Techniques for beam management

Abstract

Methods, systems, and devices for wireless communication are described. A user equipment (UE) may receive control signaling including a configuration indicating a plurality of resource sets. In some examples, the UE may train a model for channel prediction using a first set of resources and a second set of resources. Additionally, the UE may predict channel characteristics for the third set of resources using the trained model from the third set of resources and the fourth set of resources. In some examples, the UE may maintain a correspondence between the first set of resources and the third set of resources in terms of the number of resources, the order of resources, and the beam shape. Additionally, the UE may maintain a correspondence between the second set of resources and the fourth set of resources in terms of the number of resources, the order of resources, the beam shape, and the subsample pattern.

Description

Technical Field

[0001] The following pertains to wireless communication, including techniques used for beam management. Background Technology

[0002] Wireless communication systems are widely deployed to provide various types of communication content, such as voice, video, packet data, message sending and receiving, broadcasting, and so on. 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, LTE-A Advanced (LTE-A) systems, or LTE-A Pro systems) and fifth-generation (5G) systems, which may be referred to as New Radio (NR) systems. These systems may employ technologies such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal FDMA (OFDMA), or Discrete Fourier Transform Extended Orthogonal Frequency Division Multiplexing (DFT-S-OFDM). A wireless multiple access communication system may include one or more base stations, each supporting wireless communication of communication devices, which may be referred to as User Equipment (UE). Summary of the Invention

[0003] The described technology relates to methods, systems, devices, and apparatuses that support improvements to techniques for beam management. For example, the described technology enables a UE to support a resource indexing framework to maintain a correspondence between a resource set associated with a training model and a resource set associated with using the trained model to predict channel characteristics. For example, the UE can maintain a correspondence between a first resource set (e.g., training resource set A) and a second resource set (e.g., inferred resource set A) in terms of the number of resources, the order of resources, and beam characteristics (e.g., beam shape). For example, the UE can use the first... k Resources to generate the first k The model outputs features, where the UE uses the first... k The model outputs features to predict the first inference resource set A. k Channel characteristics of resources.

[0004] Additionally, the UE may maintain a correspondence between a third resource set (e.g., training resource set B) and a fourth resource set (e.g., inference resource set B) in terms of the quantity of resources, the order of resources, the beam characteristics, and the subsample mode. For example, the training resource set B and the inference resource set B may include the same subset of resources from the training resource set A and the inference resource set A, respectively. That is, if the training resource set B includes a first set of entry identifiers (IDs) indicating a subset of resources from the training resource set A, then the inference resource set B may include the same set of associated entry IDs indicating a subset of resources from the inference resource set A.

[0005] Additionally or alternatively, the UE may perform model training and model inference within multiple cycles. In some examples, the UE may maintain the same subsample pattern for both the training resource set B and the inference resource set B. In such examples, the UE may use the training resource set A, the training resource set B, the inference resource set A, and the inference resource set B for channel prediction at the same periodicity. In some examples, the UE may use different subsample patterns for the training resource set B and the inference resource set B across different channel prediction cycles. In such examples, the UE may use the training resource set A and the inference resource set A for channel prediction according to a first periodicity, and use the training resource set B and the inference resource set B according to a second periodicity that may be an integer multiple of the first periodicity.

[0006] A method for wireless communication by a UE is described. The method may include: receiving control signaling including an indication of the configuration of a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the resource parameter set including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set; training a model based on the first resource set and the second resource set, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes a first predicted channel characteristic associated with the first resource set. The set; and a second set of predicted channel characteristics associated with a third resource set based on the trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third resource set and the fourth resource set are configured using the set of resource parameters for the beam management, and wherein the fourth resource set is configured using the set of resource parameters for the beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

[0007] A UE for wireless communication is described. The UE may include: one or more memories storing processor-executable code; and one or more processors coupled to the one or more memories. The one or more processors may be able to operate individually or jointly to execute the code to cause the UE to: receive control signaling including instructions on the configuration of a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the resource parameter set including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set; train a model based on the first resource set and the second resource set, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes data related to the first resource set. A first set of associated predicted channel characteristics; and a second set of predicted channel characteristics associated with a third resource set based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

[0008] Another UE for wireless communication is described. The UE may include: components for receiving control signaling including a configuration indicating a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the resource parameter set including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set; components for training a model based on the first resource set and the second resource set, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes predicted channel characteristics associated with the first resource set. The first set; and components for obtaining a second set of predicted channel characteristics associated with a third resource set based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third resource set and the fourth resource set are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

[0009] A non-transitory computer-readable medium storing code for wireless communication is described. The code may include instructions executable by at least one processor to: receive control signaling including instructions indicating the configuration of a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more subsets of resources from the first resource set; train a model based on the first resource set and the second resource set, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes information associated with the first resource set. A first set of predicted channel characteristics; and a second set of predicted channel characteristics associated with a third resource set based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

[0010] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the second resource set includes a set of resource entries each associated with a corresponding resource in the second resource set, and the fourth resource set includes a set of resource entries each associated with a corresponding resource and a corresponding entry ID in the fourth resource set.

[0011] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first subsample pattern of the second resource set includes a first list of entry IDs corresponding to a subset of resources from the first resource set; and the second subsample pattern of the fourth resource set includes a second list of entry IDs corresponding to a subset of resources from the third resource set.

[0012] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first list of entry IDs and the second list of entry IDs may be the same list of entry IDs.

[0013] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first quantity of resources in the second resource set may be equal to the second quantity of resources in the fourth resource set.

[0014] Some examples of the methods, UEs, and nontransitory computer-readable media described herein may also include operations, features, components, or instructions for: determining a first set of input values ​​for the model based on measurements of the second resource set during training of the model, wherein a first input value in the first set of input values ​​may be associated with a first entry of the second resource set; and using the trained model, determining a second set of input values ​​for the model based on measurements of the fourth resource set, wherein a first input value in the second set of input values ​​may be associated with a first entry of the fourth resource set.

[0015] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, each resource entry of the second resource set may be associated with a corresponding beam according to the first beam shape, and each resource entry of the fourth resource set may be associated with a corresponding beam according to the second beam shape.

[0016] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first resource entry of the second resource set may be associated with a first beam including a first pointing direction and a first beamwidth; the first resource entry of the fourth resource set may be associated with a second beam including a second pointing direction and a second beamwidth; and based on the consistency between the first beam shape and the second beam shape, the difference between the first pointing direction and the second pointing direction satisfies a direction threshold, and the difference between the first beamwidth and the second beamwidth satisfies a beamwidth threshold.

[0017] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first resource set and the third resource set may be used across multiple network entities, bandwidths, or a combination of both.

[0018] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first resource set includes a set of resource entries, each associated with a corresponding resource and a corresponding entry ID in the first resource set; the first resource set can be configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, and a first beam shape; the third resource set includes a set of resource entries, each associated with a corresponding resource and a corresponding entry ID in the third resource set; and the third resource set can be configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, and a second beam shape.

[0019] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first quantity of resources in the first resource set may be equal to the second quantity of resources in the third resource set.

[0020] Some examples of the methods, UEs, and nontransitory computer-readable media described herein may also include operations, features, components, or instructions for: determining a set of output values ​​of the model based on measurements of the first resource set during training of the model, wherein a first output value in the set of output values ​​may be associated with a first entry of the first resource set; and using the set of output values ​​of the model to determine a corresponding predicted channel characteristic for each resource entry of the third resource set, wherein the first output value may be used to determine the predicted channel characteristic of the first resource entry of the third resource set based on the consistency between the first order of resources and the second order of resources.

[0021] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, each resource entry of the first resource set may be associated with a corresponding beam according to the first beam shape, and each resource entry of the third resource set may be associated with a corresponding beam according to the second beam shape.

[0022] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first resource entry of the first resource set may be associated with a first beam including a first pointing direction and a first beamwidth; the first resource entry of the third resource set may be associated with a second beam including a second pointing direction and a second beamwidth; and based on the consistency between the first beam shape and the second beam shape, the difference between the first pointing direction and the second pointing direction satisfies a direction threshold, and the difference between the first beamwidth and the second beamwidth satisfies a beamwidth threshold.

[0023] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first resource set and the third resource set may be used across multiple network entities, bandwidths, or a combination of both.

[0024] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, each of the first resource set, the second resource set, the third resource set, and the fourth resource set may be associated with the same periodicity.

[0025] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first resource set may be associated with a first periodicity, the second resource set may be associated with a second periodicity, the third resource set may be associated with a third periodicity, and the fourth resource set may be associated with a fourth periodicity.

[0026] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the first periodicity may be equal to the third periodicity, the second periodicity may be equal to the fourth periodicity, and the fourth periodicity and the second periodicity may be integer multiples of the first periodicity and the second periodicity.

[0027] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, and in particular, the methods, apparatus, and nontransitory computer-readable media may include further operations, features, components, or instructions for segmenting the second resource set and the fourth resource set into a number of subgroups equal to an integer multiple of the value.

[0028] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the duration between two resources in the same resource subgroup of the first resource set may be equal to a first duration; the duration between two resources in adjacent resource subgroups of the first resource set may be equal to a second duration; and the second duration may be greater than the first duration by a duration threshold.

[0029] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the second resource set may be configured via the number of resource subsets of the first resource set, and the fourth resource set may be configured via the same number of resource subsets of the third resource set, the integer multiple being equal to the number of resource subsets.

[0030] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the duration between two resources in the same resource subset of the first resource set may be equal to a first duration; the duration between two resources in adjacent resource subsets of the first resource set may be equal to a second duration; and the second duration may be greater than the first duration by a duration threshold.

[0031] The methods described herein, UEs, and some examples of nontransitory computer-readable media may also include operations, features, components, or instructions for: receiving a first indication of a model ID associated with the model, wherein training the model may be based on receiving the first indication of the model ID; and after training the model, receiving a second indication of the model ID associated with the model, wherein obtaining the second set of predicted channel characteristics associated with the third resource set may be based on receiving the second indication of the model ID.

[0032] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, a first set of model IDs can be configured, each model ID in the first set of model IDs is associated with a different model, and a first model ID in the first set of model IDs can be associated with that model.

[0033] The methods described herein, some examples of UEs and nontransitory computer-readable media may also include operations, features, components or instructions for: receiving from a network entity a second set of model IDs supported at that network entity; and sending to the network entity an instruction for the first set of model IDs, wherein training the model may be based on the first model ID associated with the model being included in both the first set of model IDs and the second set of model IDs.

[0034] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the set of multiple resource sets associated with the beam management indicated by the control signaling also includes the third resource set and the fourth resource set.

[0035] In some examples of the methods, UEs, and nontransitory computer-readable media described herein, the set of multiple resource sets associated with the beam management indicated by the control signaling also includes the fourth resource set, and the third resource set includes a virtual resource set.

[0036] A method for wireless communication via a network is described. The method may include: transmitting control signaling including instructions for configuring a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the resource parameter set including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, wherein the first resource set and the second resource set are associated with a model trained at a UE; and transmitting instructions for the UE using the trained model based on... Measurements of a fourth resource set are used to obtain an indication of a set of predicted channel characteristics associated with a third resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

[0037] A network for wireless communication is described. The network may include: one or more memories storing processor-executable code; and one or more processors coupled to the one or more memories. The one or more processors may be able to operate individually or jointly to execute the code to enable the network to: transmit control signaling including instructions on the configuration of a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the resource parameter set including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, wherein the first resource set and the second resource set are associated with a model trained at a UE; and transmit control signaling for the network. The UE uses a trained model to obtain an indication of a set of predicted channel characteristics associated with a third resource set based on measurements of a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

[0038] Another network for wireless communication is described. The network may include: components for transmitting control signaling, the control signaling including an indication of the configuration of a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the resource parameter set including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, wherein the first resource set and the second resource set are associated with a model trained at a UE; and components for transmitting, for the UE, a configuration based on the trained model. A component for measuring a fourth resource set to obtain an indication of a set of predicted channel characteristics associated with a third resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

[0039] A non-transitory computer-readable medium storing code for wireless communication is described. The code may include instructions executable by at least one processor to: transmit control signaling including instructions indicating the configuration of a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the resource parameter set including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, wherein the first resource set and the second resource set are associated with a model trained at a UE; and transmit commands for the UE to use... The trained model obtains an indication of a set of predicted channel characteristics associated with a third resource set based on measurements of a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern. Attached Figure Description

[0040] Figure 1 Examples of wireless communication systems supporting techniques for beam management according to one or more aspects of this disclosure are shown.

[0041] Figure 2 Examples of wireless communication systems supporting techniques for beam management according to one or more aspects of this disclosure are shown.

[0042] Figure 3 An example of a model training process supporting a technique for beam management according to one or more aspects of this disclosure is shown.

[0043] Figure 4 An example of a model inference process for a technique used in beam management, based on one or more aspects of this disclosure, is shown.

[0044] Figure 5 An example of a resource pattern loop process supporting a technique for beam management according to one or more aspects of this disclosure is shown.

[0045] Figure 6 An example of a process flow supporting a technique for beam management according to one or more aspects of this disclosure is shown.

[0046] Figure 7 and Figure 8 A block diagram of an apparatus supporting a technique for beam management according to one or more aspects of this disclosure is shown.

[0047] Figure 9 A block diagram of a communication manager supporting techniques for beam management according to one or more aspects of this disclosure is shown.

[0048] Figure 10 A diagram of a system including a device supporting a technology for beam management, according to one or more aspects of this disclosure, is shown.

[0049] Figure 11 and Figure 12 A block diagram of an apparatus supporting a technique for beam management according to one or more aspects of this disclosure is shown.

[0050] Figure 13 A block diagram of a communication manager supporting techniques for beam management according to one or more aspects of this disclosure is shown.

[0051] Figure 14 A diagram of a system including a device supporting a technology for beam management, according to one or more aspects of this disclosure, is shown.

[0052] Figure 15 and Figure 16A flowchart illustrating a method for supporting beam management techniques according to one or more aspects of this disclosure is shown. Detailed Implementation

[0053] The UE can use a defined set of resources to establish communication with network entities. For example, each resource within this set may correspond to a specific direction in the spatial domain, enabling the UE to communicate with network entities, such as via a narrow beam. The UE can utilize the resource set to estimate channel characteristics for various communications with network entities. For example, the UE can downsample an initial resource set (e.g., training resource set A) to a second resource set (e.g., training resource set B). Additionally, the UE may support artificial intelligence (AI) or machine learning (ML) techniques to train a model for predicting the channel characteristics of the initial resource set. Such a training process may involve inputting measurements obtained using the second resource set.

[0054] A UE can use a trained model to predict channels for other resource sets; this process is called model inference. For example, a UE can downsample a third resource set (e.g., inference resource set A) to a fourth resource set (e.g., inference resource set B). In some examples, the UE can input measurements from the fourth resource set into the trained model to predict channel characteristics for the third resource set. However, some wireless communication systems may not support a resource indexing framework to maintain consistency between the resources used to train the model (e.g., the first and second resource sets) and the resources used for channel prediction with the model (e.g., the third and fourth resource sets). If the UE does not determine the consistency between resources in the first and third resource sets (e.g., training and inference resource set A) or between resources in the second and fourth resource sets (e.g., training and inference resource set B), the UE may experience degraded channel prediction quality when using the model.

[0055] Various aspects of this disclosure relate to a UE-supported resource indexing framework to maintain a correspondence between a resource set associated with a training model and a resource set associated with using the trained model to predict channel characteristics. For example, the UE may maintain a correspondence between a first resource set (e.g., training resource set A) and a second resource set (e.g., inference resource set A) in terms of the number of resources, the order of resources, and beam characteristics (e.g., beam shape). For example, the UE may use the first... k Resources to generate the first k The model outputs features, where the UE uses the first... k The model outputs features to predict the first inference resource set A. k Channel characteristics of resources.

[0056] Additionally, the UE may maintain a correspondence between a third resource set (e.g., training resource set B) and a fourth resource set (e.g., inference resource set B) in terms of the quantity of resources, the order of resources, the beam characteristics, and the subsample pattern. For example, the training resource set B and the inference resource set B may include the same subset of resources from the training resource set A and the inference resource set A, respectively. That is, if the training resource set B includes a first set of entry IDs indicating a subset of resources from the training resource set A, then the inference resource set B may include the same set of associated entry IDs indicating a subset of resources from the inference resource set A.

[0057] Additionally or alternatively, the UE may perform model training and model inference within multiple cycles. In some examples, the UE may maintain the same subsample pattern for both the training resource set B and the inference resource set B. In such examples, the UE may use the training resource set A, the training resource set B, the inference resource set A, and the inference resource set B for channel prediction at the same periodicity. In some examples, the UE may use different subsample patterns for the training resource set B and the inference resource set B across different channel prediction cycles. In such examples, the UE may use the training resource set A and the inference resource set A for channel prediction according to a first periodicity, and use the training resource set B and the inference resource set B according to a second periodicity that may be an integer multiple of the first periodicity.

[0058] The various aspects of this disclosure are first described in the context of wireless communication systems. These aspects are further discussed with reference to model training processes, model inference processes, resource pattern loop processes, and process flows. The various aspects of this disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and flowcharts relating to techniques used for beam management.

[0059] Figure 1 An example of a wireless communication system 100 supporting techniques for beam management according to one or more aspects of this disclosure is shown. 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 an LTE network, an Advanced LTE (LTE-A) network, an LTE-APro network, a New Radio (NR) network, or a network operating according to other systems and radio technologies, including future systems and radio technologies not expressly mentioned herein.

[0060] Network entity 105 may be distributed across a geographical area to form wireless communication system 100, and may include devices employing different forms or having different capabilities. In various examples, network entity 105 may be referred to as a network element, mobility element, radio access network (RAN) node, or network equipment, etc. In some examples, network entity 105 and UE 115 may wirelessly communicate via one or more communication links 125 (e.g., radio frequency (RF) access links). For example, network entity 105 may support coverage area 110 (e.g., a geographical coverage area) within which UE 115 and network entity 105 may establish one or more communication links 125. Coverage area 110 may be an example of a geographical area within which network entity 105 and UE 115 may support the transmission of signals according to one or more radio access technologies (RATs).

[0061] UE 115 can be distributed throughout the coverage area 110 of wireless communication system 100, and each UE 115 can be stationary or mobile, or stationary and mobile at different times. UE 115 can be devices in different forms or with different capabilities. Figure 1 Some example UE 115s are illustrated herein. The UE 115 described herein can be able to support various types of devices (such as, e.g., ...). Figure 1 It communicates with other UEs (115 or network entity 105) as shown.

[0062] As described herein, nodes of the wireless communication system 100 (which may be referred to as network nodes or wireless nodes) may be network entity 105 (e.g., any network entity described herein), UE 115 (e.g., any UE described herein), network controller, apparatus, device, computing system, one or more components, or another suitable processing entity configured to perform any of the techniques described herein. For example, a node may be UE 115. Alternatively, a node may be network entity 105. Furthermore, a first node may be configured to communicate with a second or third node. In one aspect of this example, the first node may be UE 115, the second node may be network entity 105, and the third node may be UE 115. In another aspect of this example, the first node may be UE 115, the second node may be network entity 105, and the third node may be network entity 105. In other aspects of this example, the first node, the second node, and the third node may be different from these examples. Similarly, references to UE 115, network entity 105, device, equipment, or computing system may include disclosures that UE 115, network entity 105, device, equipment, or computing system is a node. For example, a disclosure that UE 115 is configured to receive information from network entity 105 also discloses that a first node is configured to receive information from a second node.

[0063] In some examples, network entity 105 may communicate with core network 130, communicate with each other, or both. For example, network entity 105 may communicate with 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, network entities 105 may communicate with each other directly (e.g., directly between network entities 105) or indirectly (e.g., via core network 130) via backhaul communication links 120 (e.g., according to X2, Xn, or other interface protocols). In some examples, network entities 105 may communicate with each other via midhaul communication link 162 (e.g., according to midhaul interface protocol) or fronthaul communication link 168 (e.g., according to fronthaul interface protocol) or any combination thereof. The backhaul communication link 120, midhaul communication link 162, or fronthaul communication link 168 may be one or more wired links (e.g., electrical links, fiber optic links), one or more wireless links (e.g., radio links, wireless optical links), etc., or various combinations thereof, or may include one or more wired links (e.g., electrical links, fiber optic links), one or more wireless links (e.g., radio links, wireless optical links), etc., or various combinations thereof. UE 115 may communicate with the core network 130 via communication link 155.

[0064] One or more network entities in network entity 105 described herein may include or be referred to as base station 140 (e.g., transceiver base station, radio base station, NR base station, access point, radio transceiver, node B, eNodeB (eNB), next-generation node B or gigabit node B (any of which may be referred to as gNB), 5G NB, next-generation eNB (ng-eNB), home node B, home evolution node B, or other suitable terms). In some examples, network entity 105 (e.g., base station 140) may be implemented in an aggregated (e.g., monolithic, standalone) base station architecture that may be configured to utilize a protocol stack that is physically or logically integrated within a single network entity 105 (e.g., a single RAN node, such as base station 140).

[0065] In some examples, network entity 105 may be implemented in a decomposed architecture (e.g., a decomposed base station architecture, a decomposed RAN architecture) that can be configured to utilize protocol stacks physically or logically distributed across two or more network entities 105, such as an Integrated Access Backhaul (IAB) network, an Open RAN (O-RAN) (e.g., a network configuration sponsored by the O-RAN Alliance), or a Virtualized RAN (vRAN) (e.g., a Cloud RAN (C-RAN)). For example, 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, a non-real-time RIC), a Service Management and Orchestration (SMO) 180 system, or any combination thereof. 170 may also be referred to as a radio headend, intelligent radio headend, remote radio headend (RRH), remote radio unit (RRU), or transmit / receive point (TRP). One or more components of network entity 105 in a decomposed RAN architecture may be co-located, or one or more components of network entity 105 may be located in distributed locations (e.g., separate physical locations). In some examples, one or more network entities 105 in a decomposed RAN architecture may be implemented as virtual units (e.g., virtual CU (VCU), virtual DU (VDU), virtual RU (VRU)).

[0066] The functional splitting among CU 160, DU 165, and RU 170 is flexible and can 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, or RU 170. For example, a protocol stack functional splitting can be used between CU 160 and DU 165, allowing CU 160 to support one or more layers of the protocol stack, and DU 165 to support one or more different layers of the protocol stack. In some examples, CU 160 can host higher protocol layer (e.g., Layer 3 (L3), Layer 2 (L2)) functionalities and signaling (e.g., Radio Resource Control (RRC), Serving Data Adaptation Protocol (SDAP), Packet Data Convergence Protocol (PDCP)). CU 160 can connect to one or more DU 165 or RU 170, and one or more DU 165 or RU 170 can 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 each can be at least partially controlled by CU 160. Additionally or alternatively, a protocol stack functional split can be employed between DU 165 and RU 170, such that DU 165 can support one or more layers of the protocol stack, and RU 170 can support one or more different layers of the protocol stack. DU 165 can support one or more different cells (e.g., via one or more RU 170). In some cases, functional decomposition between CU 160 and DU 165, or between DU 165 and RU 170, can be performed within the protocol layer (e.g., some functions of the protocol layer can be performed by one of CU 160, DU 165, or RU 170, while other functions of the protocol layer can be performed by different of CU 160, DU 165, or RU 170). CU 160 can be further functionally decomposed into CU control plane (CU-CP) functions and CU user plane (CU-UP) functions. CU 160 can be connected to one or more DU 165 via midhaul communication link 162 (e.g., F1, F1-c, F1-u), and DU 165 can be connected to one or more RU 170 via fronthaul communication link 168 (e.g., open fronthaul (FH) interface). In some examples, the midhaul communication link 162 or the fronthaul communication link 168 may be implemented based on the interfaces (e.g., channels) between the layers of the protocol stack, which are supported by the corresponding network entities 105 communicating via such communication links.

[0067] In a wireless communication system (e.g., wireless communication system 100), the infrastructure and spectrum resources for radio access can support wireless backhaul link capabilities to supplement wired backhaul connections, thereby providing an IAB network architecture (e.g., to core network 130). In some cases, in an IAB network, one or more network entities 105 (e.g., IAB node 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 DU 165s or one or more RU 170s may be partially controlled by one or more CU 160s associated with donor network entity 105 (e.g., 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 node 104) via supported access and backhaul links (e.g., backhaul communication link 120). 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 a separate set of antennas for relaying communication with UE 115, or may share the same antennas (e.g., those of RU 170) for access to IAB node 104 via DU 165 of IAB node 104. (e.g., referred to as a virtual IAB-MT (vIAB-MT)). In some examples, 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 IAB node 104) may be configured to operate according to the techniques described herein.

[0068] In the context of applying the techniques described herein to a decomposed RAN architecture, one or more components of the decomposed RAN architecture may be configured to support techniques for beam management as described herein. For example, some operations described as being performed by UE 115 or network entity 105 (e.g., base station 140) may additionally or alternatively be performed by one or more components of the decomposed RAN architecture (e.g., IAB node 104, DU 165, CU 160, RU 170, RIC 175, SMO 180).

[0069] UE 115 may include or be referred to as a mobile device, wireless device, remote device, handheld device, or subscriber device, or any other suitable term, wherein “device” may also be referred to as a unit, station, terminal, or client, etc. UE 115 may also include or be referred to as personal electronic devices, such as cellular phones, personal digital assistants (PDAs), multimedia / entertainment devices (e.g., radios, MP3 players, or video devices), cameras, gaming devices, navigation / positioning devices (e.g., GNSS (Global Navigation Satellite System) devices based on, for example, GPS (Global Positioning System), BeiDou system, GLONASS or Galileo system, ground-based devices, etc.), tablet computers, laptop computers, netbooks, smartbooks, personal computers, smart devices, wearable devices (e.g., smartwatches, smart clothing, smart glasses, virtual reality goggles, smart wristbands, smart jewelry (e.g., smart rings, smart bracelets)), drones, robots / robotic devices, vehicles, vehicle equipment, meters (e.g., parking timers, electricity meters, gas meters, water meters), monitors, air pumps, electrical appliances (e.g., kitchen appliances, washing machines, dryers), location tags, medical / healthcare devices, implants, sensors / actuators, displays, or any other suitable device configured to communicate via wireless or wired media. In some examples, UE 115 may include or be referred to as a wireless local loop (WLL) station, Internet of Things (IoT) device, Internet of Everything (IoE) device, or machine-type communication (MTC) device, etc., which can be implemented in a variety of objects such as appliances or vehicles, meters, etc.

[0070] The UE 115 described herein can communicate with various types of devices, such as other UEs 115 that sometimes act as relays, network entities 105, and network equipment including macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations, etc. Figure 1 As shown.

[0071] UE 115 and network entity 105 can wirelessly communicate with each other via one or more communication links 125 (e.g., access links) using resources associated with one or more carriers. The term "carrier" can refer to a set of RF spectrum resources having a defined physical layer structure for supporting communication link 125. For example, a carrier for communication link 125 may include a portion of the RF spectrum band (e.g., a bandwidth portion (BWP)) operating 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 coordinating carrier operation, user data, or other signaling. Wireless communication system 100 may support communication with UE 115 using carrier aggregation or multi-carrier operation. Depending on the carrier aggregation configuration, UE 115 may be configured to utilize multiple downlink component carriers and one or more uplink component carriers. Carrier aggregation can be used in conjunction with both frequency division duplex (FDD) component carriers and time division duplex (TDD) component carriers. Communication between network entity 105 and other devices can refer to communication between these devices and any part of network entity 105 (e.g., entity, sub-entity). For example, the terms “send,” “receive,” or “communicate” when referring to network entity 105 can refer to any part of the RAN’s network entity 105 (e.g., base station 140, CU 160, DU 165, RU 170) communicating with another device (e.g., directly or via one or more other network entities 105).

[0072] The signal waveform transmitted via a carrier may include multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques, such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform extended OFDM (DFT-S-OFDM)). In a system employing MCM, 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 decoding rate of the modulation scheme, or both), such that a relatively high number of resource elements (e.g., in the transmission duration) and a relatively high modulation scheme order correspond to a relatively high communication rate. Wireless communication resources may refer to a combination of RF spectrum resources, temporal resources, and spatial resources (e.g., spatial layers or beams), and the use of multiple spatial resources may increase the data rate or data integrity used for communication with UE 115.

[0073] It can support one or more sets of parameters for a carrier, and the set of parameters may include subcarrier spacing ( (and cyclic prefix). A carrier can be divided into one or more BWPs with the same or different sets of parameters. In some examples, UE 115 can be configured using multiple BWPs. In some examples, a single BWP of a carrier can be active at a given time, and the communication of UE 115 can be constrained to one or more active BWPs.

[0074] The time interval for network entity 105 or UE 115 can be expressed as a multiple of a basic time unit, such as the sampling period. seconds, of which It can represent the supported subcarrier spacing, and This can represent the supported Discrete Fourier Transform (DFT) size. The time interval of the communication resources can be organized according to radio frames, each with a specified duration (e.g., 10 milliseconds (ms)). Each radio frame can be identified by a System Frame Number (SFN) (e.g., ranging from 0 to 1023).

[0075] Each frame may include multiple consecutively numbered subframes or time slots, and each subframe or time slot may have the same duration. In some examples, a frame may (e.g., in the time domain) be divided into subframes, and each subframe may be further divided into a 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 number of symbol periods (e.g., depending on the length of the cyclic prefix appended to each symbol period). In some wireless communication systems 100, time slots may be further divided into multiple micro-time slots associated with one or more symbols. Excluding the cyclic prefix, each symbol period may be associated with one or more (e.g., The duration of a symbol period is associated with a (number) sampling period. The duration of a symbol period can depend on the subcarrier spacing or the operating frequency band.

[0076] A subframe, time slot, micro-time slot, or symbol can be the smallest scheduling unit of the wireless communication system 100 (e.g., in the time domain) and can be referred to as a transmission time interval (TTI). In some examples, the duration of the TTI (e.g., the number of symbol periods in the TTI) can be variable. Additionally or alternatively, the smallest scheduling unit of the wireless communication system 100 can be dynamically selected (e.g., in a burst of shortened TTIs (sTTIs)).

[0077] Depending on the technology, carriers can be used to multiplex physical channels for communication. One or more of Time Division Multiplexing (TDM), Frequency Division Multiplexing (FDM), or hybrid TDM-FDM techniques can be used, for example, to multiplex physical control channels and physical data channels for signaling via a downlink carrier. The control region (e.g., control resource set (CORESET)) of the physical control channel can be defined by a set of symbol periods and can extend across the system bandwidth of the carrier or a subset of that bandwidth. One or more control regions (e.g., CORESET) can be configured for a set of UEs 115. For example, one or more UEs in UE 115 can monitor or search for control regions to obtain control information based on one or more search space sets, and each search space set can include one or more control channel candidates in one or more aggregation levels arranged in a concatenated manner. The aggregation level of control channel candidates can refer to the amount of control channel resources (e.g., control channel elements (CCEs)) associated with coded information for a control information format having a given payload size. The search space set may include: a common search space set configured to transmit control information to multiple UEs 115, and a UE-specific search space set used to transmit control information to a specific UE 115.

[0078] In some examples, network entity 105 (e.g., base station 140, RU 170) may be mobile, and thus provide communication coverage to 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 use the same or different radio access technologies to provide coverage for various coverage areas 110.

[0079] Some UEs 115 (such as MTC or IoT devices) can be low-cost or low-complexity devices and can provide automated communication between machines (e.g., via machine-to-machine (M2M) communication). M2M communication or MTC can refer to data communication technologies that allow devices to communicate with each other or with network entities 105 (e.g., base station 140) without human intervention. In some examples, M2M communication or MTC may include communication from devices with integrated sensors or meters to measure or acquire information and relay such information to a central server or application that uses the information or presents it to people interacting with the application. Some UEs 115 may be designed to collect information or enable automated behavior of machines or other devices. Examples of applications for MTC devices include: smart metering, inventory monitoring, water level monitoring, equipment monitoring, healthcare monitoring, wildlife monitoring, weather and geographic event monitoring, queue management and tracking, remote security sensing, physical access control, and transaction-based commercial charging. In one aspect, the techniques disclosed herein are applicable to MTC or IoT UEs. MTC or IoT UE can include MTC / enhanced MTC (eMTC, also known as CAT-M, Cat M1) UE, NB-IoT (also known as CAT NB1) UE, and other types of UE. eMTC and NB-IoT can refer to future technologies that can evolve from or are based on these technologies. For example, eMTC can include FeMTC (further eMTC), eFeMTC (further enhanced eMTC), and mMTC (massive MTC), while NB-IoT can include eNB-IoT (enhanced NB-IoT) and FeNB-IoT (further enhanced NB-IoT).

[0080] Wireless communication system 100 may be configured to support ultra-reliable communication or low-latency communication, or various combinations thereof. For example, 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 prioritizing services, and such services may be used for public safety or general business applications. The terms “ultra-reliable,” “low-latency,” and “ultra-reliable low-latency” are used interchangeably herein.

[0081] In some examples, UE 115 may be configured to support direct communication with other UE 115s via device-to-device (D2D) communication link 135 (e.g., according to peer-to-peer (P2P), D2D, or sidelink protocols). In some examples, one or more UE 115s performing D2D communication in a group may be within the coverage area 110 of network entity 105 (e.g., base station 140, RU 170), which may support aspects of such D2D communication configured (e.g., scheduled by network entity 105). In some examples, one or more UE 115s in such a group may be outside the coverage area 110 of network entity 105, or may otherwise be unable or not configured to receive transmissions from network entity 105. In some examples, the group of UE 115s communicating via D2D communication may support a one-to-many (1:M) system, where each UE 115 transmits to each of the other UE 115s in the group. In some examples, network entity 105 may facilitate the scheduling of resources used for D2D communication. In other examples, D2D communication may be performed between UEs 115 without involving network entity 105.

[0082] Core network 130 provides user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. 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), 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), packet data network (PDN) gateway (P-GW), or user plane function (UPF)) for routing packets or interconnecting to external networks. The control plane entity manages non-access stratum (NAS) functions, such as mobility, authentication, and bearer management of UE 115 served by network entity 105 (e.g., base station 140) associated with core network 130. User IP packets can be delivered through the user plane entity, which provides IP address allocation and other functions. The user plane entity may connect to one or more network operator IP services 150. IP services 150 may include access to the Internet, intranets, IP Multimedia Subsystem (IMS), or packet-switched streaming services.

[0083] Wireless communication system 100 can operate using one or more frequency bands in the range of 300 MHz to 300 GHz. Generally, the area from 300 MHz to 3 GHz is referred to as the Ultra High Frequency (UHF) band or decimeter band because the wavelength range is approximately one decimeter to one meter in length. UHF waves may be blocked or redirected by buildings and environmental features (which may be referred to as clusters), but these waves are sufficient to penetrate structures so that macrocells can provide service to UE 115 located indoors. Compared to communication using smaller frequencies and longer wavelengths in the lower frequency (HF) or very high frequency (VHF) portions of the spectrum below 300 MHz, communication using UHF waves can be associated with smaller antennas and shorter ranges (e.g., less than 100 km).

[0084] Wireless communication system 100 may utilize both licensed and unlicensed RF spectrum bands. For example, wireless communication system 100 may use unlicensed bands (such as the 5 GHz Industrial, Scientific, and Medical (ISM) band) to employ Licensed Assisted Access (LAA), LTE Unlicensed (LTE-U) radio access technology, or NR technology. When operating with unlicensed RF spectrum, devices such as network entity 105 and UE 115 may employ carrier sensing for collision detection and avoidance. In some examples, operation using unlicensed bands may be combined with component carriers operating with licensed bands based on carrier aggregation configurations (e.g., LAA). Operation using unlicensed spectrum may include downlink transmission, uplink transmission, P2P transmission, or D2D transmission, etc.

[0085] 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 techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communication, or beamforming. The antennas of network entity 105 or UE 115 may be located within one or more antenna arrays or antenna panels, which can support MIMO operation 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, the antennas or antenna arrays associated with network entity 105 may be located at different geographical locations. Network entity 105 may include an antenna array having a collection of multiple rows and columns of antenna ports that network entity 105 can use to support beamforming for communication with UE 115. Similarly, UE 115 may include 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 transmitted via the antenna ports.

[0086] Beamforming (also known as spatial filtering, directional transmission, or directional reception) is a signal processing technique that can be used at a transmitting or receiving device (e.g., network entity 105, UE 115) to shape or guide an antenna beam (e.g., a transmit beam, a receive beam) along a spatial path between the transmitting and receiving devices. Beamforming can be achieved by combining signals transmitted via antenna elements of an antenna array such that some signals propagating along a specific orientation relative to the antenna array experience constructive interference, while other signals experience destructive interference. Adjustments to the signals transmitted via the antenna elements may include applying amplitude shifts, phase shifts, or both to the signals carried via the antenna elements associated with the device. The adjustments associated with each of these antenna elements may be defined by a beamforming weight set associated with a specific orientation (e.g., relative to the antenna array of the transmitting or receiving device or relative to some other orientation).

[0087] In some examples of the wireless communication system 100, the UE 115 may use a defined set of resources to establish communication with the network entity 105. For example, each resource within this set may correspond to a specific direction in the spatial domain, enabling the UE 115 to communicate with the network entity, such as via a narrow beam. The UE 115 may utilize the resource set to estimate channel characteristics for various communications with the network entity. For example, the UE 115 may downsample an initial resource set (e.g., training resource set A) to a second resource set (e.g., training resource set B). Additionally, the UE 115 may support AI or ML techniques to train a model for predicting channel characteristics of the initial resource set. Such a training process may involve inputting measurements obtained using the second resource set. Thus, the UE 115 can use the trained model to predict channels for other resource sets; this process is called model inference. For example, the UE 115 may downsample a third resource set (e.g., inference resource set A) to a fourth resource set (e.g., inference resource set B). In some examples, UE 115 may input measurements from a fourth resource set into a trained model to predict channel characteristics of a third resource set. However, some wireless communication systems may not support a resource indexing framework to maintain consistency between the resources used to train the model (e.g., the first and second resource sets) and the resources used for channel prediction with the model (e.g., the third and fourth resource sets). If UE 115 does not determine consistency between resources in the first and third resource sets (e.g., training and inference resource set A) or between resources in the second and fourth resource sets (e.g., training and inference resource set B), UE 115 may experience degraded channel prediction quality when using the model.

[0088] UE 115 can utilize a resource indexing framework to maintain the correspondence between the resource set used to train the model and the resource set used to predict channel characteristics using the trained model. For example, UE 115 can maintain the correspondence between training resource set A and inference resource set A in terms of the number of resources, resource order, and beamform. For instance, UE 115 can use the first... k Resources to generate the first k Model output features, where UE 115 uses the first k The model outputs features to predict and infer the first feature of resource set A. k Channel characteristics of resources.

[0089] Additionally, UE 115 may maintain a correspondence between training resource set B and inference resource set B in terms of the number of resources, the order of resources, beamform, and subsample pattern. For example, training resource set B and inference resource set B may include the same subset of resources from training resource set A and inference resource set A, respectively. That is, if training resource set B includes a first set of entry IDs indicating a subset of resources from training resource set A, then inference resource set B may include the same set of associated entry IDs indicating a subset of resources from inference resource set A.

[0090] Additionally or alternatively, UE 115 may perform model training and model inference across multiple prediction cycles. In some cases, UE 115 may maintain the same subsample pattern for training resource set B and inference resource set B. In such cases, UE 115 may use training resource set A, training resource set B, inference resource set A, and inference resource set B for channel prediction at the same periodicity. In some cases, UE 115 may use different subsample patterns for training resource set B and inference resource set B across different channel prediction cycles. In such cases, UE 115 may use training resource set A and inference resource set A for channel prediction according to a first periodicity, and use training resource set B and inference resource set B according to a second periodicity that is an integer multiple of the first periodicity.

[0091] Figure 2 Examples of wireless communication systems 200 supporting techniques for beam management according to one or more aspects of this disclosure are shown. In some examples, wireless communication system 200 may implement aspects of wireless communication system 100. For example, wireless communication system 200 may include UE 115-a and network entity 105-a, which may be as described in reference... Figure 1 The corresponding examples of UE115-a 115 and network entity 105-a 105 are described.

[0092] In some examples, UE 115-a may use a defined set of resources to establish communication with network entity 105-a. For example, network entity 105-a may send a resource set configuration 205 to UE 115-a, which indicates multiple time and frequency resources that UE 115-a can use to perform wireless communication with network entity 105-a. In some cases, the resources of resource set configuration 205 may include one or more different resource types, such as Channel State Information Reference Signal (CSI-RS) resources, Synchronization Signal Block (SSB), Primary Synchronization Signal (PSS) resources, or Secondary Synchronization Signal (SSS) resources. Additionally, one or more resources may be associated with different pointing directions and corresponding spatial widths, such that a given resource can be used for communication across a portion of the spatial domain. That is, each resource may be associated with beam 255. Therefore, UE 115-a can use resources spanning a corresponding portion of the spatial domain to communicate with network entity 105-a (e.g., via narrow-to-narrow beam communication).

[0093] In some examples, UE 115-a may utilize resource sets to estimate channel characteristics for communication with network entity 105-a. For instance, as part of resource set configuration 205, network entity 105-a may designate a first number of ordered SSB and CSI-RS resources as training resource set A 215. Additionally, resource set configuration 205 may designate one or more ordered subsets of training resource set B 220. If multiple subsets of training resource set B 220 are configured or designated, each subset can be considered in different measurement cycles. Figure 2 In the examples, training resource set A 215 may include 16 ordered resources associated with 16 corresponding ordered beams 255, and multiple configurations of training resource set B 220. In some examples, each of the multiple configurations of training resource set B 220 may be a corresponding downsampled resource subset from training resource set A 215 associated with a corresponding subsample mode 260. For example, training resource set B 220 with mode 260-a may include the 3rd, 6th, 11th, and 12th resources from training resource set A 215. Therefore, each of subsample modes 260-a, 260-b, 260-c, and 260-d may be associated with a different resource subset from training resource set A 215.

[0094] In some examples, UE 115-a may use training resource set A 215 and training resource set B 220 to train a model (e.g., an AI model or an ML model) associated with predicted channel characteristics. For example, UE 115-a may perform a model training procedure 235, wherein the input to the model includes signal characteristics measured from training resource set B 220, and the output of the model includes at least the predicted channel characteristics associated with training resource set A 215. Additionally, by performing model training procedure 235, UE 115-a may determine a baseline truth label set 240. In some examples, the baseline truth label set 240 may be associated with the predicted channel characteristics used to train resource set A 215, wherein UE 115-a may determine the baseline truth label set 240 based on measurements collected when training the model using training resource set A 215 and training resource set B 220.

[0095] In some cases, UE 115-a may additionally utilize other resource sets for configuration. For example, as part of resource set configuration 205, network entity 105-a may indicate the amount of SSB and CSI-RS resources as inferred resource set A 225. Additionally, resource set configuration 205 may indicate one or more ordered subsets of inferred resource set B 230. If multiple subsets of inferred resource set B 230 are configured or indicated, each subset can be considered in different measurement cycles. Figure 2 In the example, inferred resource set A 225 may include 16 ordered resources associated with 16 corresponding ordered beams 255, and multiple configurations of inferred resource set B 230. In some examples, each of the multiple configurations of inferred resource set B 230 may be a corresponding downsampled resource subset associated with a corresponding subsample pattern 260 from training resource set A 215. Additionally or alternatively, inferred resource set A 225 may be a virtual resource set. For example, virtual resources may be examples of resources predicted by UE 115-a (e.g., based on AI or ML technology) rather than received from network devices as indications of physical resources (e.g., resources indicated in resource set configuration 205).

[0096] In some examples, UE 115-a may use a trained model (e.g., trained via model training procedure 235) to predict channel characteristics associated with inference resource set A 225 (e.g., model inference techniques). For example, UE 115-a may perform model inference procedure 245, where UE 115-a inputs signal characteristics measured from inference resource set B 230 into the trained model. Therefore, the output of the trained model may include predicted channel characteristics 250 associated with inference resource set A 225.

[0097] Therefore, UE 115-a can use a first set of resources to train an AI or ML model and use the trained model to predict the channel characteristics of a second set of resources. To improve the efficiency of channel prediction using the trained model, it may be advantageous for UE 115-a to utilize a resource indexing framework to maintain the correspondence between resources used for model training (e.g., training resource set A 215 and training resource set B 220) and resources used with the trained model (e.g., inference resource set A 225 and inference resource set B 230).

[0098] For example, UE 115-a can maintain the relationship between training resource set A 215 and inferred resource set A 225, such that the two resource sets can have a correspondence in terms of resource quantity, resource order, and beam shape. For example, training resource set A 215 can be configured through a first single SSB or CSI-RS resource set, wherein the training resource set A 215's first... k An entry can be defined as the first entry in a resource set. k Resources. Additionally, the inferred resource set A 225 can be configured via a second single SSB, CSI-RS, or virtual resource set, wherein the inferred resource set A 225's... k An entry can be defined as the first entry in a resource set. k Resources. To maintain a correspondence in terms of resource quantity, the training resource set A 215 and the inference resource set A 225 may include the same number of resources (e.g., Figure 2 (16 resources in the example).

[0099] To maintain correspondence in terms of resource ordering, UE 115-a can use the training resource set A 215's... k Resources are used to determine the first of the baseline truth labels 240. k The label, the k-th label, can be associated with the k-th label during model training process 235. k The output features are correlated. Therefore, UE 115-a can use the first [feature] during the model inference process 245. k Output features to determine and infer the first element in resource set A 225 k Predicted channel characteristics associated with resources.

[0100] To maintain correspondence in beam shape, each entry in training resource set A 215 and inference resource set A 225 may have a beam 255 that maintains the pointing direction and beamwidth between the two resource sets. For example, the pointing direction of a given beam 255 may be defined by the line-of-sight pointing direction of the given beam 255 at the center of the antenna panel of reference network entity 105-a. Therefore, if the first entry in training resource set A 215... k The resource is relative to the training resource set A in the 215th direction.j Resource offset value X (For example, the degree value in the Location Services (LCS) dimension), then infer the first value in resource set A 225. k Resources can be inferred relative to the first in resource set A 225 j Negative resource offset Z , among which value Z In value X Within the threshold. That is to say, Z Can be equal to X ± Y ,in Y >0 and Y <<| X Additionally, the beamwidth of a given beam 255 can be defined as the physical width of the given beam 255 in the spatial domain. Therefore, the beamwidth of the training resource set A 215... k Resources may have a first beamwidth, and the first beamwidth in resource set B 230 is inferred. k The resource may have a second beamwidth, wherein the difference between the first beamwidth and the second beamwidth satisfies a beamwidth tolerance threshold. That is, the difference may be less than a predefined tolerance threshold.

[0101] Additionally or alternatively, UE 115-a can maintain the training resource set A 215 and the inferred resource set A 225 across various radio devices and bandwidth portions (BWPs). That is, UE 115-a can maintain the correspondence between the training resource set A 215 and the inferred resource set A 225 as described herein across different serving cells, different BWPs, different DUs, different CUs, or combinations thereof.

[0102] Additionally or alternatively, UE 115-a can maintain the relationship between training resource set B 220 and inference resource set B 230, such that the two resource sets can have a correspondence in terms of resource quantity, resource order, beam shape, and subsample mode. For example, training resource set B 220 can be configured through a third single SSB or CSI-RS resource set, wherein the training resource set B 220 is the first k An entry can be defined as the first entry in a resource set. k Resources. Additionally, the inferred resource set B 230 can be configured via a fourth single SSB or CSI-RS resource set, wherein the inferred resource set B 230's... k An entry can be defined as the first entry in a resource set. k resource.

[0103] To maintain correspondence in terms of subsample patterns, the physical beams 255 associated with resources in training resource set B 220 can be a subset of the physical beams 255 associated with training resource set A 215. The patterns of the physical beams 255 associated with resources in training resource set B 220 can be identified based on entry IDs in training resource set A 215, which correspond to the selected physical beams 255 associated with resources in training resource set A 215. These entry IDs can be sorted in ascending order and mapped to resources in training resource set B 220, where the resource IDs in training resource set B 220 also maintain an ascending order relative to training resource set A 215. For example, pattern 260-a of training resource set B 220 may include beams 255 of training resource set A 215 associated with the 3rd entry ID and resource ID, the 6th entry ID and resource ID, the 11th entry ID and resource ID, and the 12th entry ID and resource ID (e.g., a subset {3, 6, 11, 12}). Similarly, physical beams 255 associated with resources in inference resource set B 230 may be a subset of physical beams 255 associated with inference resource set A 225, wherein the pattern of physical beams 255 associated with resources in inference resource set B 230 may be identified based on entry IDs of inference resource set A 225, which correspond to selected physical beams 255 associated with resources in inference resource set A 225. Such entry IDs may be sorted in ascending order and mapped to resources in inference resource set B 230, wherein resource IDs of inference resource set B 230 also maintain ascending order relative to inference resource set A 225. Therefore, the subsample pattern 260 between the training resource set B 220 and the inference resource set B 230 can be the same. For example, as Figure 2 As illustrated, if training resource set B 220 has pattern 260-a (e.g., subset {3, 6, 11, 12}), then it is inferred that resource set B 230 also follows pattern 260-a (e.g., subset {3, 6, 11, 12}).

[0104] To maintain correspondence in terms of resource ordering, UE 115-a can use the first [resource name] in training resource set B 220. k Resources are used to determine the association with the training of the model during the model training process 235. k The input values ​​of the input features. Additionally, the values ​​of the first feature in the trained model. k The input values ​​associated with the input features can be used during the model inference process 245 based on the inference resource set B 230. k Resource-related measurements are used to determine this.

[0105] To maintain a correspondence in terms of resource quantity, the training resource set B 220 and the inference resource set B 230 may include the same number of resources (e.g., Figure 2 (4 resources in the example).

[0106] To maintain the correspondence in beam shape, each entry in training resource set B 220 and inference resource set B 230 may have a beam 255 that maintains the pointing direction and beamwidth between the two resource sets. The process for maintaining the correspondence between the beam shapes of training resource set B 220 and inference resource set B 230 may be the same as the process described for training resource set A 215 and inference resource set A 225.

[0107] Additionally or alternatively, UE 115-a may maintain the correspondence between training resource set B 220 and inference resource set B 230 as described herein across different serving cells, different BWPs, different DUs, different CUs, or combinations thereof.

[0108] In some cases, UE 115-a can be configured using multiple sets of training resource set B 220 and multiple sets of inference resource set B 230. For example, a first set of training resource set B 220 can be associated with mode 260-a, a second set of training resource set B 220 can be associated with mode 260-b, a third set of training resource set B 220 can be associated with mode 260-c, and a fourth set of training resource set B 220 can be associated with mode 260-d. Similar sets can be defined for the multiple sets of inference resource set B 230. In such cases, UE 115-a can maintain the first set of training resource set B 220. n Training resource set B 220 and the n Infer the correspondence between subsample patterns 260 of resource set B 230. For example, ... Figure 2As illustrated, if the first training resource set B 220 has pattern 260-a (e.g., subset {3, 6, 11, 12}), then the first inference resource set B 230 can also follow pattern 260-a (e.g., subset {3, 6, 11, 12}). If the second training resource set B 220 has pattern 260-b (e.g., subset {1, 7, 9, 15}), then the second inference resource set B 230 can also follow pattern 260-b (e.g., subset {1, 7, 9, 15}). If the third training resource set B 220 has pattern 260-c (e.g., subset {2, 4, 10, 13}), then the third inference resource set B 230 can also follow pattern 260-c (e.g., subset {2, 4, 10, 13}). If the fourth training resource set B 220 has pattern 260-d (e.g., subset {1, 6, 8, 16}), then the third inference resource set B 230 can also follow pattern 260-d (e.g., subset {1, 6, 8, 16}).

[0109] To maintain correspondence in resource ordering, UE 115-a can use the... n The first training resource in set B 220 k Resources are used to determine the association with the training of the model during the model training process 235. k The input values ​​of the input features. Additionally, the values ​​of the first feature in the trained model. k The input values ​​associated with the input features can be used during the model inference process 245 based on the first... n Infer the resource set B 230's k Resource-related measurements are used to determine this.

[0110] In order to maintain the correspondence in the quantity of resources, the first n Training resource set B 220 and the n It can be inferred that resource set B 230 may include the same number of resources.

[0111] In order to maintain the correspondence in beam shape, the first n Training resource set B 220 and the n It is inferred that each entry in resource set B230 may have a beam 255, which maintains the pointing direction and beamwidth between the two resource sets. This is used to maintain the... n Training resource set B 220 and the n The process of inferring the correspondence between beam shapes in resource set B 230 is the same as the process described for training resource set A 215 and inference resource set A 225.

[0112] Additionally or alternatively, UE 115-a can maintain the following as described herein across different serving cells, different BWPs, different DUs, different CUs, or combinations thereof. n Training resource set B 220 and the n Infer the correspondence between resource set B 230.

[0113] In some examples, UE 115-a may be associated with multiple AI or ML models and may determine which model is used for the model training process 235 and the model inference process 245 based on model ID communication 210.

[0114] In some examples, UE 115-a may determine the model based on a process based on Online Model ID Lifecycle Management (LCM). For example, the same model functionality or model ID may be identified during two data collections used for model training process 235, where the correspondence between the training set and the inference set may be based on 3GPP predefined rules. In such examples, network entity 105-a may be an example of a network device capable of managing AI and ML functionality and model IDs associated with different AI and ML functionality or features. Thus, network entity 105-a may initiate a data collection process at UE 115-a by sending a first message indicating the model ID associated with an AI or ML feature or functionality. Based on the received indication of the model ID, UE 115-a may use the model associated with the model ID to perform model training process 235. Additionally or alternatively, network entity 105-a may send a second message indicating the model ID, whereby in response to the second message, UE 115-a performs a model inference process based on the model.

[0115] In some examples, UE 115-a may determine the model based on an offline model ID-based process. For example, the same model functionality or model ID may be identified during two data collections used in the model training process, where the correspondence between the training set and the inference set avoids 3GPP predefinition (e.g., coordination between network entity 105-a and UE 115-a outside of 3GPP). In such examples, UE 115-a may utilize a predefined set of logical model IDs or physical model IDs associated with the corresponding AI or ML functionality. Therefore, network entity 105-a may send a first set of model IDs supported by network entity 105-a to UE 115-a. Additionally, UE 115-a may send a second set of model IDs supported by UE 115-a to network entity 105-a, where UE 115-a may select model IDs that can be included in both the first and second sets of model IDs. Therefore, UE 115-a may use the model associated with the selected model ID for the model training process 235 and the model inference process 245.

[0116] Figure 3 Examples of a model training process 300 supporting techniques for beam management according to one or more aspects of this disclosure are shown. In some examples, the model training process 300 may implement aspects of wireless communication systems 100 and 200. For example, the model training process 300 may be an example of model training process 235, as referenced... Figure 2 As described. Additionally, training resource set A 315 can be an example of training resource set A 215, and training resource set B 310 can be an example of training resource set B 220, as referenced. Figure 2 As described. Therefore, Figure 3 The technique can be illustrated by one or more examples of UE 115 performing model training process 300.

[0117] like Figure 3 As illustrated, UE 115 may be associated with resource configuration 305. In some examples, resource configuration 305 may be an example of resource set configuration 205, such as... Figure 2 As described. For example, the resources of resource configuration 305 may include a non-zero power (NZP) CSI-RS resource set, which network entity 105 may (e.g., via RRC configuration) configure for UE 115 to communicate with a given serving cell of network entity 105. Figure 3 In the example, UE 115 can be configured using an initial set of 192 resources (e.g., resources #0 to #191), and UE 115 can use one or more of these 192 resources for training resource set A 315 and training resource set B 310. For example, UE 115 can configure 16 resources for training resource set A 315 (e.g., 16 NZP-CSI-RS resources selected from the NZP-CSI-RS resource set) and 4 resources for training resource set B 310 (e.g., 4 CSI-RS resources selected from the SSB resource set of resource configuration). Additionally, Figure 3 The number of resources used can be an example, and it should be understood that the number of resources used for resource configuration 305, training resource set A 315 and training resource set B 310 can be any number.

[0118] In some examples, training resource set A 315 may be associated with set A entry 330. That is, each of the 16 selected resources used to train resource set A 315 may correspond to a corresponding entry in set A entry 330. Additionally, UE 115 may maintain the correspondence between set A entry 330 and set A sequence 335. For example, UE 115 may select a given resource as the [number]th [entry] in set A entry 330. k Entries that allow the resource to be sorted as the 315th item in the training resource set A. k Resources. In some examples, each entry in set A 315 can be associated with a corresponding entry ID (e.g., resource #9 is associated with entry ID 1, resource #2 with entry ID 2, resource #78 with entry ID 15, and resource #52 with entry ID 16).

[0119] In some examples, the narrow beam set associated with resources in training resource set A 315 may be quasi-co-located (QCL) (e.g., within physical proximity that meets a threshold). Therefore, resources in training resource set A 315 may be divided into multiple QCL sources 355 (e.g., multiple SSBs), where resources of a given QCL source 355 are associated with the beam set corresponding to that QCL source 355. For example, resources in training resource set A 315 with entry IDs 1–4 may be associated with QCL source 355-a, resources in training resource set A 315 with entry IDs 5–8 may be associated with QCL source 355-b, resources in training resource set A 315 with entry IDs 9–12 may be associated with QCL source 355-c, and resources in training resource set A 315 with entry IDs 13–16 may be associated with QCL source 355-d.

[0120] In some cases, training resource set B 310 may include a subset of resources included in training resource set A 315. In some examples, each resource training resource set B 310 may be associated with a corresponding QCL 355. For example, as... Figure 3 As illustrated, training resource set B 310 may include resources associated with entry IDs 1, 5, 9, and 13 of training resource set A 315 (e.g., resources #9, #13, #23, and #15, respectively). See reference... Figure 2As described, UE 115 can map the subsampled entry IDs of training resource set A 315 to entries of training resource set B 310 in ascending order. That is, the first entry in set B entry 320 can be associated with entry ID 1 (e.g., resource #9) of set A entry 330, the second entry in set B entry 320 can be associated with entry ID 5 (e.g., resource #13) of set A entry 330, the third entry in set B entry 320 can be associated with entry ID 9 (e.g., resource #23) of set A entry 330, and the fourth entry in set B entry 320 can be associated with entry ID 13 (e.g., resource #15) of set A entry 330. UE 115 can maintain the correspondence between set B entry 320 and set B order 325. For example, UE 115 can select a given resource as the first subsampled entry in set B entry 320. k Entries that allow the given resource to be sorted into the training resource set B 310. k resource.

[0121] For reference Figure 2 As described, UE 115 can use resources in training resource set B 310 as input to model 340. In some examples, model 340 may be an example of an AI or ML model associated with a given AI or ML functionality. For example, the functionality of model 340 may be associated with predicting channel characteristics of a set of resources associated with a narrow beam set used for communication between UE 115 and network entity 105. Therefore, UE 115 can train model 340 to predict channel characteristics using training resource set A 315 and training resource set B 310.

[0122] According to model training procedure 300, UE 115 can measure the corresponding quality metric (e.g., the corresponding Layer 1 (L1)-Reference Signal Received Power (RSRP)) for each resource in training resource set B 310. In some examples, UE 115 can input the corresponding measurement of training resource set B 310 into model 340, where the first layer (L1) of the training resource set B 310 is the first layer (L1) of the training resource set B 310. m Resources are compatible with model 340. mInput features are associated. Therefore, model 340 can use each input feature to determine one or more output features. For example, based on the association of each resource in training resource set B 310 with a different QCL source 355, each input feature of model 340 can be associated with a corresponding QCL source 355. Therefore, model 340 can use the input features associated with a given QCL source 355 to generate output features for each resource of a given QCL source 355. For example, the model can use a first input feature to generate output features for resources 1–4 in training resource set A 315, a second input feature to generate output features for resources 5–8 in training resource set A 315, a third input feature to generate output features for resources 9–12 in training resource set A 315, and a fourth input feature to generate output features for resources 13–16 in training resource set A 315.

[0123] Additionally or alternatively, UE 115 may measure the corresponding quality metric (e.g., the corresponding L1-RSRP) for each resource in training resource set A 315. In some cases, UE 115 may use the corresponding quality metric for training resource set A 315 to determine a baseline truth label set 345. In some examples, UE 115 may use the output features of model 340 and the baseline truth label set 345 to determine a set 350 of predicted channel characteristics for training resource set A 315.

[0124] Therefore, UE 115 can train model 340 according to model training process 300. In some examples, Figure 4 An example of UE 115 using the trained model to perform the model inference process 400 is provided.

[0125] Figure 4 Examples of a model inference process 400 supporting techniques for beam management according to one or more aspects of this disclosure are shown. In some examples, the model inference process 400 may implement aspects of the wireless communication system 100, the wireless communication system 200, and the model training process 300. For example, the model inference process 400 may be an example of model inference process 245, as referenced... Figure 2 As described. Additionally, inferred resource set A 415 can be an example of inferred resource set A 225, and inferred resource set B 410 can be an example of inferred resource set B 230, as referenced. Figure 2 As described. Therefore, Figure 4 The technique can be illustrated by one or more examples of UE115 using the trained model 440 to perform the model inference process 400, wherein the trained model 440 is based on the model training process 300.

[0126] like Figure 4As illustrated, UE 115 may be associated with resource configuration 405. In some examples, resource configuration 405 may be an example of resource configuration 305, such as... Figure 2 As described. Based on the resource indexing framework, the number of resources, resource order, and beamform of inferred resource set A 415 can correspond to the number of resources, resource order, and beamform of training resource set A 315. Additionally, the number of resources, resource order, beamform, and subsample pattern of inferred resource set B 410 can correspond to the number of resources, resource order, beamform, and subsample pattern of training resource set B 310. For example, UE 115 can configure 16 resources for inferred resource set A415 (e.g., 16 NZP-CSI-RS resources selected from the NZP-CSI-RS resource set) and 4 resources for inferred resource set B 410 (e.g., 4 CSI-RS resources selected from the SSB resource set of resource configuration 405).

[0127] In some examples, the inferred resource set A 415 may be associated with set A entry 430. That is, each of the 16 selected resources used to infer resource set A 415 may correspond to a corresponding entry in set A entry 430. Additionally, UE 115 may maintain the correspondence between set A entry 430 and set A sequence 435. For example, UE 115 may select a given resource as the first entry in set A entry 430. k The entry allows the resource to be sorted as the inferred resource set A 415. k Resources. In some examples, each entry in set A 415 can be associated with a corresponding entry ID (e.g., resource #3 is associated with entry ID 1, resource #9 with entry ID 2, resource #18 with entry ID 15, and resource #57 with entry ID 16).

[0128] In some other examples, inferred resource set A 415 may be associated with virtual set A entry 445. For example, the UE may determine or predict the virtual resource set to include inferred resource set A 415, rather than including resources from resource configuration 405. For example, the UE may... k The virtual resource is identified as item 445 in virtual set A. k Entries. Additionally, UE 115 may maintain the correspondence between virtual set A entry 445 and virtual set A sequence 450. For example, UE 115 may select a given virtual resource as the first entry in set A entry 430. k The entry allows the virtual resource to be sorted as the 415th item in the inferred resource set A. k resource.

[0129] In some examples, the inference resource set A 415 can be divided into multiple QCL sources 460 (e.g., multiple SSBs), where resources of a given QCL source 460 are associated with the beam set corresponding to that QCL source 460. For example, resources in inference resource set A 415 with entry IDs 1–4 can be associated with QCL source 460-a, resources in inference resource set A 415 with entry IDs 5–8 can be associated with QCL source 460-b, resources in inference resource set A 415 with entry IDs 9–12 can be associated with QCL source 460-c, and resources in inference resource set A 415 with entry IDs 13–16 can be associated with QCL source 460-d. Based on the correspondence between training set A 315 and inference resource set A 415, QCL source 460 can correspond to QCL source 355.

[0130] In some cases, inferred resource set B 410 may include a subset of resources included in inferred resource set A 415. For example... Figure 4 As illustrated, inferred resource set B 410 may include resources associated with entry IDs 1, 5, 9, and 13 of inferred resource set A 415. (See reference...) Figure 2 As described, UE 115 can map the subsampled entry IDs of inferred resource set A 415 to entries in inferred resource set B 410 in ascending order. That is, the first entry in set B entry 420 can be associated with entry ID 1 (e.g., resource #3) of set A entry 430, the second entry in set B entry 420 can be associated with entry ID 5 (e.g., resource #7) of set A entry 430, the fourth entry in set B entry 420 can be associated with entry ID 9 (e.g., resource #2) of set A entry 430, and the fourth entry in set B entry 420 can be associated with entry ID 13 (e.g., resource #16) of set A entry 430. Similar to inferred resource set A 415, UE 115 can maintain the correspondence between set B entry 420 and set B order 425. For example, UE 115 can select a given resource as the first subsampled entry in set B entry 420. k Entries that allow the given resource to be sorted into the inferred resource set B 410. k Resources. Based on the correspondence between the training resource set B 310 and the inferred resource set B 410, the two resource sets B can be associated with the same subset of entry IDs (e.g., entry IDs 1, 5, 9, and 13) from their respective resource sets A.

[0131] For reference Figure 2As described, UE 115 can use resources in the inference resource set B 410 as input to the trained model 440 (e.g., model 340 trained according to model training process 300). For example, UE 115 can measure the corresponding quality metric (e.g., the corresponding L1-RSRP) for each resource in the inference resource set B 410. In some examples, UE 115 can input the corresponding measurement of the inference resource set B 410 into the model 440, where the first... m Resources can be used with model 440. m Input features are associated. Therefore, model 440 can use each input feature to determine one or more output features. For example, based on the association of each resource in inference resource set B 410 with a different QCL source 460, each input feature of model 440 can be associated with the corresponding QCL source 460. Therefore, model 440 can use the input features associated with a given QCL source 460 to generate output features for each resource of a given QCL source 460. For example, the model can use a first input feature to generate output features for resources 1–4 in inference resource set A 415, a second input feature to generate output features for resources 5–8 in inference resource set A 415, a third input feature to generate output features for resources 9–12 in inference resource set A 415, and a fourth input feature to generate output features for resources 13–16 in inference resource set A 415.

[0132] In some examples, the trained model 440's... n The output features can be used to determine the first feature in the inference resource set A415. n The predicted L1-RSRP measurement of the resources. Therefore, the UE can use the output features of the trained model 440 to determine the predicted set of channel characteristics of the inferred resource set A 415.

[0133] Figure 5 Examples of a resource pattern loop process 500 supporting techniques for beam management according to one or more aspects of this disclosure are shown. In some examples, the resource pattern loop process 500 may implement aspects of wireless communication system 100, wireless communication system 200, model training process 300, and model inference process 400. Figure 5 In the context of this discussion, several examples may be presented, in which the first resource set refers to the training resource set A 215, the second resource set refers to the inference resource set A 225, the third resource set refers to the training resource set B 220, and the fourth resource set refers to the inference resource set B 230.

[0134] like Figure 5As illustrated, UE 115 may utilize a set of beams 520 (e.g., 16 narrow beams 520 for communicating with network entities). Therefore, UE 115 may iterate through different sub-sample patterns 510 of the third and fourth resource sets to perform model training process 235 and model inference process 245 for the first and second resource sets. Each sub-sample pattern 510 of the third and fourth resource sets may be associated with a corresponding set of resources 505, where each resource 505 is associated with a corresponding beam 520. For example, sub-sample pattern 510-a may be associated with a first corresponding resource 505 associated with a corresponding beam 520, sub-sample pattern 510-b may be associated with a second corresponding resource 505 associated with a corresponding beam 520, sub-sample pattern 510-c may be associated with a second corresponding resource 505 associated with a corresponding beam 520, and sub-sample pattern 510-d may be associated with a second corresponding resource 505 associated with a corresponding beam 520.

[0135] In some implementations of the resource pattern cycle process 500, the UE 115 may use the same subsample pattern 510 for each prediction cycle. In such implementations, each of the first resource set, the second resource set, the third resource set, and the fourth resource set may be associated with the same periodicity 515.

[0136] In some specific implementations of the resource pattern loop process 500, the UE 115 may use a variable subsample pattern 510 across different prediction loops (e.g., as...). Figure 5 (As illustrated). In such a specific implementation, each of the first resource set, the second resource set, the third resource set, and the fourth resource set may correspond to the corresponding periodicity 515. In such examples, and It can be equal to the periodicity 515-a, and and It can be equal to the periodicity 515-b, where the periodicity 515-b can be an integer multiple of the periodicity 515-a. That is to say, ,in N It is an integer value greater than 1.

[0137] In some examples of the variable subsample pattern 510 loop, UE 115 can segment the third and fourth resource sets into N subsample patterns 510. N Subgroups. For example, UE 115 can... M Defined as the total number of resources in the third resource set, where the resource ID can be based on the entry ID in the resource set. Therefore, UE 115 can divide the third resource set into {resource #1, resource #2, ..., resource #[...]} M / N The first subgroup corresponding to ]}, and {resources#[ M / N +1, Resources#[ M / N +2,...,resources#[ M / N The second subgroup corresponding to ]×2}... and the group corresponding to {resource#( N -1)× M / N +1, Resources#( N -1)× M / N +2, ...,Resources# N The corresponding first N Subgroups. UE 115 can use the same subgroups as the third resource set to separate the fourth resource set.

[0138] Reference subgroups are separated, and any two resources in the same subgroup can be used with... The time-division offset is associated with the duration 520-a, which can be related to the time-division offset between two resources in the same subgroup. The maximum value is associated with it. Additionally, two resources associated with different subgroups can be related to... The time-division offset is associated with the duration 520-b, which can be correlated with the time-division offset between two resources in different subgroups. This is associated with the lowest value. Therefore, UE 115 is configurable. and The value of allows duration 520-b to be larger than the configured duration threshold than duration 520-a. In other words, the th... k The first in the subgroup j Resources and the k The first in the +1 subgroup j The time-division offset between resources can be equal to ,in It can be an integer value greater than 0, which is associated with the number of symbols or time slots that satisfy the configured duration threshold.

[0139] In some other examples of the variable subsample mode 510 loop, UE 115 can be configured to utilize multiple third resource sets and multiple fourth resource sets. For example, resource set configuration 205 can utilize training resource set B 220. N A set (e.g., N (a third resource set) and inferred resource set B 230 N A set (e.g., N UE 115 is configured using a fourth resource set. In this case, each resource set in the third and fourth resource sets can be associated with a set ID based on the associated entry ID set by the CSI resource. For configurations for the third resource set... N There are a total of sets that can exist. MOne resource. Therefore, the first set of the third resource set may include [ M / N [ ] resources, the second set of the third resource set may include [ M / N [The resource set] ... and the third resource set's [number]th [item]. N Sets may include One resource. The fourth resource set. N A set can be combined with a third resource set. N Each set has the same number of resources.

[0140] Refer to the third or fourth resource set N Collection configuration, any two resources in the same collection can be configured with The time-division offset is associated with the duration 520-a, which can be related to the time-division offset between two resources in the same subgroup. The maximum value is associated with it. Additionally, two resources associated with different subgroups can be related to... The time-division offset is associated with the duration 520-b, which can be correlated with the time-division offset between two resources in different subgroups. This is associated with the lowest value. Therefore, UE 115 is configurable. and The value of allows duration 520-b to be larger than the configured threshold than duration 520-a. That is, the th k The first in the set j Resources and the k The first in set +1 j The time-division offset between resources can be equal to ,in It can be an integer value greater than 0, which is associated with the number of symbols or time slots that satisfy the configured duration threshold.

[0141] Figure 6 An example of a process flow 600 supporting a technique for beam management according to one or more aspects of this disclosure is shown. In some examples, process flow 600 may implement aspects of wireless communication system 100, wireless communication system 200, model training process 300, model inference process 400, and resource pattern loop process 500. Process flow 600 includes UE 115-b and network entity 105-b, which may be as described in reference... Figures 1 to 5 The following describes corresponding examples of UE 115 and network entity 105. Alternative examples may be implemented, some of which may be performed in a different order than described or not at all. In some cases, steps may include additional features not mentioned below, or additional steps may be added. Furthermore, while process flow 600 illustrates the process between a single UE 115 and a single network entity 105, it should be understood that these processes can occur between any number of network devices and network device types.

[0142] At position 605, UE 115-b and network entity 105-b can communicate using the Model ID.

[0143] In some examples, model ID communication may be based on a technique using an online model ID (LCM). For instance, UE 115-b may receive a first indication of a model ID associated with a model used for training, wherein training the model may be based on receiving the first indication of the model ID. Therefore, after training the model, UE 115-b may receive a second indication of a model ID associated with the model, wherein UE 115-b may use the trained model to perform a model inference process based on receiving the second indication of the model ID.

[0144] In some examples, model ID communication may be based on a technology using offline model ID identification. For instance, UE 115-b may be configured using a first set of model IDs, where each model ID in the first set is associated with a different model. Therefore, UE 115-b may receive a second set of model IDs supported at network entity 105-b. UE 115-b may send an indication of the first set of model IDs to network entity 105-b. In such examples, UE 115-b may train the models included in the first set and second set of model IDs according to a model training process.

[0145] At 610, UE 115-b can receive control signaling including configuration indicating multiple resource sets associated with beam management. For example, the multiple resource sets may include at least a first resource set (e.g., training resource set A215) and a second resource set (e.g., training resource set B 220). In some examples, the first and second resource sets may be configured using a set of resource parameters for beam management, including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern. Additionally, the second resource set may include one or more subsets of resources from the first resource set.

[0146] In some examples, the multiple resource sets may also include a third resource set (e.g., inferred resource set A 225) and a fourth resource set (e.g., inferred resource set B 230). In some other examples, the resource set associated with beam management indicated by control signaling may include a fourth resource set, and the third resource set may include a virtual resource set (e.g., determined by UE 115-a).

[0147] In some examples, the fourth resource set may include one or more subsets of resources from the third resource set. In some examples, the third and fourth resource sets may be configured using a set of resource parameters for beam management. Additionally, the fourth resource set may be configured using a set of resource parameters for beam management, which includes a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern.

[0148] In some examples, a first resource set may include a set of resource entries each associated with a corresponding resource and corresponding entry ID in the first resource set; a second resource set may include a set of resource entries each associated with a corresponding resource and corresponding entry ID in the second resource set; a third resource set may include a set of resource entries each associated with a corresponding resource and corresponding entry ID in the third resource set; and a fourth resource set may include a set of resource entries each associated with a corresponding resource and corresponding entry ID in the fourth resource set.

[0149] In some examples, the first subsample pattern of the second resource set may include a first list of entry IDs corresponding to a subset of resources from the first resource set, and the second subsample pattern of the fourth resource set may include a second list of entry IDs corresponding to a subset of resources from the third resource set. In such examples, the first list and the second list of entry IDs may be the same list of entry IDs. Additionally, the first number of resources in the second resource set may be equal to the second number of resources in the fourth resource set.

[0150] In some examples, each resource entry in the second resource set can be associated with a corresponding beam according to a first beam shape, and each resource entry in the fourth resource set can be associated with a corresponding beam according to a second beam shape. For example, a first resource entry in the second resource set can be associated with a first beam including a first pointing direction and a first beamwidth. Additionally, a first resource entry in the fourth resource set can be associated with a second beam including a second pointing direction and a second beamwidth. Therefore, based on the consistency between the first and second beam shapes, the difference between the first and second pointing directions can satisfy a direction threshold, and the difference between the first and second beamwidths can satisfy a beamwidth threshold.

[0151] In some examples, a first resource set may be configured using a set of resource parameters for beam management, including a first quantity of resources, a first order of resources, and a first beam shape, and a third resource set may be configured using a set of resource parameters for beam management, including a second quantity of resources, a second order of resources, and a second beam shape. In such examples, the first quantity of resources in the first resource set is equal to the second quantity of resources in the third resource set.

[0152] In some examples, each resource entry in the first resource set can be associated with a corresponding beam according to a first beam shape, and each resource entry in the third resource set can be associated with a corresponding beam according to a second beam shape. For example, a first resource entry in the first resource set can be associated with a first beam including a first pointing direction and a first beamwidth. Additionally, a first resource entry in the third resource set can be associated with a second beam including a second pointing direction and a second beamwidth. Therefore, based on the consistency between the first beam shape and the second beam shape, the difference between the first pointing direction and the second pointing direction can satisfy a direction threshold, and the difference between the first beamwidth and the second beamwidth can satisfy a beamwidth threshold.

[0153] In some examples, the first resource set, the second resource set, the third resource set, and the fourth resource set can be used across multiple network entities 105, bandwidth, serving cells, or combinations thereof.

[0154] In some examples, each of the first, second, third, and fourth resource sets may be associated with the same periodicity.

[0155] In some examples, a first resource set may be associated with a first periodicity, a second resource set may be associated with a second periodicity, a third resource set may be associated with a third periodicity, and a fourth resource set may be associated with a fourth periodicity. In such examples, the first periodicity may be equal to the third periodicity, and the second periodicity may be equal to the fourth periodicity, wherein the fourth periodicity and the second periodicity may be integer multiples of the first periodicity and the second periodicity.

[0156] In some cases, a second resource set may include a single resource subset of a first resource set, and a fourth resource set may include a single resource subset of a third resource set. In such cases, UE 115-b may segment the second and fourth resource sets into a number of subgroups equal to integer multiples of each other. In some examples, the duration between two resources in the same resource subgroup of the first resource set may be equal to the first duration, and the duration between two resources in adjacent resource subgroups of the first resource set may be equal to the second duration. In such examples, the second duration may be a duration threshold greater than the first duration.

[0157] In some cases, a second resource set can be configured with the number of resource subsets of the first resource set, and a fourth resource set can be configured with the same number of resource subsets of the third resource set, where an integer multiple of the number of resource subsets is equal to the number of resource subsets. In such cases, the duration between two resources in the same resource subset of the first resource set can be equal to the first duration, and the duration between two resources in adjacent resource subsets of the first resource set can be equal to the second duration. Therefore, the second duration can be a duration threshold greater than the first duration.

[0158] At 615, UE 115-b may perform a model training procedure (e.g., model training procedure 300). For example, UE 115-b may train a model based on a first resource set and a second resource set, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and the output of the model may include a first set of predicted channel characteristics associated with the first resource set.

[0159] In some examples, UE 115-b may determine a first set of input values ​​for the model based on measurements of a second resource set during model training. For example, a first input value in the first set of input values ​​may be associated with a first entry in the second resource set. Additionally, UE 115-b may determine a set of output values ​​for the model based on the input values ​​of the second resource set during model training, wherein a first output value in the set of output values ​​may be associated with a first entry in the first resource set.

[0160] In some examples, at 620, UE 115-b may determine the set of virtual resources for the third resource set. For example, UE 115-b may predict or determine the set of virtual resources to be used as the third resource set, instead of using resources configured as part of the resource set configuration at 610.

[0161] At 625, UE 115-b may perform a model inference process (e.g., model inference process 400). For example, UE 115-b may obtain a second set of predicted channel characteristics associated with a third resource set based on a trained model, wherein the input to the trained model may include a set of measured channel characteristics associated with a fourth resource set.

[0162] In some examples, UE 115-b can use the trained model to determine a second set of input values ​​for the model based on measurements of a fourth resource set. For example, a first input value in the second set of input values ​​may be associated with a first entry in the fourth resource set. Additionally, UE 115-b can use the set of output values ​​from the trained model to determine the corresponding predicted channel characteristics for each resource entry in a third resource set. For example, a first output value may be used to determine the predicted channel characteristics of a first resource entry in the third resource set based on the consistency between a first order and a second order of resources.

[0163] Figure 7A block diagram 700 illustrates a device 705 supporting techniques for beam management according to one or more aspects of this disclosure. Device 705 may be an example of aspects of UE 115 as described herein. Device 705 may include a receiver 710, a transmitter 715, and a communication manager 720. Device 705, or one or more components of device 705 (e.g., receiver 710, transmitter 715, and communication manager 720), may include at least one processor that may be coupled to at least one memory to individually or jointly support or implement the described techniques. Each of these components may communicate with each other (e.g., via one or more buses).

[0164] Receiver 710 may provide components for receiving 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 technologies used for beam management). The information may be transmitted to other components of device 705. Receiver 710 may utilize a single antenna or a collection of multiple antennas.

[0165] Transmitter 715 may provide components for transmitting signals generated by other components of device 705. For example, transmitter 715 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 techniques used for beam management). In some examples, transmitter 715 may be co-located with receiver 710 in a transceiver module. Transmitter 715 may utilize a single antenna or a collection of multiple antennas.

[0166] The communication manager 720, receiver 710, transmitter 715, or various combinations thereof, or various components thereof, may be examples of components used to perform various aspects of the techniques for beam management as described herein. For example, the communication manager 720, receiver 710, transmitter 715, or various combinations thereof, or components thereof, may be able to perform one or more of the functions described herein.

[0167] In some examples, the communication manager 720, receiver 710, transmitter 715, or various combinations or components thereof may be implemented in hardware (e.g., in communication management circuitry). The hardware may include at least one of the following: a processor, digital signal processor (DSP), central processing unit (CPU), graphics processing unit (GPU), neural processing unit (NPU), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, microcontroller, discrete gate or transistor logic component, discrete hardware component, or any combination thereof, configured as or otherwise individually or collectively to support components for performing the functions described herein. In some examples, at least one processor and at least one memory coupled to said at least one processor may be configured to perform one or more of the functions described herein (e.g., instructions stored in at least one memory are executed individually or collectively by one or more processors).

[0168] Additionally or alternatively, the communication manager 720, receiver 710, transmitter 715, or various combinations or components thereof may be implemented in code executed by at least one processor (e.g., as communication management software). If implemented in code executed by at least one processor, the functionality of the communication manager 720, receiver 710, transmitter 715, or various combinations or components thereof may be performed by a general-purpose processor, DSP, CPU, GPU, NPU, ASIC, FPGA, microcontroller, or any combination of these or other programmable logic devices (e.g., configured as or otherwise individually or jointly to support components for performing the functions described in this disclosure).

[0169] In some examples, the communication manager 720 may be configured to use or otherwise cooperate with the receiver 710, transmitter 715, or both to perform various operations (e.g., receiving, acquiring, monitoring, outputting, transmitting). For example, the communication manager 720 may receive information from the receiver 710, transmit information to the transmitter 715, or be integrated with the receiver 710, transmitter 715, or both to acquire information, output information, or perform various other operations as described herein.

[0170] The communication manager 720 may support wireless communication according to examples disclosed herein. For example, the communication manager 720 may be capable of, configured to, or operable to support components for receiving control signaling including a configuration indicating a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set. The communication manager 720 may be capable of, configured to, or operable to support components for training a model based on the first and second resource sets, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes a first set of predicted channel characteristics associated with the first resource set. The communication manager 720 is capable of, configured to, or operable to support components for obtaining a second set of predicted channel characteristics associated with a third resource set based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern.

[0171] By including or configuring a communication manager 720 according to an example as described herein, device 705 (e.g., controlling receiver 710, transmitter 715, communication manager 720 or a combination thereof, or at least one processor otherwise coupled thereto) can support techniques for reducing processing, lowering power consumption, and utilizing communication resources more efficiently.

[0172] Figure 8 A block diagram 800 of a device 805 supporting techniques for beam management according to one or more aspects of this disclosure is shown. Device 805 may be an example of aspects of device 705 or UE 115 as described herein. Device 805 may include receiver 810, transmitter 815, and communication manager 820. Device 805, or one or more components of device 805 (e.g., receiver 810, transmitter 815, and communication manager 820), may include at least one processor that can be coupled to at least one memory to support the described techniques. Each of these components may communicate with each other (e.g., via one or more buses).

[0173] Receiver 810 may provide components for receiving 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 techniques used for beam management). The information may be transmitted to other components of device 805. Receiver 810 may utilize a single antenna or a collection of multiple antennas.

[0174] Transmitter 815 may provide components for transmitting signals generated by other components of device 805. For example, transmitter 815 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 techniques used for beam management). In some examples, transmitter 815 may be co-located with receiver 810 in a transceiver module. Transmitter 815 may utilize a single antenna or a collection of multiple antennas.

[0175] Device 805 or its various components may be examples of parts used to perform various aspects of the techniques for beam management as described herein. For example, communication manager 820 may include control signal monitoring component 825, model training component 830, model inference component 835, or any combination thereof. Communication manager 820 may be examples of aspects of communication manager 720 as described herein. In some examples, communication manager 820 or its various components may be configured to use or otherwise cooperate with receiver 810, transmitter 815, or both to perform various operations (e.g., receiving, acquiring, monitoring, outputting, transmitting). For example, communication manager 820 may receive information from receiver 810, transmit information to transmitter 815, or be integrated in combination with receiver 810, transmitter 815, or both to acquire information, output information, or perform various other operations as described herein.

[0176] The communication manager 820 may support wireless communication according to examples disclosed herein. The control signal monitoring component 825 is capable of, configured to, or operable to support components for receiving control signaling including configuration indicating a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set. The model training component 830 is capable of, configured to, or operable to support components for training a model based on the first and second resource sets, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes a first set of predicted channel characteristics associated with the first resource set. The model inference component 835 is capable of, configured to, or operable to support components for obtaining a second set of predicted channel characteristics associated with a third resource set based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the resource parameter set including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern.

[0177] Figure 9 A block diagram 900 is shown of a communication manager 920 supporting techniques for beam management according to one or more aspects of this disclosure. The communication manager 920 may be an example of aspects of the communication manager 720, communication manager 820, or both as described herein. The communication manager 920 or its various components may be examples of parts for performing various aspects of the techniques for beam management as described herein. For example, the communication manager 920 may include a control signal monitoring component 925, a model training component 930, a model inference component 935, a control signaling component 940, a resource set segmentation component 945, or any combination thereof. Each of these components, or its components or sub-components (e.g., one or more processors, one or more memories), may communicate directly or indirectly with each other (e.g., via one or more buses).

[0178] The communication manager 920 may support wireless communication according to examples disclosed herein. The control signal monitoring component 925 is capable of, configured to, or operable to support components for receiving control signaling including configuration indicating a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set. The model training component 930 is capable of, configured to, or operable to support components for training a model based on the first and second resource sets, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes a first set of predicted channel characteristics associated with the first resource set. The model inference component 935 is capable of, configured to, or operable to support components for obtaining a second set of predicted channel characteristics associated with a third resource set based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern.

[0179] In some examples, the second resource set includes a collection of resource entries, each associated with a corresponding resource in the second resource set. In some examples, the fourth resource set includes a collection of resource entries, each associated with a corresponding resource and a corresponding entry ID in the fourth resource set.

[0180] In some examples, the first subsample pattern of the second resource set includes a first list of entry identifiers corresponding to a subset of resources from the first resource set; and the second subsample pattern of the fourth resource set includes a second list of entry identifiers corresponding to a subset of resources from the third resource set.

[0181] In some examples, the first list of entry identifiers and the second list of entry identifiers are the same list of entry identifiers.

[0182] In some examples, the first quantity of resources in the second resource set is equal to the second quantity of resources in the fourth resource set.

[0183] In some examples, the model training component 930 is capable of, configured to, or operable to support components for determining a first set of input values ​​for the model based on measurements of a second resource set during model training, wherein a first input value in the first set of input values ​​is associated with a first entry in the second resource set. In some examples, the model inference component 935 is capable of, configured to, or operable to support components for determining a second set of input values ​​for the model using the trained model based on measurements of a fourth resource set, wherein a first input value in the second set of input values ​​is associated with a first entry in the fourth resource set.

[0184] In some examples, each resource entry in the second resource set is associated with a corresponding beam according to the first beam shape, and each resource entry in the fourth resource set is associated with a corresponding beam according to the second beam shape.

[0185] In some examples, a first resource entry of a second resource set is associated with a first beam including a first pointing direction and a first beamwidth; a first resource entry of a fourth resource set is associated with a second beam including a second pointing direction and a second beamwidth; and based on the consistency between the first beam shape and the second beam shape, the difference between the first pointing direction and the second pointing direction satisfies a direction threshold, and the difference between the first beamwidth and the second beamwidth satisfies a beamwidth threshold.

[0186] In some examples, the first and third resource sets are used across multiple network entities, bandwidths, or a combination of both.

[0187] In some examples, the first resource set includes a set of resource entries, each associated with a corresponding resource and a corresponding entry identifier in the first resource set; the first resource set is configured using a set of resource parameters for beam management, which includes a first number of resources, a first order of resources, and a first beam shape; the third resource set includes various sets of resource entries associated with corresponding resources and corresponding entry identifiers in the third resource set; the third resource set is configured using a set of resource parameters for beam management, which includes a second number of resources, a second order of resources, and a second beam shape.

[0188] In some examples, the first quantity of resources in the first resource set is equal to the second quantity of resources in the third resource set.

[0189] In some examples, model training component 930 is capable of, configured to, or operable to support components for determining a set of output values ​​of the model based on measurements of a first resource set during model training, wherein a first output value in the set of output values ​​is associated with a first entry of the first resource set. In some examples, model inference component 935 is capable of, configured to, or operable to support components for determining a corresponding predicted channel characteristic for each resource entry of a third resource set using the set of output values ​​of the model, wherein the first output value is used to determine the predicted channel characteristic of the first resource entry of the third resource set based on the consistency between a first order of resources and a second order of resources.

[0190] In some examples, each resource entry in the first resource set is associated with a corresponding beam according to a first beam shape, and each resource entry in the third resource set is associated with a corresponding beam according to a second beam shape.

[0191] In some examples, a first resource entry of a first resource set is associated with a first beam including a first pointing direction and a first beamwidth; a first resource entry of a third resource set is associated with a second beam including a second pointing direction and a second beamwidth; and based on the consistency between the first beam shape and the second beam shape, the difference between the first pointing direction and the second pointing direction satisfies a direction threshold, and the difference between the first beamwidth and the second beamwidth satisfies a beamwidth threshold.

[0192] In some examples, the first and third resource sets are used across multiple network entities, bandwidths, or a combination of both.

[0193] In some examples, each of the first, second, third, and fourth resource sets is associated with the same periodicity.

[0194] In some examples, the first resource set is associated with the first periodicity. In some examples, the second resource set is associated with the second periodicity. In some examples, the third resource set is associated with the third periodicity. In some examples, the fourth resource set is associated with the fourth periodicity.

[0195] In some examples, the first periodicity equals the third periodicity. In some examples, the second periodicity equals the fourth periodicity. In some examples, the fourth and second periodicities are integer multiples of the first and second periodicities.

[0196] In some examples, to support resource segmentation, the resource set segmentation component 945 can be configured or operated to support components for segmenting the second and fourth resource sets into a number of subgroups equal to integer multiples of the value.

[0197] In some examples, the duration between two resources in the same resource subgroup of the first resource set is equal to the first duration; the duration between two resources in adjacent resource subgroups of the first resource set is equal to the second duration; and the second duration is greater than the first duration by a duration threshold.

[0198] In some examples, the second resource set is configured with the number of resource subsets of the first resource set, and the fourth resource set is configured with the same number of resource subsets of the third resource set, with integer multiples equal to the number of resource subsets.

[0199] In some examples, the duration between two resources in the same resource subset of the first resource set is equal to the first duration; the duration between two resources in adjacent resource subsets of the first resource set is equal to the second duration; and the second duration is greater than the first duration by a duration threshold.

[0200] In some examples, the control signal monitoring component 925 is capable of, configured to, or operable to support components for receiving a first indication of a model identifier associated with a model, wherein training the model is based on receiving the first indication of the model identifier. In some examples, the control signal monitoring component 925 is capable of, configured to, or operable to support components for receiving a second indication of a model identifier associated with a model after training the model, wherein obtaining a second set of predicted channel characteristics associated with a third resource set is based on receiving the second indication of the model identifier.

[0201] In some examples, a first set of model identifiers is configured, and each model identifier in the first set of model identifiers is associated with a different model. In some examples, the first model identifier in the first set of model identifiers is associated with a model.

[0202] In some examples, the control signal monitoring component 925 is capable of, configured to, or operable to support components for receiving a second set of model identifiers supported at the network entity. In some examples, the control signaling component 940 is capable of, configured to, or operable to support components for sending an indication to the network entity of a first set of model identifiers, wherein training the model is based on first model identifiers associated with the model included in both the first set and the second set of model identifiers.

[0203] In some examples, the set of multiple resource sets associated with beam management indicated by control signaling also includes a third resource set and a fourth resource set.

[0204] In some examples, the set of multiple resource sets associated with beam management indicated by control signaling also includes a fourth resource set, and the third resource set includes a set of virtual resources.

[0205] Figure 10 A diagram of a system 1000 including a device 1005 supporting techniques for beam management, according to one or more aspects of this disclosure, is shown. Device 1005 may be an example of device 705, device 805, or UE 115 as described herein, or may include components thereof. Device 1005 may communicate with one or more network entities 105, one or more UEs 115, or any combination thereof (e.g., wirelessly). Device 1005 may include components for bidirectional voice and data communication, including components for transmitting and receiving communications, such as a communication manager 1020, an input / output (I / O) controller 1010, a transceiver 1015, an antenna 1025, at least one memory 1030, code 1035, and at least one processor 1040. These components may communicate electronically via one or more buses (e.g., bus 1045) or be coupled in other ways (e.g., operational ground, communication ground, functional ground, electronic ground, electrical ground).

[0206] I / O controller 1010 manages the input and output signals of device 1005. I / O controller 1010 can also manage peripheral devices not integrated into device 1005. In some cases, I / O controller 1010 may represent a physical connection or port to an external peripheral device. In some cases, I / O controller 1010 may utilize an operating system such as iOS. ® ANDROID ® MS-DOS ® MS-WINDOWS ® OS / 2 ® UNIX ® LINUX ® Or another known operating system. Additionally or alternatively, the I / O controller 1010 may represent or interact with a modem, keyboard, mouse, touchscreen, or similar device. In some cases, the I / O controller 1010 may be implemented as part of one or more processors, such as at least one processor 1040. In some cases, a user may interact with the device 1005 via the I / O controller 1010 or via hardware components controlled by the I / O controller 1010.

[0207] In some cases, device 1005 may include a single antenna 1025. However, in other cases, device 1005 may have more than one antenna 1025, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. Transceiver 1015 may communicate bidirectionally via one or more antennas 1025 as described herein, or via a wired or wireless link. For example, transceiver 1015 may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. Transceiver 1015 may also include a modem for: modulating packets; providing the modulated packets to one or more antennas 1025 for transmission; and demodulating packets received from one or more antennas 1025. Transceiver 1015, or transceiver 1015 and one or more antennas 1025, may be an example of transmitter 715, transmitter 815, receiver 710, receiver 810, or any combination thereof or components thereof as described herein.

[0208] At least one memory 1030 may include random access memory (RAM) and read-only memory (ROM). At least one memory 1030 may store computer-readable, computer-executable code 1035, including instructions that, when executed by at least one processor 1040, cause device 1005 to perform the various functions described herein. Code 1035 may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, code 1035 may not be directly executable by at least one processor 1040, but may enable a computer (e.g., when compiled and executed) to perform the functions described herein. In some cases, among other things, at least one memory 1030 may also include a basic I / O system (BIOS) that controls basic hardware or software operations, such as interaction with peripheral components or devices.

[0209] At least one processor 1040 may include intelligent hardware devices (e.g., general-purpose processors, DSPs, CPUs, GPUs, NPUs, microcontrollers, ASICs, FPGAs, programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or any combination thereof). In some cases, at least one processor 1040 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated into at least one processor 1040. At least one processor 1040 may be configured to execute computer-readable instructions stored in memory (e.g., at least one memory 1030) to cause device 1005 to perform various functions (e.g., functions or tasks supporting techniques for beam management). For example, device 1005 or components of device 1005 may include at least one processor 1040 and at least one memory 1030 coupled to or coupled to at least one processor 1040, wherein at least one processor 1040 and at least one memory 1030 are configured to perform the various functions described herein. In some examples, at least one processor 1040 may include multiple processors, and at least one memory 1030 may include multiple memories. One or more of a plurality of processors may be coupled to one or more of a plurality of memories, which may be configured individually or collectively to perform the various functions described herein. In some examples, at least one processor 1040 may be a component of a processing system, which may refer to a system of machines (such as a series of machines), circuitry (including, for example, one or both of processor circuitry (which may include at least one processor 1040) and memory circuitry (which may include at least one memory 1030)) or components that receive or receive input and process the input to produce, generate or obtain a set of outputs. The processing system may be configured to perform one or more of the functions described herein. Thus, at least one processor 1040 or a processing system including at least one processor 1040 may be configured, configurable, or operable to cause device 1005 to perform one or more of the functions described herein. Furthermore, as described herein, “configured to,” “capable of being configured to,” and “capable of being operable to” are used interchangeably and may be associated with the ability to perform one or more of the functions described herein when executing code stored in at least one memory 1030 or otherwise.

[0210] The communication manager 1020 may support wireless communication according to examples disclosed herein. For example, the communication manager 1020 may be capable of, configured to, or operable to support components for receiving control signaling including a configuration indicating a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set. The communication manager 1020 may be capable of, configured to, or operable to support components for training a model based on the first and second resource sets, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes a first set of predicted channel characteristics associated with the first resource set. The communication manager 1020 is capable of, configured to, or operable to support components for obtaining a second set of predicted channel characteristics associated with a third resource set based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern.

[0211] By including or configuring a communication manager 1020 according to an example as described herein, device 1005 can support techniques for improving communication reliability, reducing latency, improving and reducing user experience related to processing, reducing power consumption, utilizing communication resources more efficiently, improving coordination between devices, extending battery life, and improving the utilization of processing power.

[0212] In some examples, the communication manager 1020 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or in cooperation with transceiver 1015, one or more antennas 1025, or any combination thereof. Although the communication manager 1020 is illustrated as a separate component, in some examples, one or more functions described with reference to the communication manager 1020 may be supported by or executed by at least one processor 1040, at least one memory 1030, code 1035, or any combination thereof. For example, code 1035 may include instructions that can be executed by at least one processor 1040 to cause device 1005 to perform various aspects of the beam management techniques described herein, or at least one processor 1040 and at least one memory 1030 may be otherwise configured to perform or support such operations individually or jointly.

[0213] Figure 11 A block diagram 1100 of a device 1105 supporting techniques for beam management according to one or more aspects of this disclosure is shown. Device 1105 may be an example of aspects of network entity 105 as described herein. Device 1105 may include a receiver 1110, a transmitter 1115, and a communication manager 1120. Device 1105, or one or more components of device 1105 (e.g., receiver 1110, transmitter 1115, and communication manager 1120), may include at least one processor that may be coupled to at least one memory to individually or jointly support or implement the described techniques. Each of these components may communicate with each other (e.g., via one or more buses).

[0214] Receiver 1110 may provide components for acquiring (e.g., receiving, determining, identifying) information (such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units)) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack). The information may be passed to other components of device 1105. In some examples, receiver 1110 may support acquiring information by receiving signals via one or more antennas. Additionally or alternatively, receiver 1110 may support acquiring information by receiving signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof.

[0215] Transmitter 1115 may provide components for outputting (e.g., transmitting, providing, conveying, transmitting) information generated by other components of device 1105. For example, transmitter 1115 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, transmitter 1115 may support outputting information by transmitting signals via one or more antennas. Additionally or alternatively, transmitter 1115 may support outputting information by transmitting signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof. In some examples, transmitter 1115 and receiver 1110 may be co-located in a transceiver, which may include or be coupled to a modem.

[0216] The communication manager 1120, receiver 1110, transmitter 1115, or various combinations thereof, or various components thereof, may be examples of components used to perform various aspects of the techniques for beam management as described herein. For example, the communication manager 1120, receiver 1110, transmitter 1115, or various combinations thereof, or components thereof, may be able to perform one or more of the functions described herein.

[0217] In some examples, the communication manager 1120, receiver 1110, transmitter 1115, or various combinations or components thereof may be implemented in hardware (e.g., in communication management circuitry). The hardware may include at least one of a processor, DSP, CPU, GPU, NPU, ASIC, FPGA, or other programmable logic device, microcontroller, discrete gate or transistor logic device, discrete hardware component, or any combination thereof, configured as or otherwise individually or collectively to support components for performing the functions described herein. In some examples, at least one processor and at least one memory coupled to said at least one processor may be configured to perform one or more of the functions described herein (e.g., instructions stored in at least one memory are executed individually or collectively by one or more processors).

[0218] Additionally or alternatively, the communication manager 1120, receiver 1110, transmitter 1115, or various combinations or components thereof may be implemented in code executed by at least one processor (e.g., as communication management software). If implemented in code executed by at least one processor, the functionality of the communication manager 1120, receiver 1110, transmitter 1115, or various combinations or components thereof may be performed by any combination of a general-purpose processor, DSP, CPU, GPU, NPU, ASIC, FPGA, microcontroller, or these or other programmable logic devices (e.g., configured as or otherwise individually or collectively to support components for performing the functions described in this disclosure).

[0219] In some examples, the communication manager 1120 may be configured to use or otherwise cooperate with the receiver 1110, the transmitter 1115, or both to perform various operations (e.g., receiving, acquiring, monitoring, outputting, transmitting). For example, the communication manager 1120 may receive information from the receiver 1110, transmit information to the transmitter 1115, or be integrated with the receiver 1110, the transmitter 1115, or both to acquire information, output information, or perform various other operations as described herein.

[0220] The communication manager 1120 may support wireless communication according to examples disclosed herein. For example, the communication manager 1120 may be, configured, or operable to support components for transmitting control signaling, the control signaling including a configuration indicating a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, wherein the first and second resource sets are associated with training a model at the UE. The communication manager 1120 is capable of, configured to, or operable to support components for transmitting an indication to a UE of a set of predicted channel characteristics associated with a third resource set based on measurements of a fourth resource set using a trained model, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern.

[0221] By including or configuring a communication manager 1120 according to an example as described herein, device 1105 (e.g., controlling receiver 1110, transmitter 1115, communication manager 1120 or a combination thereof, or at least one processor otherwise coupled thereto) can support techniques for reducing processing, lowering power consumption, and utilizing communication resources more efficiently.

[0222] Figure 12 A block diagram 1200 of a device 1205 supporting techniques for beam management according to one or more aspects of this disclosure is shown. Device 1205 may be an example of aspects of device 1105 or network entity 105 as described herein. Device 1205 may include receiver 1210, transmitter 1215, and communication manager 1220. Device 1205, or one or more components of device 1205 (e.g., receiver 1210, transmitter 1215, and communication manager 1220), may include at least one processor coupled to at least one memory to support the described techniques. Each of these components may communicate with each other (e.g., via one or more buses).

[0223] Receiver 1210 may provide components for acquiring (e.g., receiving, determining, identifying) information (such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units)) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack). The information may be passed to other components of device 1205. In some examples, receiver 1210 may support acquiring information by receiving signals via one or more antennas. Additionally or alternatively, receiver 1210 may support acquiring information by receiving signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof.

[0224] Transmitter 1215 may provide components for outputting (e.g., transmitting, providing, conveying, transmitting) information generated by other components of device 1205. For example, transmitter 1215 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, transmitter 1215 may support outputting information by transmitting signals via one or more antennas. Additionally or alternatively, transmitter 1215 may support outputting information by transmitting signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof. In some examples, transmitter 1215 and receiver 1210 may be co-located in a transceiver, which may include or be coupled to a modem.

[0225] Device 1205 or its various components may be examples of parts used to perform various aspects of the techniques for beam management as described herein. For example, communication manager 1220 may include control signaling component 1225 or any combination thereof. Communication manager 1220 may be examples of aspects of communication manager 1120 as described herein. In some examples, communication manager 1220 or its various components may be configured to use or otherwise cooperate with receiver 1210, transmitter 1215, or both to perform various operations (e.g., receiving, acquiring, monitoring, outputting, transmitting). For example, communication manager 1220 may receive information from receiver 1210, transmit information to transmitter 1215, or be integrated in combination with receiver 1210, transmitter 1215, or both to acquire information, output information, or perform various other operations as described herein.

[0226] The communication manager 1220 may support wireless communication according to examples disclosed herein. The control signaling component 1225 is capable of, configured to, or operable to support components for transmitting control signaling, the control signaling including a configuration indicating a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, and wherein the first and second resource sets are associated with a model trained at the UE. The control signaling component 1225 is capable of, configured to, or operable to support the transmission of an indication for the UE to obtain a set of predicted channel characteristics associated with a third resource set based on measurements of a fourth resource set using a trained model, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern.

[0227] Figure 13A block diagram 1300 is shown of a communication manager 1320 supporting techniques for beam management according to one or more aspects of this disclosure. The communication manager 1320 may be an example of aspects of the communication manager 1120, communication manager 1220, or both as described herein. The communication manager 1320 or its various components may be examples of components for performing various aspects of the techniques for beam management as described herein. For example, the communication manager 1320 may include control signaling components 1325 or any combination thereof. These components, or each of their components or sub-components (e.g., one or more processors, one or more memories), may communicate directly or indirectly with each other (e.g., via one or more buses), and this communication may include communication within protocol layers of a protocol stack, communication associated with logical channels of the protocol stack (e.g., between protocol layers of the protocol stack, within devices, components, or virtualization components associated with network entity 105, between devices, components, or virtualization components associated with network entity 105), or any combination thereof.

[0228] The communication manager 1320 may support wireless communication according to examples disclosed herein. The control signaling component 1325 is capable of, configured to, or operable to support components for transmitting control signaling, the control signaling including a configuration indicating a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, and wherein the first and second resource sets are associated with a model trained at the UE. In some examples, control signaling component 1325 is capable of, configured to, or able to operate to support components for transmitting an indication for the UE to obtain a set of predicted channel characteristics associated with a third resource set based on measurements of a fourth resource set using a trained model, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern.

[0229] Figure 14A diagram of a system 1400 including a device 1405 supporting techniques for beam management, according to one or more aspects of this disclosure, is shown. Device 1405 may be an example of device 1105, device 1205, or network entity 105 as described herein, or may include components thereof. Device 1405 may communicate with one or more network entities 105, one or more UEs 115, or any combination thereof, and this communication may include communication via one or more wired interfaces, one or more wireless interfaces, or any combination thereof. Device 1405 may include components supporting output and obtaining communication, such as a communication manager 1420, a transceiver 1410, an antenna 1415, at least one memory 1425, code 1430, and at least one processor 1435. These components may communicate electronically or be otherwise coupled (e.g., operational ground, communication ground, functional ground, electronic ground, electrical ground) via one or more buses (e.g., bus 1440).

[0230] Transceiver 1410 may support bidirectional communication via a wired link, a wireless link, or both, as described herein. In some examples, transceiver 1410 may include a wired transceiver and be capable of bidirectional communication with another wired transceiver. Additionally or alternatively, in some examples, transceiver 1410 may include a wireless transceiver and be capable of bidirectional communication with another wireless transceiver. In some examples, device 1405 may include one or more antennas 1415 that are capable of transmitting or receiving wireless transmissions (e.g., concurrently). Transceiver 1410 may also include a modem for: modulating a signal; providing the modulated signal for transmission (e.g., via one or more antennas 1415, via a wired transmitter); receiving the modulated signal (e.g., from one or more antennas 1415, from a wired receiver); and demodulating the signal. In some embodiments, transceiver 1410 may include one or more interfaces, such as one or more interfaces coupled to one or more antennas 1415 configured to support various receive or acquire operations, or one or more interfaces coupled to one or more antennas 1415 configured to support various transmit or output operations, or combinations thereof. In some embodiments, transceiver 1410 may include one or more processors or one or more memory components or configured to be coupled to such processors or memory components, which are operable to perform or support operations based on received or acquired information or signals, or to generate information or other signals for transmission or other output, or any combination thereof. In some embodiments, transceiver 1410, or transceiver 1410 and one or more antennas 1415, or transceiver 1410 and one or more antennas 1415 and one or more processors or one or more memory components (e.g., at least one processor 1435, at least one memory 1425, or both) may be included in a chip or chip assembly mounted in device 1405. In some examples, transceiver 1410 may be able to operate to support communication via one or more communication links (e.g., communication link 125, backhaul communication link 120, midhaul communication link 162, fronthaul communication link 168).

[0231] At least one memory 1425 may include RAM, ROM, or any combination thereof. At least one memory 1425 may store computer-readable, computer-executable code 1430 including instructions that, when executed by one or more of at least one processor 1435, cause device 1405 to perform the various functions described herein. Code 1430 may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, code 1430 may not be directly executable by one of the at least one processor 1435, but may enable a computer (e.g., when compiled and executed) to perform the functions described herein. In some cases, at least one memory 1425 may also include a BIOS, among other things, that controls basic hardware or software operation, such as interaction with peripheral components or devices. In some examples, at least one processor 1435 may include multiple processors, and at least one memory 1425 may include multiple memories. One or more of the multiple processors may be coupled to one or more of the multiple memories, which may be configured individually or collectively to perform the various functions described herein (e.g., as part of a processing system).

[0232] At least one processor 1435 may include intelligent hardware devices (e.g., general-purpose processors, DSPs, ASICs, CPUs, GPUs, NPUs, FPGAs, microcontrollers, programmable logic devices, discrete gate or transistor logic units, discrete hardware components, or any combination thereof). In some cases, at least one processor 1435 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated into one or more of the at least one processor 1435. At least one processor 1435 may be configured to execute computer-readable instructions stored in memory (e.g., one or more memories in at least one memory 1425) to cause device 1405 to perform various functions (e.g., functions or tasks supporting techniques for beam management). For example, device 1405 or components of device 1405 may include at least one processor 1435 and at least one memory 1425 coupled to one or more of the at least one processor 1435, wherein at least one processor 1435 and at least one memory 1425 are configured to perform the various functions described herein. At least one processor 1435 may be an example of a cloud computing platform (e.g., one or more physical nodes and supporting software such as an operating system, virtual machine, or container instance) that can (e.g., by executing code 1430) host functions for performing the functions of device 1405. At least one processor 1435 may be any one or more suitable processors capable of executing scripts or instructions of one or more software programs stored in device 1405 (such as within one or more memories of at least one memory 1425). In some examples, at least one processor 1435 may include multiple processors, and at least one memory 1425 may include multiple memories. One or more of the multiple processors may be coupled to one or more of the multiple memories, which may be configured individually or collectively to perform the various functions described herein. In some examples, at least one processor 1435 may be a component of a processing system, which may refer to a system of machines (such as a series of machines), circuits (including, for example, one or both of processor circuitry (which may include at least one processor 1435) and memory circuitry (which may include at least one memory 1425)) or components that receive or acquire input and process the input to produce, generate, or acquire a set of outputs. The processing system may be configured to perform one or more of the functions described herein. Therefore, at least one processor 1435 or a processing system including at least one processor 1435 may be configured, configured to, or operated to cause the device 1405 to perform one or more of the functions described herein.Furthermore, as described herein, “configured to,” “capable of being configured to,” and “capable of operating to” are used interchangeably and may be associated with the ability to perform one or more of the functions described herein when executing code stored in at least one memory 1425 or otherwise.

[0233] In some examples, bus 1440 may support communication at protocol layers of the protocol stack (e.g., within a protocol layer). In some examples, bus 1440 may support communication associated with logical channels of the protocol stack (e.g., between protocol layers of the protocol stack), which may include communication performed within components of device 1405, or communication performed between different components of device 1405 that are co-addressable or may be located in different locations (e.g., where device 1405 may refer to a system in which one or more of communication manager 1420, transceiver 1410, at least one memory 1425, code 1430 and at least one processor 1435 may be located in one component of different components or partitioned between different components).

[0234] In some examples, the communication manager 1420 can manage (e.g., via one or more wired or wireless backhaul links) various aspects of communication with the core network 130. For example, the communication manager 1420 can manage the delivery of data communications by client devices, such as one or more UEs 115. In some examples, the communication manager 1420 can manage communication with other network entities 105 and may include a controller or scheduler for cooperating with other network entities 105 to control communication with UE 115. In some examples, the communication manager 1420 may support an X2 interface within LTE / LTE-A wireless communication network technology to provide communication between network entities 105.

[0235] The communication manager 1420 may support wireless communication according to examples disclosed herein. For example, the communication manager 1420 may be able to be configured or operated to support components for transmitting control signaling, the control signaling including a configuration indicating a set of multiple resource sets associated with beam management, wherein the set of multiple resource sets includes at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, wherein the first resource set and the second resource set are associated with training a model at the UE. The communication manager 1420 is capable of, configured to, or operable to support components for transmitting an indication to a UE of a set of predicted channel characteristics associated with a third resource set based on measurements of a fourth resource set using a trained model, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern.

[0236] By including or configuring a communication manager 1420 according to an example as described herein, device 1405 can support techniques for improving communication reliability, reducing latency, improving and reducing user experience related to processing, reducing power consumption, utilizing communication resources more efficiently, improving coordination between devices, extending battery life, and improving the utilization of processing power.

[0237] In some examples, the communication manager 1420 may be configured to use or otherwise coordinate with the transceiver 1410, one or more antennas 1415 (e.g., where applicable), or any combination thereof to perform various operations (e.g., receiving, acquiring, monitoring, outputting, transmitting). Although the communication manager 1420 is illustrated as a separate component, in some examples, one or more functions described with reference to the communication manager 1420 may be supported or performed by the transceiver 1410, one or more processors in at least one processor 1435, one or more memories in at least one memory 1425, code 1430, or any combination thereof (e.g., by a processing system including at least a portion of at least one processor 1435, at least one memory 1425, code 1430, or any combination thereof). For example, code 1430 may include instructions that can be executed by one or more processors in at least one processor 1435 to cause the device 1405 to perform various aspects of the beam management techniques described herein, or at least one processor 1435 and at least one memory 1425 may be otherwise configured to perform or support such operations individually or jointly.

[0238] Figure 15 A flowchart illustrating a method 1500 for beam management according to various aspects of this disclosure is shown. Operation of method 1500 may be implemented by a UE or its components as described herein. For example, operation of method 1500 may be implemented by, as referenced... Figures 1 to 10 The UE 115 described herein is used to perform this function. In some examples, the UE can execute a set of instructions to control the functional elements of the UE to perform the described function. Additionally or alternatively, the UE may use dedicated hardware to perform aspects of the described function.

[0239] At 1505, the method may include receiving control signaling including instructions for the configuration of a plurality of resource sets associated with beam management, wherein the plurality of resource sets include at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set. Operation of block 1505 may be performed according to examples as disclosed herein. In some examples, aspects of the operation of 1505 may be provided by reference to [reference needed]. Figure 9 The control signal monitoring component 925 described herein performs this function.

[0240] At 1510, the method may include training a model at least partially based on a first resource set and a second resource set, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes a first set of predicted channel characteristics associated with the first resource set. The operation of block 1510 may be performed according to examples as disclosed herein. In some examples, aspects of the operation of 1510 may be derived from references... Figure 9 The model training component 930 described is used to perform the training.

[0241] At 1515, the method may include obtaining a second set of predicted channel characteristics associated with a third resource set, at least in part based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern. The operation of block 1515 may be performed according to examples as disclosed herein. In some examples, aspects of the operation of 1515 may be derived from references... Figure 9 The model inference component 935 is used to perform the inference.

[0242] Figure 16 A flowchart illustrating a method 1600 for beam management, exemplifying various aspects of this disclosure, is shown. Operation of method 1600 may be implemented by a network entity or its components as described herein. For example, operation of method 1600 may be implemented by, as referenced... Figures 1 to 6 as well as Figures 11 to 14 The network entity described is used to perform this function. In some examples, the network entity may execute a set of instructions to control the functional elements of the network entity to perform the described function. Additionally or alternatively, the network entity may use dedicated hardware to perform aspects of the described function.

[0243] At 1605, the method may include sending control signaling including instructions for the configuration of a plurality of resource sets associated with beam management, wherein the plurality of resource sets include at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, and wherein the first and second resource sets are associated with a model trained at the UE. Operation of block 1605 may be performed according to examples as disclosed herein. In some examples, aspects of operation of 1605 may be provided by reference to [reference needed]. Figure 13The control signaling component 1325 described herein is used to execute this.

[0244] At 1610, the method may include transmitting an indication to the UE that it uses a trained model to obtain a set of predicted channel characteristics associated with a third resource set, based at least in part on measurements of a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using a set of resource parameters for beam management, and wherein the fourth resource set is configured using a set of resource parameters for beam management, the set of resource parameters including a second number of resources, a second order of resources, a second beam shape, and a second subsample pattern. Operation of block 1610 may be performed according to examples as disclosed herein. In some examples, aspects of operation of 1610 may be provided by reference to [reference needed]. Figure 13 The control signaling component 1325 described herein is used to execute this.

[0245] The following provides an overview of the various aspects of this disclosure: Aspect 1: A method for wireless communication at a UE, the method comprising: receiving control signaling including instructions for configuring a plurality of resource sets associated with beam management, wherein the plurality of resource sets include at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set; training a model at least in part based on the first resource set and the second resource set, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes a set of pre-measured channel characteristics associated with the first resource set. A first set of measured channel characteristics; and a second set of predicted channel characteristics associated with a third resource set, at least in part based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using the set of resource parameters for beam management, and wherein the fourth resource set is configured using the set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

[0246] Aspect 2: According to the method of aspect 1, wherein the second resource set includes a set of resource entries each associated with a corresponding resource in the second resource set, and the fourth resource set includes a set of resource entries each associated with a corresponding resource and a corresponding entry ID in the fourth resource set.

[0247] Aspect 3: According to the method of aspect 2, the first subsample pattern of the second resource set includes a first list of entry IDs corresponding to a subset of resources from the first resource set; and the second subsample pattern of the fourth resource set includes a second list of entry IDs corresponding to a subset of resources from the third resource set.

[0248] Aspect 4: According to the method of aspect 3, wherein the first list of entry IDs and the second list of entry IDs are the same list of entry IDs.

[0249] Aspect 5: The method according to any one of Aspects 2 to 4, wherein the first quantity of resources in the second resource set is equal to the second quantity of resources in the fourth resource set.

[0250] Aspect 6: The method according to any one of Aspects 2 to 5, the method further comprising: during training of the model, determining a first set of input values ​​of the model based at least in part on measurements of the second resource set, wherein a first input value in the first set of input values ​​is associated with a first entry of the second resource set; and using the trained model, determining a second set of input values ​​of the model based at least in part on measurements of the fourth resource set, wherein a first input value in the second set of input values ​​is associated with a first entry of the fourth resource set.

[0251] Aspect 7: The method according to any one of Aspects 2 to 6, wherein each resource entry of the second resource set is associated with a corresponding beam according to the first beam shape, and each resource entry of the fourth resource set is associated with a corresponding beam according to the second beam shape.

[0252] Aspect 8: According to the method of aspect 7, wherein the first resource entry of the second resource set is associated with a first beam including a first pointing direction and a first beamwidth; the first resource entry of the fourth resource set is associated with a second beam including a second pointing direction and a second beamwidth; and at least in part based on the consistency between the first beam shape and the second beam shape, the difference between the first pointing direction and the second pointing direction satisfies a direction threshold, and the difference between the first beamwidth and the second beamwidth satisfies a beamwidth threshold.

[0253] Aspect 9: The method according to any one of Aspects 1 to 8, wherein the first resource set and the third resource set are used across multiple network entities, bandwidths, or both.

[0254] Aspect 10: The method according to any one of Aspects 1 to 9, wherein the first resource set comprises a set of resource entries each associated with a corresponding resource and a corresponding entry ID in the first resource set; the first resource set is configured using the set of resource parameters for beam management, the set of resource parameters including a first quantity of resources, a first order of resources, and a first beam shape; the third resource set comprises a set of resource entries each associated with a corresponding resource and a corresponding entry ID in the third resource set; and the third resource set is configured using the set of resource parameters for beam management, the set of resource parameters including a second quantity of resources, a second order of resources, and a second beam shape.

[0255] Aspect 11: According to the method of aspect 10, the first quantity of resources in the first resource set is equal to the second quantity of resources in the third resource set.

[0256] Aspect 12: The method according to any one of Aspects 10 to 11, the method further comprising: during training of the model, determining a set of output values ​​of the model based at least in part on measurements of the first resource set, wherein a first output value in the set of output values ​​is associated with a first entry of the first resource set; and using the set of output values ​​of the model to determine a corresponding predicted channel characteristic for each resource entry of the third resource set, wherein the first output value is used to determine the predicted channel characteristic of the first resource entry of the third resource set based at least in part on the consistency between the first order of resources and the second order of resources.

[0257] Aspect 13: The method according to any one of Aspects 10 to 12, wherein each resource entry of the first resource set is associated with a corresponding beam according to the first beam shape, and each resource entry of the third resource set is associated with a corresponding beam according to the second beam shape.

[0258] Aspect 14: According to the method of aspect 13, wherein the first resource entry of the first resource set is associated with a first beam including a first pointing direction and a first beamwidth; the first resource entry of the third resource set is associated with a second beam including a second pointing direction and a second beamwidth; and at least in part based on the consistency between the first beam shape and the second beam shape, the difference between the first pointing direction and the second pointing direction satisfies a direction threshold, and the difference between the first beamwidth and the second beamwidth satisfies a beamwidth threshold.

[0259] Aspect 15: The method according to any one of Aspects 10 to 14, wherein the first resource set and the third resource set are used across multiple network entities, bandwidths, or both.

[0260] Aspect 16: The method according to any one of Aspects 1 to 15, wherein each of the first resource set, the second resource set, the third resource set, and the fourth resource set is associated with the same periodicity.

[0261] Aspect 17: The method according to any one of Aspects 1 to 16, wherein the first resource set is associated with a first periodicity, the second resource set is associated with a second periodicity, the third resource set is associated with a third periodicity, and the fourth resource set is associated with a fourth periodicity.

[0262] Aspect 18: According to the method of aspect 17, wherein the first periodicity is equal to the third periodicity, the second periodicity is equal to the fourth periodicity, and the fourth periodicity and the second periodicity are integer multiples of the first periodicity and the second periodicity.

[0263] Aspect 19: The method according to aspect 18, wherein the second resource set includes a single resource subset of the first resource set, and the fourth resource set includes a single resource subset of the third resource set, the method comprising: segmenting the second resource set and the fourth resource set into number subgroups equal to integer multiples thereof.

[0264] Aspect 20: According to the method of aspect 19, the duration between two resources in the same resource subgroup of the first resource set is equal to a first duration; the duration between two resources in adjacent resource subgroups of the first resource set is equal to a second duration; and the second duration is greater than the first duration by a duration threshold.

[0265] Aspect 21: The method according to any one of Aspects 18 to 20, wherein the second resource set is configured with respect to the number of resource subsets of the first resource set, and the fourth resource set is configured with respect to the same number of resource subsets of the third resource set, wherein the integer multiple is equal to the number of resource subsets.

[0266] Aspect 22: According to the method of aspect 21, the duration between two resources in the same resource subset of the first resource set is equal to a first duration; the duration between two resources in adjacent resource subsets of the first resource set is equal to a second duration; and the second duration is greater than the first duration by a duration threshold.

[0267] Aspect 23: The method according to any one of Aspects 1 to 22, the method further comprising: receiving a first indication for a model ID associated with the model, wherein training the model is at least partially based on receiving the first indication for the model ID; and after training the model, receiving a second indication for the model ID associated with the model, wherein obtaining a second set of predicted channel characteristics associated with the third resource set is at least partially based on receiving the second indication for the model ID.

[0268] Aspect 24: The method according to any one of Aspects 1 to 23, wherein a first set of model IDs is configured, each model ID in the first set of model IDs is associated with a different model, and a first model ID in the first set of model IDs is associated with the model.

[0269] Aspect 25: The method according to aspect 24, the method further comprising: receiving from a network entity a second set of model IDs supported at the network entity; and sending to the network entity an indication of the first set of model IDs, wherein training the model is based at least in part on the inclusion of the first model IDs associated with the model in both the first set of model IDs and the second set of model IDs.

[0270] Aspect 26: The method according to any one of aspects 1 to 25, wherein the plurality of resource sets associated with the beam management indicated by the control signaling further include the third resource set and the fourth resource set.

[0271] Aspect 27: The method according to any one of Aspects 1 to 26, wherein the plurality of resource sets associated with the beam management indicated by the control signaling further includes the fourth resource set, and the third resource set includes a virtual resource set.

[0272] Aspect 28: A method for wireless communication at a network, the method comprising: transmitting control signaling including instructions for configuring a plurality of resource sets associated with beam management, wherein the plurality of resource sets include at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, wherein the first resource set and the second resource set are associated with training a model at a UE; and transmitting instructions for the UE using the trained model. The model obtains an indication of a set of predicted channel characteristics associated with a third resource set based at least in part on measurements of a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using the set of resource parameters for beam management, and wherein the fourth resource set is configured using the set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

[0273] Aspect 29: A UE for wireless communication, the UE comprising: one or more memories storing processor-executable code; and one or more processors coupled to the one or more memories and capable of operating individually or jointly to execute the code, so that the UE performs a method according to any one of aspects 1 to 27.

[0274] Aspect 30: A UE for wireless communication, the UE including at least one component for performing the method according to any one of aspects 1 to 27.

[0275] Aspect 31: A non-transitory computer-readable medium storing code for wireless communication, the code including instructions executable by at least one processor to perform the method according to any one of aspects 1 to 27.

[0276] Aspect 32: A network for wireless communication, the network comprising: one or more memories storing processor-executable code; and one or more processors coupled to the one or more memories and capable of operating individually or jointly to execute the code to enable the network to perform the method according to aspect 28.

[0277] Aspect 33: A network for wireless communication, the network including at least one component for performing the method according to aspect 28.

[0278] Aspect 34: A non-transitory computer-readable medium storing code for wireless communication, the code including instructions executable by at least one processor to perform the method according to aspect 28.

[0279] It should be noted that the methods described herein describe possible specific implementations, and the operations and steps can be rearranged or otherwise modified, and other specific implementations are also possible. Furthermore, aspects from two or more of these methods can be combined.

[0280] While aspects of LTE, LTE-A, LTE-A Pro, or NR systems may be described for illustrative purposes, and the terms LTE, LTE-A, LTE-A Pro, or NR may be used in most of the description, the techniques described herein are also applicable to networks outside of LTE, LTE-A, LTE-A Pro, or NR networks. For example, the techniques described are applicable to a variety of other wireless communication systems, such as Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash OFDM, and other systems and radio technologies not explicitly mentioned herein, including future systems and radio technologies.

[0281] The information and signals described herein can be represented using any of a variety of different techniques and skills. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0282] The various exemplary frames and components described herein can be implemented or performed using a general-purpose processor, DSP, ASIC, CPU, GPU, NPU, FPGA, or other programmable logic device, discrete gate or transistor logic component, discrete hardware component, or any combination thereof, designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in alternative embodiments, a processor may be any processor, controller, microcontroller, or state machine. A 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 working in conjunction with a DSP core, or any other such configuration). Any function or operation described herein that can be performed by a processor may be performed by multiple processors capable of performing the described functions or operations individually or jointly.

[0283] The functionality described herein can be implemented using hardware, software executed by a processor, or any combination thereof. Software should be broadly interpreted as instructions, instruction sets, code, code segments, program code, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, or functions, whether referred to as software, firmware, middleware, microcode, hardware description languages, or other terms. When implemented using software executed by a processor, the functionality can be stored as one or more instructions or code on a computer-readable medium or transmitted using one or more instructions or code on a computer-readable medium. Other examples and specific implementations are within the scope of this disclosure and the appended claims. For example, due to the nature of software, the functionality described herein can be implemented using software executed by a processor, firmware, hardwired, or any combination thereof. Features implementing the functionality can also be physically located in various locations, including various portions distributed such that the functionality is implemented in different physical locations.

[0284] Computer-readable media includes both non-transitory computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. Non-transitory storage media can be any available medium accessible by a general-purpose or special-purpose computer. By way of example and not limitation, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, phase-change memory, compact disc (CD) ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, or any other non-transitory medium that can be used to carry or store desired program code components in the form of instructions or data structures and is accessible by a general-purpose or special-purpose computer or a general-purpose or special-purpose processor. Furthermore, any connection is appropriately referred to as computer-readable media. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included within the definition of computer-readable media. As used herein, disks and optical discs include CDs, laser discs, optical discs, digital multifunction discs (DVDs), floppy disks, and Blu-ray discs. Disks can magnetically reproduce data, and optical discs can optically reproduce data using lasers. Combinations of the above are also included within the scope of computer-readable media. Any function or operation described herein that can be performed by memory can be performed by multiple memories capable of performing the described function or operation individually or jointly.

[0285] As used herein (including in the claims), the word "or" in an enumeration of items (e.g., including enumerations of items ending with phrases such as "at least one of" or "one or more of") indicates an inclusive enumeration such that an enumeration of, for example, 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). Furthermore, as used herein, the phrase "based on" should not be construed as a reference to a closed set of conditions. For example, an example step described as "based on condition A" may be based on both condition A and condition B without departing from the scope of this disclosure. In other words, as used herein, the phrase "based on" should be interpreted in the same manner as the phrase "at least partially based on". As used herein, when the term "and / or" is used in a list of two or more items, it means that any one of the listed items may be used alone, or any combination of two or more of the listed items may be used. For example, if a composition is described as containing components A, B and / or C, then the composition may contain A alone; B alone; C alone; a combination of A and B; a combination of A and C; a combination of B and C; or a combination of A, B and C.

[0286] As used herein, including in claims, the article “a” preceding a noun is open-ended and is understood to refer to “at least one” or “one or more” of those nouns. Therefore, the terms “a,” “at least one,” “one or more,” and “at least one of one or more” are interchangeable. For example, where a claim enumerates “components” performing one or more functions, each of the individual functions may be performed by a single component or by any combination of multiple components. Thus, the term “component” having a characteristic or performing a function may refer to “at least one of one or more components” having a particular characteristic or performing a particular function. Subsequent references to a component introduced with the article “a” using the terms “the” or “the” can refer to any or all of the one or more components. For example, a component introduced with the article “a” can be understood to mean “one or more components,” and subsequent reference to “component” in a claim can be understood as equivalent to referring to “at least one of the one or more components.” Similarly, subsequent references to a component introduced with the terms “the” or “the” as “one or more components” can refer to any or all of the one or more components. For example, reference to "one or more components" in subsequent claims can be understood as equivalent to reference to "at least one of the one or more components".

[0287] The terms "determine" or "identify" encompass a variety of actions, and therefore, "determine" or "identify" can include calculation, computation, processing, derivation, investigation, lookup (such as by searching in a table, database, or other data structure), and ascertainment. Additionally, "determine" or "identify" can include receiving (such as receiving information or signaling, e.g., receiving information or signaling for determination, receiving information or signaling for identification) and accessing (such as accessing data in memory or accessing information). Furthermore, "determine" or "identify" can include parsing, obtaining, selecting, choosing, creating, and other similar actions.

[0288] In the accompanying drawings, similar components or features may have the same reference numerals. Furthermore, various components of the same type can be distinguished by adding a dash after the reference numeral and a second reference numeral to differentiate them. If only the first reference numeral is used in the description, the description can be applied to any of the similar components having the same first reference numeral, regardless of the second or other subsequent reference numerals.

[0289] The description herein, illustrated with reference to the accompanying drawings, describes an example configuration and does not represent all achievable examples or those within the scope of the claims. The term "example" as used herein means "serving as an example, instance, or illustration," not "preferred" or "advantageous over other examples." The detailed description includes specific details used to provide an understanding of the described techniques. However, these techniques can be practiced without these specific details. In some instances, known structures and devices are shown in block diagram form to avoid obscuring the concept of the described examples.

[0290] The description herein is provided to enable those skilled in the art to implement or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of this disclosure. Therefore, this disclosure is not limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A user equipment (UE), the user equipment (UE) comprising: One or more memories, wherein the one or more memories store processor-executable code; and One or more processors, coupled to one or more memories and capable of operating individually or jointly to execute the code to enable the UE: Receive control signaling, the control signaling including instructions for the configuration of a plurality of resource sets associated with beam management, wherein the plurality of resource sets include at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set; The model is trained at least in part on the first resource set and the second resource set, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes a first set of predicted channel characteristics associated with the first resource set; and A second set of predicted channel characteristics associated with a third resource set is obtained, at least in part, based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using the set of resource parameters for beam management, and wherein the fourth resource set is configured using the set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

2. The UE according to claim 1, wherein: The second resource set includes a set of resource entries, each associated with a corresponding resource in the second resource set, and The fourth resource set includes a set of resource entries, each associated with a corresponding resource and a corresponding entry identifier in the fourth resource set.

3. The UE according to claim 2, wherein: The first subsample pattern of the second resource set includes a first list of entry identifiers corresponding to a subset of resources from the first resource set; and The second subsample pattern of the fourth resource set includes a second list of entry identifiers corresponding to a subset of resources from the third resource set.

4. The UE of claim 3, wherein the first list of entry identifiers and the second list of entry identifiers are the same list of entry identifiers.

5. The UE according to claim 2, wherein the first quantity of resources in the second resource set is equal to the second quantity of resources in the fourth resource set.

6. The UE of claim 2, wherein the one or more processors are individually or jointly capable of further operating to execute the code to cause the UE to: During the training of the model, a first set of input values ​​for the model is determined at least in part based on measurements of the second resource set, wherein a first input value in the first set of input values ​​is associated with a first entry in the second resource set; and Using the trained model, a second set of input values ​​for the model is determined, at least in part based on measurements of the fourth resource set, wherein a first input value in the second set of input values ​​is associated with a first entry of the fourth resource set.

7. The UE of claim 2, wherein each resource entry of the second resource set is associated with a corresponding beam according to the first beam shape, and each resource entry of the fourth resource set is associated with a corresponding beam according to the second beam shape.

8. The UE according to claim 7, wherein: The first resource entry of the second resource set is associated with a first beam that includes a first pointing direction and a first beamwidth; The first resource entry of the fourth resource set is associated with a second beam that includes a second pointing direction and a second beamwidth; and Based at least in part on the consistency between the first beam shape and the second beam shape, the difference between the first pointing direction and the second pointing direction satisfies a direction threshold, and the difference between the first beamwidth and the second beamwidth satisfies a beamwidth threshold.

9. The UE of claim 1, wherein the first resource set and the third resource set are used across multiple network entities, bandwidths, or both.

10. The UE according to claim 1, wherein: The first resource set includes a set of resource entries, each associated with a corresponding resource and a corresponding entry identifier in the first resource set; The first resource set is configured using the resource parameter set for beam management, the resource parameter set including a first number of resources, a first order of resources, and a first beam shape; The third resource set includes a set of resource entries, each associated with a corresponding resource and a corresponding entry identifier within the third resource set; and The third resource set is configured using the resource parameter set for beam management, the resource parameter set including a second quantity of resources, a second order of resources, and a second beam shape.

11. The UE of claim 10, wherein the first quantity of resources in the first resource set is equal to the second quantity of resources in the third resource set.

12. The UE of claim 10, wherein the one or more processors are individually or jointly further operable to execute the code to cause the UE to: During the training of the model, the set of output values ​​of the model is determined at least in part based on measurements of the first resource set, wherein a first output value in the set of output values ​​is associated with a first entry of the first resource set; and The set of output values ​​of the model is used to determine the corresponding predicted channel characteristics of each resource entry in the third resource set, wherein the first output value is used to determine the predicted channel characteristics of the first resource entry in the third resource set based at least in part on the consistency between the first order of resources and the second order of resources.

13. The UE of claim 10, wherein each resource entry of the first resource set is associated with a corresponding beam according to a first beam shape, and each resource entry of the third resource set is associated with a corresponding beam according to a second beam shape.

14. The UE according to claim 13, wherein: The first resource entry of the first resource set is associated with a first beam that includes a first pointing direction and a first beamwidth; The first resource entry of the third resource set is associated with a second beam that includes a second pointing direction and a second beamwidth; and Based at least in part on the consistency between the first beam shape and the second beam shape, the difference between the first pointing direction and the second pointing direction satisfies a direction threshold, and the difference between the first beamwidth and the second beamwidth satisfies a beamwidth threshold.

15. The UE of claim 10, wherein the first resource set and the third resource set are used across multiple network entities, bandwidths, or both.

16. The UE of claim 1, wherein each of the first resource set, the second resource set, the third resource set, and the fourth resource set is associated with the same periodicity.

17. The UE of claim 1, wherein the first resource set is associated with a first periodicity, the second resource set is associated with a second periodicity, the third resource set is associated with a third periodicity, and the fourth resource set is associated with a fourth periodicity.

18. The UE of claim 17, wherein the first periodicity is equal to the third periodicity, the second periodicity is equal to the fourth periodicity, and the fourth periodicity and the second periodicity are integer multiples of the first periodicity and the second periodicity.

19. The UE according to claim 18, wherein, The one or more processors can operate individually or jointly to execute the code to enable the UE: The second resource set and the fourth resource set are divided into number subgroups equal to integer multiples of the specified value.

20. The UE according to claim 19, wherein: The duration between two resources in the same resource subgroup of the first resource set is equal to the first duration; The duration between two resources in adjacent resource subgroups of the first resource set is equal to the second duration; and The second duration is a duration threshold greater than the first duration.

21. The UE of claim 18, wherein the second resource set is configured with respect to the number of resource subsets of the first resource set, and the fourth resource set is configured with respect to the same number of resource subsets of the third resource set, wherein the integer multiple is equal to the number of resource subsets.

22. The UE according to claim 21, wherein: The duration between two resources in the same resource subset of the first resource set is equal to the first duration; The duration between two resources in adjacent resource subsets of the first resource set is equal to the second duration; and The second duration is a duration threshold greater than the first duration.

23. The UE of claim 1, wherein the one or more processors are individually or jointly further operable to execute the code to cause the UE to: Receive a first indication of a model identifier associated with the model, wherein training the model is based at least in part on receiving the first indication of the model identifier; and After training the model, a second indication is received for the model identifier associated with the model, wherein the second set of predicted channel characteristics associated with the third resource set is obtained at least in part based on the receipt of the second indication for the model identifier.

24. The UE of claim 1, wherein a first set of configuration model identifiers is configured, each model identifier in the first set of model identifiers is associated with a different model, and a first model identifier in the first set of model identifiers is associated with the model.

25. The UE of claim 24, wherein the one or more processors are individually or jointly further operable to execute the code to cause the UE to: Receive a second set of model identifiers supported at the network entity from the network entity; and Send an indication to the network entity for the first set of model identifiers, wherein training the model is based at least in part on the first model identifiers associated with the model being included in both the first set of model identifiers and the second set of model identifiers.

26. The UE of claim 1, wherein the plurality of resource sets associated with the beam management indicated by the control signaling further include the third resource set and the fourth resource set.

27. The UE of claim 1, wherein the plurality of resource sets associated with the beam management indicated by the control signaling further includes the fourth resource set, and the third resource set includes a virtual resource set.

28. A network comprising: One or more memories, wherein the one or more memories store processor-executable code; and One or more processors, coupled to one or more memories and capable of operating individually or jointly to execute the code to enable the network: Sending control signaling, the control signaling including instructions for the configuration of a plurality of resource sets associated with beam management, wherein the plurality of resource sets include at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, wherein the first resource set and the second resource set are associated with training a model at the user equipment (UE); and Sending an indication to the UE to obtain a set of predicted channel characteristics associated with a third resource set using a trained model based at least in part on measurements of a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using the set of resource parameters for beam management, and wherein the fourth resource set is configured using the set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

29. A method for conducting wireless communication at a user equipment (UE), the method comprising: Receive control signaling, the control signaling including instructions for the configuration of a plurality of resource sets associated with beam management, wherein the plurality of resource sets include at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape and a first subsample pattern, and wherein the second resource set includes one or more resource subsets of the first resource set; The model is trained at least in part on the first resource set and the second resource set, wherein the input to the model includes a set of measured channel characteristics associated with the second resource set, and wherein the output of the model includes a first set of predicted channel characteristics associated with the first resource set; and A second set of predicted channel characteristics associated with a third resource set is obtained, at least in part, based on a trained model, wherein the input to the trained model includes a set of measured channel characteristics associated with a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using the set of resource parameters for beam management, and wherein the fourth resource set is configured using the set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

30. A method for conducting wireless communication at a network, the method comprising: Sending control signaling, the control signaling including instructions for the configuration of a plurality of resource sets associated with beam management, wherein the plurality of resource sets include at least a first resource set and a second resource set, wherein the second resource set is configured using a set of resource parameters for the beam management, the set of resource parameters including a first number of resources, a first order of resources, a first beam shape, and a first subsample pattern, wherein the second resource set includes one or more resource subsets of the first resource set, wherein the first resource set and the second resource set are associated with training a model at the user equipment (UE); and Sending an indication to the UE to obtain a set of predicted channel characteristics associated with a third resource set using a trained model based at least in part on measurements of a fourth resource set, wherein the fourth resource set includes one or more resource subsets of the third resource set, wherein the third and fourth resource sets are configured using the set of resource parameters for beam management, and wherein the fourth resource set is configured using the set of resource parameters for beam management, the set of resource parameters including a second number of resources consistent with the first number of resources, a second order of resources consistent with the first order of resources, a second beam shape consistent with the first beam shape, and a second subsample pattern consistent with the first subsample pattern.

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