radio frequency chain, a phase coefficient associated with a phase shifter, an amplitude associated with the phase shifter, an antenna panel identifier associated with the receiving direction, an antenna panel orientation associated with the receiving direction, a target arrival angle for the communication, or an arrival zenith angle for the communication.

[0259] Aspect 32: A method for performing wireless communications at a network entity, comprising: receiving, at a UE, information associated with one or more channel measurement resources and a receiving direction for communication; training a machine learning model for obtaining channel characteristic predictions based at least in part on inputting one or more channel characteristics and the information into the machine learning model; and sending signaling indicative of the machine learning model based at least in part on training the machine learning model.

[0260] Aspect 33: The method according to Aspect 32 further includes: receiving the information according to a periodicity; inputting the one or more channel characteristics and the information into the machine learning model according to the periodicity to obtain the channel characteristic prediction, wherein the channel characteristic prediction includes a reference signal received power associated with the one or more channel measurement resources, a channel measurement resource identifier, or both; and sending additional signaling indicating an updated machine learning model based at least in part on inputting the one or more channel characteristics and the information into the machine learning model according to the periodicity.

[0261] Aspect 34: The method according to any one of aspects 32 to 33 further includes: receiving a message indicating that the UE supports a capability to send the information, wherein receiving the information is at least partially based on the capability.

[0262] Aspect 35: The method according to any one of Aspects 32 to 34 further includes: receiving a first channel state information report having a first priority; and receiving a second channel state information report having a second priority, wherein the second channel state information report includes the information, wherein the first priority is greater than the second priority.

[0263] Aspect 36: A method according to any one of Aspects 32 to 35, wherein the information includes a phase coefficient associated with the RF chain, an amplitude coefficient associated with the RF chain, a phase coefficient associated with a phase shifter, an amplitude associated with the phase shifter, an antenna panel identifier associated with the receiving direction, an antenna panel orientation associated with the receiving direction, a target arrival angle for the communication, or an arrival zenith angle for the communication.

[0264] Aspect 37: An apparatus for performing wireless communications at a UE, 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 a method according to any one of Aspects 1 to 13.

[0265] Aspect 38: An apparatus for wireless communication at a UE, comprising at least one means for performing a method according to any one of aspects 1 to 13.

[0266] Aspect 39: A non-transitory computer-readable medium storing a code for wireless communication at a UE, the code comprising instructions executable by a processor to perform a method according to any one of aspects 1 to 13.

[0267] Aspect 40: An apparatus for wireless communication at a network entity, 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 a method according to any one of Aspects 14 to 25.

[0268] Aspect 41: An apparatus for wireless communication at a network entity, comprising at least one means for performing a method according to any one of aspects 14 to 25.

[0269] Aspect 42: A non-transitory computer-readable medium storing code for wireless communication at a network entity, the code comprising instructions executable by a processor to perform the method according to any one of aspects 14 to 25.

[0270] Aspect 43: An apparatus for wireless communication at a UE, 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 a method according to any one of Aspects 26 to 31.

[0271] Aspect 44: An apparatus for wireless communication at a UE, comprising at least one means for performing a method according to any one of aspects 26 to 31.

[0272] Aspect 45: A non-transitory computer-readable medium storing code for wireless communication at a UE, the code comprising instructions executable by a processor to perform a method according to any one of aspects 26 to 31.

[0273] Aspect 46: An apparatus for wireless communication at a network entity, 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 a method according to any one of Aspects 32 to 36.

[0274] Aspect 47: An apparatus for wireless communication at a network entity, comprising at least one means for performing a method according to any one of aspects 32 to 36.

[0275] Aspect 48: A non-transitory computer-readable medium storing code for wireless communication at a network entity, the code comprising instructions executable by a processor to perform the method according to any one of aspects 32 to 36.

[0276] It should be noted that the methods described herein describe possible implementations, and that the various operations and steps may be rearranged or otherwise modified and that other implementations are possible. Furthermore, aspects from two or more methods may be combined.

[0277] Although aspects of LTE, LTE-A, LTE-A Pro, or NR systems may be described for example purposes, and LTE, LTE-A, LTE-A Pro, or NR terminology may be used in much of the description, the techniques described herein may also be applicable to networks other than LTE, LTE-A, LTE-A Pro, or NR networks. For example, the described techniques may be applicable to various other wireless communication systems, such as Ultra Mobile Broadband (UMB), Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, and other systems and radio technologies not explicitly mentioned herein.

[0278] The information and signals described herein may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the specification may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0279] The various illustrative blocks and components described in conjunction with the disclosure herein may be implemented or performed with a general purpose processor, DSP, ASIC, CPU, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0280] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. When implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on a computer-readable medium, or sent via a computer-readable medium. Other examples and specific implementations are within the scope of the present disclosure and the appended claims. For example, due to the nature of software, the functions described herein may be implemented using software executed by a processor, hardware, firmware, hard wiring, or a combination of any of these items. Features that implement the functions may also be physically located at different locations, including being distributed so that the various parts of the functions are implemented at different physical locations.

[0281] Computer-readable medium includes both non-transient computer storage medium and communication medium, and it includes any medium that promotes computer program to be transmitted from one place to another.Non-transient storage medium can be any available medium that can be accessed by general or special-purpose computer.By way of example and not limitation, non-transient computer-readable medium can include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, compact disc (CD) ROM or other optical disc storage device, disk storage device or other magnetic storage device or can be used for carrying or storing desired program code means and any other non-transient medium that can be accessed by general or special-purpose computer or general or special-purpose processor in the form of instruction or data structure.In addition, any connection is appropriately referred to as computer-readable medium.For example, if software is sent from website, server or other remote source using coaxial cable, optical fiber cable, twisted pair, digital subscriber line (DSL) or wireless technology such as infrared, radio and microwave, then coaxial cable, optical fiber cable, twisted pair, DSL or wireless technology such as infrared, radio and microwave are included in the definition of computer-readable medium. Disk and disc, as used herein, includes CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media.

[0282] As used herein (including in the claims), "or" used in a list of items (e.g., a list of items followed by a phrase such as "at least one of" or "one or more of") indicates an inclusive list, so that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). In addition, as used herein, the phrase "based on" should not be interpreted as a reference to a closed set of conditions. For example, an example step described as "based on condition A" can be based on condition A and condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" should be interpreted in the same manner as the phrase "based at least in part on."

[0283] The terms "determine" or "determine" encompass various actions and "determine" may include calculating, computing, processing, deriving, investigating, looking up (such as via looking up in a table, database or other data structure), ascertaining, etc. Additionally, "determining" may include receiving (such as receiving information), accessing (such as accessing data in a memory), etc. Additionally, "determining" may include parsing, retrieving, selecting, choosing, establishing, and other such similar actions.

[0284] In the drawings, similar components or features may have the same reference label. In addition, various components of the same type may be distinguished by following the reference label with a dash and a second label to distinguish between the similar components. If only the first reference label is used in the specification, the description may apply to any of the similar components having the same first reference label regardless of the second reference label or other subsequent reference labels.

[0285] The descriptions set forth herein in conjunction with the accompanying drawings describe example configurations and do not represent all examples that may be implemented or within the scope of the claims. The term "example" as used herein means "used as an example, instance, or illustration" and does not mean "preferred" or "having advantages over other examples". The specific implementation includes specific details to provide an understanding of the technology. However, the technologies may be practiced without these specific details. In some instances, known structures and devices are shown in block diagram form to avoid obscuring the concepts of the examples.

[0286] The description herein is provided to enable one of ordinary skill in the art to implement or use the present disclosure. Various modifications to the present disclosure will be apparent to one of ordinary skill in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. The present disclosure is not limited to the examples and designs described herein, but should be given the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for wireless communication at a user equipment (UE), include: transmitting a plurality of reference signals indicating information associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at the UE corresponding to the receive direction for the communication; receiving signaling indicating a machine learning model for obtaining channel characteristic predictions associated with the first set of channel measurement resources based at least in part on transmitting the plurality of reference signals, the machine learning model being based at least in part on the receive beam at the UE; Inputting an input for obtaining the channel characteristic prediction into the machine learning model; as well as Signaling is received based at least in part on obtaining the channel characteristic prediction associated with the first set of channel measurement resources.

2. The method according to claim 1, further comprising: include: determining, for the UE, the receive beam corresponding to the receive direction for the communication based at least in part on one or more channel measurement resource identifiers corresponding to the second set of channel measurement resources and one or more first channel characteristics associated with the second set of channel measurement resources, wherein the information includes the one or more first channel characteristics; as well as The one or more channel measurement resource identifiers are transmitted in a same signal as a subset of the plurality of reference signals associated with the second set of channel measurement resources, wherein receiving the signaling is based at least in part on transmitting the one or more channel measurement resource identifiers.

3. The method according to claim 2, further comprising: include: inputting the one or more channel measurement resource identifiers, one or more second channel characteristics associated with the first set of channel measurement resources, or both to the machine learning model based at least in part on transmitting the plurality of reference signals; and The channel characteristic prediction associated with the first set of channel measurement resources is obtained based at least in part on inputting the one or more channel measurement resource identifiers, the one or more second channel characteristics associated with the first set of channel measurement resources, or both.

4. The method according to claim 2, further comprising: include: An indication of reference signal received power associated with the first set of channel measurement resources is sent in the same signal as the subset of the plurality of reference signals. 5 . The method of claim 2 , wherein the one or more first channel characteristics comprise one or more reference signal received power values ​​for the subset of the plurality of reference signals.

6. The method according to claim 2, further comprising: include: A spatial filter corresponding to the receive beam is determined based at least in part on one or more sounding reference signal resource identifiers corresponding to the second set of channel measurement resources, wherein the one or more channel measurement resource identifiers include the one or more sounding reference signal resource identifiers.

7. The method according to claim 1, further comprising: include: receiving control signaling indicating to the UE that a plurality of channel characteristics associated with the first set of channel measurement resources are to be sent together with the plurality of reference signals; as well as The plurality of channel characteristics are sent in the same signal as the plurality of reference signals, the plurality of channel characteristics comprising one or more of reference signal received power, signal to interference plus noise ratio, rank indicator, channel quality indicator, or precoding matrix indicator.

8. The method according to claim 1, further comprising: include: Sending a message indicating that the UE supports a capability to report the information associated with the first set of channel measurement resources, the second set of channel measurement resources, or the receive direction for the communication, wherein sending the multiple reference signals indicating the information is based at least in part on the capability.

9. The method according to claim 1, further comprising: include: sending the plurality of reference signals according to a periodicity; as well as According to the periodicity, additional signaling indicating updates to the machine learning model is received based at least in part on sending the plurality of reference signals.

10. The method according to claim 1, further comprising: include: sending a first channel state information report having a first priority; as well as A second channel state information report having a second priority is sent, the second channel state information report including the information, wherein the first priority is greater than the second priority.

11. The method of claim 1, wherein a first angular spread of a first reference signal among the multiple reference signals associated with the second set of channel measurement resources is smaller than a second angular spread of a second reference signal among the multiple reference signals associated with the first set of channel measurement resources.

12. The method of claim 1, wherein the plurality of reference signals comprises a channel state information reference signal, a synchronization signal block, or both.

13. The method of claim 1, wherein the channel characteristics associated with the first set of channel measurement resources predict time domain channel characteristics for the plurality of reference signals, spatial domain channel characteristics for the plurality of reference signals, or both.

14. A method for wireless communication at a network entity, include: receiving a plurality of reference signals indicating information corresponding to one or more channel characteristics associated with a first set of channel measurement resources, a second set of channel measurement resources, and a receive direction for communication, the information corresponding to a receive beam at a user equipment (UE) corresponding to the receive direction for the communication; training the machine learning model to obtain channel characteristic predictions associated with the first set of channel measurement resources based at least in part on inputting the one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, and the receive direction for communication into a machine learning model for obtaining channel characteristic predictions associated with the first set of channel measurement resources, the one or more channel characteristics being based at least in part on the receive beam at the UE; as well as Sending signaling indicative of the machine learning model based at least in part on training the machine learning model.

15. The method according to claim 14, further comprising: include: receiving one or more channel measurement resource identifiers associated with the second set of channel measurement resources in a same signal as a subset of the plurality of reference signals associated with the second set of channel measurement resources; as well as The receive beam at the UE for measuring the first set of channel measurement resources is determined based at least in part on the one or more channel measurement resource identifiers.

16. The method according to claim 15, further comprising: include: receiving the plurality of reference signals according to a periodicity; inputting the one or more channel characteristics and the one or more channel measurement resource identifiers into the machine learning model according to the periodicity to obtain the channel characteristic prediction, wherein the channel characteristic prediction comprises a reference signal received power associated with the first set of channel measurement resources, a channel measurement resource identifier associated with the second set of channel measurement resources, or both; and According to the periodicity, additional signaling indicating an updated machine learning model is sent based at least in part on inputting the one or more channel characteristics and the one or more channel measurement resource identifiers.

17. The method according to claim 16, further comprising: include: A loss function for the machine learning model is calculated using the reference signal received power associated with the first set of channel measurement resources, the channel measurement resource identifier associated with the second set of channel measurement resources, or both.

18. The method according to claim 15, further comprising: include: An indication of reference signal received power associated with the first set of channel measurement resources is received in the same signal as the subset of the plurality of reference signals.

19. The method according to claim 15, further comprising: include: A spatial filter corresponding to the receive beam is determined based at least in part on one or more sounding reference signal resource identifiers corresponding to the second set of channel measurement resources, wherein the one or more channel measurement resource identifiers include the one or more sounding reference signal resource identifiers.

20. The method according to claim 14, further comprising: include: sending control signaling, the control signaling instructing the UE to send a plurality of channel characteristics associated with the first set of channel measurement resources together with the plurality of reference signals; as well as The plurality of channel characteristics are received in the same signal as the plurality of reference signals, the plurality of channel characteristics comprising one or more of a reference signal received power, a signal to interference plus noise ratio, a rank indicator, a channel quality indicator, or a precoding matrix indicator.

21. The method according to claim 14, further comprising: include: Receiving a message indicating that the UE supports a capability to report the information corresponding to the one or more channel characteristics associated with the first set of channel measurement resources, the second set of channel measurement resources, or the receive direction for the communication, wherein receiving the multiple reference signals indicating the information is based at least in part on the capability.

22. The method according to claim 14, further comprising: include: receiving a first channel state information report having a first priority; as well as A second channel state information report having a second priority is received, the second channel state information report including the plurality of reference signals, wherein the first priority is greater than the second priority.

23. The method of claim 14, wherein a first angular spread of a first reference signal of the plurality of reference signals associated with the second set of channel measurement resources is smaller than a second angular spread of a second reference signal of the plurality of reference signals associated with the first set of channel measurement resources.

24. The method of claim 14, wherein the plurality of reference signals comprises a channel state information reference signal, a synchronization signal block, or both.

25. The method of claim 14, wherein the one or more channel characteristics associated with the first group of channel measurement resources, the second group of channel measurement resources, and the receiving direction for communication include one or more of: time domain channel characteristics of the multiple reference signals, spatial domain channel characteristics of the multiple reference signals, or both.

26. A method for wireless communication at a user equipment (UE), include: transmitting information associated with one or more channel measurement resources and a receive direction for communication; receiving signaling indicating a machine learning model for obtaining channel characteristic predictions associated with the one or more channel measurement resources based at least in part on sending the information; inputting to the machine learning model an input for obtaining a prediction of the channel characteristic associated with the one or more channel measurement resources; as well as Signaling is received based at least in part on obtaining the channel characteristic prediction associated with the one or more channel measurement resources.

27. The method according to claim 26, further comprising: include: inputting into the machine learning model one or more channel characteristics, the transmitted information, or both, associated with the one or more channel measurement resources; as well as The channel characteristic prediction, the transmitted information, or both associated with the one or more channel measurement resources are obtained based at least in part on the input.

28. The method according to claim 26, further comprising: include: A message is sent indicating that the UE supports a capability to send the information, wherein sending the information is based at least in part on the capability.

29. The method according to claim 26, further comprising: include: sending the information according to a periodicity; as well as According to the periodicity, additional signaling indicating updates to the machine learning model is received based at least in part on sending the information.

30. The method according to claim 26, further comprising: include: sending a first channel state information report having a first priority; as well as A second channel state information report having a second priority is sent, the second channel state information report including the information, wherein the first priority is greater than the second priority.

31. The method of claim 26, wherein the information comprises a phase coefficient associated with a radio frequency chain, an amplitude coefficient associated with the radio frequency chain, a phase coefficient associated with a phase shifter, an amplitude associated with the phase shifter, an antenna panel identifier associated with the receive direction, an antenna panel orientation associated with the receive direction, a target angle of arrival for the communication, or a zenith angle of arrival for the communication.

32. A method for wireless communication at a network entity, include: receiving, at a user equipment (UE), information associated with one or more channel measurement resources and a receive direction for communication; training a machine learning model for obtaining channel characteristic predictions based at least in part on inputting one or more channel characteristics and the information into the machine learning model for obtaining channel characteristic predictions; and Sending signaling indicative of the machine learning model based at least in part on training the machine learning model.

33. The method according to claim 32, further comprising: include: receiving the information according to a periodicity; inputting the one or more channel characteristics and the information into the machine learning model according to the periodicity to obtain the channel characteristic prediction, wherein the channel characteristic prediction includes a reference signal received power associated with the one or more channel measurement resources, a channel measurement resource identifier, or both; and According to the periodicity, additional signaling indicating an updated machine learning model is sent based at least in part on inputting the one or more channel characteristics and the information into the machine learning model.

34. The method according to claim 32, further comprising: include: A message is received indicating that the UE supports a capability to send the information, wherein receiving the information is based at least in part on the capability.

35. The method according to claim 32, further comprising: include: receiving a first channel state information report having a first priority; as well as A second channel state information report having a second priority is received, the second channel state information report including the information, wherein the first priority is greater than the second priority.

36. The method of claim 32, wherein the information comprises a phase coefficient associated with a radio frequency chain, an amplitude coefficient associated with the radio frequency chain, a phase coefficient associated with a phase shifter, an amplitude associated with the phase shifter, an antenna panel identifier associated with the receive direction, an antenna panel orientation associated with the receive direction, a target angle of arrival for the communication, or a zenith angle of arrival for the communication.

Images