Model relationships and unified switching, activation and deactivation

Abstract

Methods, systems, and devices for wireless communication are described. A user equipment (UE) can identify a set of groups, each group of the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition under which each function is implemented by the UE. The UE can receive an indication of a handover from a first group to a second group of the set of groups based at least in part on the threshold change of the condition. The UE can switch each associated function implemented by the UE from a first machine learning model associated with the first set to a second machine learning model associated with the second set based at least in part on the indication of handover.

Description

Technical Field

[0001] The following content covers wireless communications, including model relationships and unified switching, activation, and deactivation. Background Art

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

[0003] The described technology relates to improved methods, systems, devices and apparatuses that support model relationships and unified switching, activation and deactivation. For example, the described technology provides for grouping machine learning models from different functions according to the conditions of the wireless network. For example, a user equipment (UE) can identify or otherwise determine a set of groups. Each group may generally include machine learning models for one or more functions implemented by the UE and the network entity (e.g., a first model for a first function, a second model for a second function, etc.). In each group, the machine learning models for different functions included in the group may each correspond to or otherwise be associated with a specific condition (e.g., a given Doppler condition, a delay spread condition, a non-line-of-sight (NLIOS) condition, a line-of-sight (LOS) condition, an indoor condition, an outdoor condition, a network feature including an antenna layout and a beamforming scheme, etc.). In some examples, the UE may define a grouping with a network entity, the network entity may define the grouping and notify the UE of the grouping, or the network entity and the UE may collaborate to define the grouping. This may enable the network entity to rely on a unified signaling technique, in which the network switches the UE from the first group to the second group based on a change in conditions. This can enable the network to switch from a current group corresponding to a first condition (e.g., an old condition) to a second group corresponding to a second condition (e.g., a new condition) for each function implemented in the wireless network. That is, registering models for different functions but corresponding to a given condition into a group can enable the network to configure, activate, and deactivate models for multiple functions based on conditions updated within the network.

[0004] Additionally or alternatively, when one or more machine learning models for a function within a group (e.g., a first machine learning model) fails to meet a performance threshold, this may indicate that the conditions associated with the group for the corresponding function may have changed, such that the current group of machine learning models is incorrect under the changed conditions. Therefore, the UE may send a report to the network that carries or otherwise conveys an indication that the group (e.g., identifying a particular group, a model within the group, or both) failed to meet the performance threshold. The network entity may optionally send a switching indication in response to the group failure report, the switching indication directing the UE to switch from the current group (e.g., the first group) to a different group (e.g., the second group) that more closely corresponds to the changed conditions.

[0005] A method for wireless communication at a UE is described. The method may include: identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; receiving an indication to switch from a first group in the set of groups to a second group based on a threshold change of the condition; and switching each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based on the indication to switch.

[0006] An apparatus for wireless communication at a UE is described. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executed by the processor to cause the apparatus to: identify a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; receive an indication to switch from a first group in the set of groups to a second group based on a threshold change of the condition; and based on the indication of the switch, switch each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group.

[0007] Another apparatus for wireless communication at a UE is described. The apparatus may include: a component for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; a component for receiving an indication to switch from a first group in the set of groups to a second group based on a threshold change of the condition; and a component for switching each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based on the indication of switching.

[0008] A non-transitory computer-readable medium storing code for wireless communication at a UE is described. The code may include instructions executable by a processor to: identify a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; receiving an indication to switch from a first group in the set of groups to a second group based on a threshold change of the condition; and based on the indication to switch, switching each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group.

[0009] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for sending an indication of a group set to a network entity to register the group set, wherein an indication of a switch may be received based on the registration.

[0010] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, an indication of a set of groups includes, for each model, a group identifier that can be unique for each group in the set of groups, and a shared group identifier between one or more machine learning models defines the group.

[0011] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, an indication of a set of groups includes, for each machine learning model, an associated model or function identifier, and the associated model or function identifier defines the group as including the machine learning model and an associated model or function corresponding to the associated model or function identifier.

[0012] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving an indication of a set of groups from a network entity, wherein the identification may be based on the indication of the set of groups.

[0013] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for identifying a group identifier for each machine learning model based on an indication of a set of groups, where the group identifier can be unique for each group in the set of groups, where each group can be defined by a shared group identifier between one or more machine learning models.

[0014] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for identifying an associated model or function identifier for each model based on an indication of a set of groups, wherein the associated model or function identifier defines the group as including a machine learning model and an associated model or function corresponding to the associated model or function identifier.

[0015] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, identifying a set of groups may include operations, features, components, or instructions for: identifying a first list of machine learning models corresponding to a first function and a second list of machine learning models corresponding to a second function; and identifying a first group based on a first machine learning model from the first list of machine learning models and a first machine learning model from the second list of machine learning models; and identifying a second group based on a second machine learning model from the first list of machine learning models and a second machine learning model from the second list of machine learning models.

[0016] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the indication may be received in radio resource control (RRC) signaling or in a medium access control-control element (MAC-CE).

[0017] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, identifying a set of groups may include operations, features, components, or instructions for: identifying an indication of switching from a first group to a second group for a first set of functions implemented by the UE based on a first field in an indication of switching; and identifying an indication of switching from a third group to a fourth group for a second set of functions implemented by the UE based on a second field in the indication of switching.

[0018] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the indication of the switch indicates that the first group can be deactivated, the second group can be activated, or both.

[0019] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, an indication of a handover may be received in UE-specific downlink control information (DCI), in a group-common DCI, or in a MAC-CE.

[0020] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for: determining that a function implemented by the UE according to a first machine learning model in a first group fails to meet a performance threshold; and sending a report to a network entity indicating that the first group fails to meet the performance threshold, wherein an indication for switching may be based on the report.

[0021] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for determining that a function implemented by the UE according to a second machine learning model in a second group satisfies a performance threshold, wherein the report identifies the second group as a preferred group.

[0022] In some examples of the methods, devices, and non-transitory computer-readable media described herein, the functions include at least one of the following: channel state information (CSI) feedback function, channel optimization function, beam management function, CSI-reference signal (RS) optimization function, demodulation reference signal (DMRS) function, physical layer function, timing function, synchronization function, spatial function, power management function, interference management function, or positioning function.

[0023] In some examples of the methods, devices, and non-transitory computer-readable media described herein, the conditions include at least one of the following: a Doppler condition, an angular velocity condition, a travel direction condition, a travel speed condition, a beam width condition, a beam condition, a power condition, an interference condition, a traffic load condition, a traffic mode condition, a traffic type condition, a reference signal configuration condition, a location condition, an antenna configuration condition, a coverage condition, a connection condition, or a change in one or more conditions.

[0024] A method for wireless communication at a network entity is described. The method may include: identifying a set of groups for UEs, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; determining that the condition for each function to be implemented by the UE has changed by at least a threshold change; and sending an indication for the UE to switch from a first group to a second group in the set of groups based on the threshold change in the condition.

[0025] An apparatus for wireless communication at a network entity is described. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executed by the processor to cause the apparatus to: identify a set of groups for UEs, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; determine that the condition for each function to be implemented by the UE has changed by at least a threshold change; and send an indication that the UE switches from a first group in the set of groups to a second group based on the threshold change in the condition.

[0026] Another apparatus for wireless communication at a network entity is described. The apparatus may include: means for identifying a set of groups for UEs, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; means for determining that the condition for each function to be implemented by the UE has changed by at least a threshold change; and means for sending an indication that the UE is switching from a first group to a second group in the set of groups based on the threshold change in the condition.

[0027] A non-transitory computer-readable medium storing code for wireless communication at a network entity is described. The code may include instructions executable by a processor to: identify a set of groups for UEs, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; determine that the condition for each function to be implemented by the UE has changed by at least a threshold change; and send an indication that the UE switches from a first group in the set of groups to a second group based on the threshold change in the condition.

[0028] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for sending an indication of a group set to a UE to register the group set.

[0029] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, an indication of a set of groups includes, for each machine learning model, a group identifier that can be unique for each group in the set of groups, and a shared group identifier between one or more machine learning models defines the group.

[0030] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, an indication of a set of groups includes, for each machine learning model, an associated model or function identifier, and the associated model or function identifier defines the group as including the machine learning model and an associated model or function corresponding to the associated model or function identifier.

[0031] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving an indication of a set of groups from a UE, wherein the identification may be based on the indication of the set of groups.

[0032] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for identifying a group identifier for each machine learning model based on an indication of a set of groups, where the group identifier can be unique for each group in the set of groups, where each group can be defined by a shared group identifier between one or more machine learning models.

[0033] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for identifying an associated model or function identifier for each model based on an indication of a set of groups, wherein the associated model or function identifier defines the group as including a machine learning model and an associated model or function corresponding to the associated model or function identifier.

[0034] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, identifying a set of groups may include operations, features, components, or instructions for: identifying a first list of machine learning models corresponding to a first function and a second list of machine learning models corresponding to a second function; and identifying a first group based on a first machine learning model from the first list of machine learning models and a first machine learning model from the second list of machine learning models; and identifying a second group based on a second machine learning model from the first list of machine learning models and a second machine learning model from the second list of machine learning models.

[0035] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the indication may be received in RRC signaling or in a MAC-CE.

[0036] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, sending an indication of a switch may include an operation, feature, component, or instruction for sending a first field and a second field in the indication of the switch, the first field indicating a switch from a first group to a second group for a first set of functions implemented by the UE, and the second field indicating a switch from a third group to a fourth group for a second set of functions implemented by the UE.

[0037] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the indication of the switch indicates that the first group can be deactivated, the second group can be activated, or both.

[0038] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, an indication of a handover may be sent in a UE-specific DCI, in a group-common DCI, or in a MAC-CE.

[0039] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving a report from a UE indicating that a function implemented by the UE according to a first machine learning model in the first group failed to meet a performance threshold, wherein an indication to switch to the second group may be based on the report.

[0040] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for determining, based on the report, that the UE satisfies a performance threshold for implementing functionality according to a second machine learning model in the second group, wherein an indication to switch to the second group may be sent based on the report.

[0041] In some examples of the methods, devices, and non-transitory computer-readable media described herein, the functions include at least one of the following: CSI feedback function, channel optimization function, beam management function, CSI-RS optimization function, DMRS function, physical layer function, timing function, synchronization function, spatial function, power management function, interference management function, or positioning function.

[0042] In some examples of the methods, devices, and non-transitory computer-readable media described herein, the conditions include at least one of the following: a Doppler condition, an angular velocity condition, a travel direction condition, a travel speed condition, a beam width condition, a beam condition, a power condition, an interference condition, a traffic load condition, a traffic mode condition, a traffic type condition, a reference signal configuration condition, a location condition, an antenna configuration condition, a coverage condition, a connection condition, or a change in one or more conditions.

[0043] A method for wireless communication at a UE is described. The method may include: identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; determining that a function implemented by the UE according to a first machine learning model in a first group fails to meet a performance threshold; and sending a report to a network entity indicating that the first group fails to meet the performance threshold.

[0044] An apparatus for wireless communication at a UE is described. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executed by the processor to cause the apparatus to: identify a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; determine that a function implemented by the UE according to a first machine learning model in a first group fails to meet a performance threshold; and send a report to a network entity indicating that the first group fails to meet the performance threshold.

[0045] Another apparatus for wireless communication at a UE is described. The apparatus may include: means for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementation of each function by the UE; means for determining that a function implemented by the UE according to a first machine learning model in a first group fails to meet a performance threshold; and means for sending a report to a network entity indicating that the first group fails to meet the performance threshold.

[0046] A non-transitory computer-readable medium storing code for wireless communication at a UE is described. The code may include instructions executable by a processor to: identify a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; determine that a function implemented by the UE according to a first machine learning model in a first group fails to meet a performance threshold; and send a report to a network entity indicating that the first group fails to meet the performance threshold.

[0047] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for: receiving an indication to switch from a first group in a set of groups to a second group based on a report; and switching a function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based on the indication of the switch.

[0048] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for sending an indication of the group set to a network entity to register the group set.

[0049] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving an indication of a set of groups from a network entity, wherein the identifying may be based on the indication.

[0050] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for determining that a function implemented by the UE according to a second machine learning model in a second group satisfies a performance threshold, wherein the report identifies the second group as a preferred group.

[0051] A method for wireless communication at a network entity is described. The method may include: identifying a set of groups for a UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; and receiving a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold.

[0052] An apparatus for wireless communication at a network entity is described. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions may be executed by the processor to cause the apparatus to: identify a set of groups for a UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; and receive a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold.

[0053] Another apparatus for wireless communication at a network entity is described. The apparatus may include: means for identifying a set of groups for a UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementation of each function by the UE; and means for receiving a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold.

[0054] A non-transitory computer-readable medium storing code for wireless communication at a network entity is described. The code may include instructions executable by a processor to: identify a set of groups for a UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; and receive a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold.

[0055] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for sending an indication that the UE switches from a first group to a second group in a set of groups based on a report, wherein the functionality implemented by the UE can switch from a first machine learning model associated with the first group to a second machine learning model associated with the second group.

[0056] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving an indication of a group set from a UE to register the group set, wherein the identifying may be based on the indication.

[0057] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for sending an indication of a set of groups to a UE.

[0058] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for determining that a function implemented by the UE according to a second machine learning model in a second group satisfies a performance threshold, wherein the report identifies the second group as a preferred group.

[0059] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for determining, based on the report, that a function implemented by the UE according to the first machine learning model in the first group fails to meet a performance threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 An example of a wireless communication system supporting model relationships and unified handover, activation, and deactivation according to one or more aspects of the present disclosure is illustrated.

[0061] Figure 2 An example of a wireless communication system supporting model relationships and unified handover, activation, and deactivation according to one or more aspects of the present disclosure is illustrated.

[0062] Figure 3 An example of a group configuration that supports model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is illustrated.

[0063] Figure 4A and Figure 4B An example of a signaling configuration that supports model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is illustrated.

[0064] Figure 5 and Figure 6 A block diagram of a device supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown.

[0065] Figure 7 A block diagram of a communications manager supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown.

[0066] Figure 8 A diagram of a system including a device that supports model relationships and unified switching, activation, and deactivation in accordance with one or more aspects of the present disclosure is shown.

[0067] Fig. 9 and Fig.10A block diagram of a device supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown.

[0068] Fig.11 A block diagram of a communications manager supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown.

[0069] Fig.12 A diagram of a system including a device that supports model relationships and unified switching, activation, and deactivation in accordance with one or more aspects of the present disclosure is shown.

[0070] Figures 13 to 16 A flow chart illustrating a method of supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. DETAILED DESCRIPTION

[0071] The channel state information (CSI) feedback function can be used to manage the performance of the wireless channel. The CSI feedback function includes the network entity and the user equipment (UE) exchanging and interpreting information related to the physical channel to improve throughput and support configuration decisions for wireless communication. Another example of a function includes a beam management function, in which the network entity and the UE again exchange and interpret information related to beam selection and management (e.g., to support beamforming communications). Wireless communication can occur under different conditions, such as under different Doppler conditions, under different angular velocity conditions, under different delay spread conditions, under different UE mobility conditions, and so on. Therefore, the wireless network can define various machine learning models for a given function (e.g., an artificial intelligence (AI) model, an adaptive feedback model, or any other technology for evaluating input information within a given context and presenting decisions and outputs that improve the corresponding function), wherein each machine learning model corresponds to a technology, recipe, procedure, process, etc. utilized by a function under given conditions.

[0072] For example, a first machine learning model may be used to implement a CSI feedback function within a UE and a network entity during high Doppler data conditions, the first machine learning model being different from a second (or third) machine learning model used for implementing a CSI feedback function during medium or low Doppler data conditions. For example, a first machine learning model may be used to implement a CSI feedback function within a UE and a network entity during an indoor scenario (e.g., large delay spread, non-line of sight (NLOS) conditions), the first machine learning model being different from a second (or third) machine learning model used for implementing a CSI feedback function during an outdoor scenario (e.g., medium or low delay spread conditions or line of sight (LOS) conditions). Thus, each function implemented within a UE may learn or otherwise configure one or more machine learning models to be implemented by the function, wherein different models correspond to different conditions for performing the function. However, the wireless network uses individual signaling to configure, activate, or deactivate a given model for each function. That is, for each model registered within a conventional network, individual signaling is used to activate and deactivate the machine learning model. When multiple functions are implemented within a wireless network (e.g., by individual network entities and UEs) and when conditions change, separate signaling can be used to update the machine learning model for each function.

[0073] Therefore, the described technology relates to improved methods, systems, devices and apparatuses that support model relationships and unified switching, activation and deactivation. For example, the described technology provides for grouping machine learning models from different functions according to the conditions of the wireless network and the wireless environment. For example, the UE may identify or otherwise determine a set of groups. Each group may generally include machine learning models for one or more functions implemented by the UE and the network entity (e.g., a first model for a first function, a second model for a second function, etc.). In each group, the machine learning models for different functions included in the group may each correspond to or otherwise be associated with a specific condition (e.g., a given Doppler condition, a delay spread condition, NLOS or LOS, indoors or outdoors, network features including antenna layout and beamforming schemes, etc.). In some examples, the UE may define a grouping with the network entity, the network entity may define the grouping and notify the UE of the grouping, or the network entity and the UE may collaborate to define the grouping. This may enable the network entity to rely on a unified signaling technology, in which the network switches the UE from the first group to the second group based on a change in conditions. This can enable the network to switch from a current group corresponding to a first condition (e.g., an old condition) to a second group corresponding to a second condition (e.g., a new condition) for each function implemented in the wireless network. That is, registering models for different functions but corresponding to a given condition into a group can enable the network to configure, activate, and deactivate models for multiple functions based on conditions updated within the network.

[0074] Additionally or alternatively, when one or more machine learning models for a function within a group (e.g., a first machine learning model) fails to meet a performance threshold, this may indicate that the conditions associated with the group for the corresponding function may have changed, such that the current group of machine learning models is incorrect under the changed conditions. Therefore, the UE may send a report to the network that carries or otherwise conveys an indication that the group (e.g., identifying a particular group, a model within the group, or both) failed to meet the performance threshold. The network entity may optionally send a switching indication in response to the group failure report, the switching indication directing the UE to switch from the current group (e.g., the first group) to a different group (e.g., the second group) that more closely corresponds to the changed conditions.

[0075] Aspects of the present disclosure are first described in the context of a wireless communication system. Aspects of the present disclosure are further illustrated and described by and with reference to apparatus diagrams, system diagrams, and flow diagrams related to model relationships and unified switching, activation, and deactivation.

[0076] Figure 1 An example of a wireless communication system 100 that supports model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is illustrated. The wireless communication system 100 may include one or more network entities 105, one or more UEs 115, and a core network 130. In some examples, the wireless communication system 100 may be a long term evolution (LTE) network, an advanced LTE (LTE-A) network, an LTE-A Pro network, a new radio (NR) network, or a network operating according to other systems and radio technologies (including future systems and radio technologies not explicitly mentioned herein).

[0077] The network entities 105 may be dispersed throughout a geographic area to form the wireless communication system 100, and may include devices in different forms or with different capabilities. In various examples, the network entities 105 may be referred to as network elements, mobility elements, radio access network (RAN) nodes, or network equipment, among other nomenclature. In some examples, the network entities 105 and the UE 115 may communicate wirelessly via one or more communication links 125 (e.g., radio frequency (RF) access links). For example, the network entity 105 may support a coverage area 110 (e.g., a geographic coverage area) within which the UE 115 and the network entity 105 may establish one or more communication links 125. The coverage area 110 may be an example of a geographic area within which the network entity 105 and the UE 115 may support signal communications according to one or more radio access technologies (RATs).

[0078] The UEs 115 may be dispersed throughout the coverage area 110 of the wireless communication system 100, and each UE 115 may be stationary or mobile, or stationary and mobile at different times. The UEs 115 may be devices in different forms or with different capabilities. Figure 1 Some example UEs 115 are illustrated in FIG. 1. The UEs 115 described herein may be capable of supporting communication with various types of devices, such as Figure 1 Communicate with other UEs 115 or network entities 105) as shown.

[0079] As described herein, a node of the wireless communication system 100 (which may be referred to as a network node or a wireless node) may be a network entity 105 (e.g., any network entity described herein), a UE 115 (e.g., any UE described herein), a network controller, an apparatus, a device, a computing system, one or more components, or another suitable processing entity configured to perform any of the techniques described herein. For example, the node may be a UE 115. As another example, the node may be a network entity 105. As another example, a first node may be configured to communicate with a second node or a third node. In one aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a UE 115. In another aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a network entity 105. In other aspects of this example, the first node, the second node, and the third node may be different relative to these examples. Similarly, references to UE 115, network entity 105, apparatus, device, computing system, etc. may include disclosure of UE 115, network entity 105, apparatus, device, computing system, etc. as nodes. For example, a disclosure that UE 115 is configured to receive information from network entity 105 also discloses that the first node is configured to receive information from a second node.

[0080] In some examples, the network entities 105 may communicate with the core network 130, or with each other, or both. For example, the network entities 105 may communicate with the core network 130 via one or more backhaul communication links 120 (e.g., according to S1, N2, N3, or other interface protocols). In some examples, the network entities 105 may communicate with each other directly (e.g., directly between the network entities 105) or indirectly (e.g., via the core network 130) via the backhaul communication links 120 (e.g., according to X2, Xn, or other interface protocols). In some examples, the network entities 105 may communicate with each other via midhaul communication links 162 (e.g., according to a midhaul interface protocol) or fronthaul communication links 168 (e.g., according to a fronthaul interface protocol) or any combination thereof. The backhaul communication links 120, midhaul communication links 162, or fronthaul communication links 168 may be or include one or more wired links (e.g., electrical links, optical fiber links), one or more wireless links (e.g., radio links, wireless optical links), etc. or various combinations thereof. UE 115 may communicate with core network 130 via communication link 155 .

[0081] One or more of the network entities 105 described herein may include or may be referred to as a base station 140 (e.g., a transceiver base station, a radio base station, an NR base station, an access point, a radio transceiver, a Node B, an evolved Node B (eNB), a next generation Node B, or a Gigabit Node B (any of which may be referred to as a gNB), a 5G NB, a next generation eNB (ng-eNB), a home Node B, a home evolved Node B, or other suitable terms). In some examples, the network entity 105 (e.g., a base station 140) may be implemented in an aggregated (e.g., monolithic, stand-alone) base station architecture, which may be configured to utilize a protocol stack physically or logically integrated within a single network entity 105 (e.g., a single RAN node, such as a base station 140).

[0082] In some examples, the network entity 105 may be implemented in a disaggregated architecture (e.g., a disaggregated base station architecture, a disaggregated RAN architecture) that may be configured to utilize a protocol stack that is physically or logically distributed between two or more network entities 105, such as an integrated access backhaul (IAB) network, an open RAN (O-RAN) (e.g., a network configuration sponsored by the O-RAN Alliance), or a virtualized RAN (vRAN) (e.g., a cloud RAN (C-RAN)). For example, the network entity 105 may include one or more of the following: a central unit (CU) 160, a distributed unit (DU) 165, a radio unit (RU) 170, a RAN intelligent controller (RIC) 175 (e.g., a near real-time RIC (near RT RIC), a non-real-time RIC (non-RT RIC)), a service management and orchestration (SMO) 180 system, or any combination thereof. RU 170 may also be referred to as a radio head, a smart radio head, a remote radio head (RRH), a remote radio unit (RRU), or a transmit receive point (TRP). One or more components of the network entity 105 in the decomposed RAN architecture may be co-located, or one or more components of the network entity 105 may be located in distributed locations (e.g., separate physical locations). In some examples, one or more network entities 105 of the decomposed RAN architecture may be implemented as virtual units (e.g., virtual CU (VCU), virtual DU (VDU), virtual RU (VRU)).

[0083] The functional split between 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 functional split of a protocol stack can be employed between CU 160 and DU 165, such that CU 160 can support one or more layers of a protocol stack and DU 165 can support one or more different layers of a protocol stack. In some examples, CU 160 can host higher protocol layer (e.g., layer 3 (L3), layer 2 (L2)) functionality and signaling (e.g., radio resource control (RRC), service data adaptation protocol (SDAP), packet data convergence protocol (PDCP)). The CU 160 may be connected to one or more DUs 165 or RUs 170, and the one or more DUs 165 or RUs 170 may host lower protocol layers, such as layer 1 (L1) (e.g., physical (PHY) layer) or L2 (e.g., radio link control (RLC) layer, medium access control (MAC) layer) functionality and signaling, and may each be at least partially controlled by the CU 160. Additionally or alternatively, a functional split of the protocol stack may be employed between the DUs 165 and RUs 170, such that the DUs 165 may support one or more layers of the protocol stack, and the RUs 170 may support one or more different layers of the protocol stack. The DUs 165 may support one or more different cells (e.g., via one or more RUs 170). In some cases, the functional split between CU 160 and DU 165 or between DU 165 and RU 170 may be within the protocol layer (e.g., some functions of the protocol layer may be performed by one of CU 160, DU 165, or RU 170, while other functions of the protocol layer are performed by different ones of CU 160, DU 165, or RU 170). CU 160 may be further functionally split into CU control plane (CU-CP) and CU user plane (CU-UP) functions. CU 160 may be connected to one or more DUs 165 via midhaul communication links 162 (e.g., F1, F1-c, F1-u), and DU 165 may be connected to one or more RUs 170 via fronthaul communication links 168 (e.g., open fronthaul (FH) interface). In some examples, midhaul communication link 162 or fronthaul communication link 168 may be implemented based on interfaces (eg, channels) between layers of a protocol stack supported by respective network entities 105 that communicate via those communication links.

[0084] In some wireless communication systems (e.g., wireless communication system 100), infrastructure and spectrum resources for radio access may support wireless backhaul link capabilities to supplement wired backhaul connections, thereby providing an IAB network architecture (e.g., to core network 130). In some cases, in an IAB network, one or more network entities 105 (e.g., IAB nodes 104) may be partially controlled by each other. One or more IAB nodes 104 may be referred to as donor entities or IAB donors. One or more DUs 165 or one or more RUs 170 may be controlled in part by one or more CUs 160 associated with a donor network entity 105 (e.g., a donor base station 140). One or more donor network entities 105 (e.g., IAB donors) may communicate with one or more additional network entities 105 (e.g., IAB nodes 104) via supported access and backhaul links (e.g., backhaul communication links 120). The IAB node 104 may include an IAB mobile terminal (IAB-MT) controlled (e.g., scheduled) by a DU 165 of a coupled IAB donor. The IAB-MT may include a collection of independent antennas for relaying communications with the UE 115, or may share the same antenna of the IAB node 104 (e.g., of the RU 170) for access via the DU 165 of the IAB node 104 (e.g., referred to as a virtual IAB-MT (vIAB-MT)). In some examples, the IAB node 104 may include a DU 165 that supports communication links with additional entities (e.g., IAB node 104, UE 115) within a relay chain or configuration (e.g., downstream) of the access network. In such cases, one or more components of the decomposed RAN architecture (e.g., one or more IAB nodes 104 or components of the IAB node 104) may be configured to operate according to the techniques described herein.

[0085] For example, an access network (AN) or RAN may include an access node (e.g., an IAB donor), communications between an IAB node 104, and one or more UEs 115. The IAB donor may facilitate a connection between the core network 130 and the AN (e.g., via a wired or wireless connection to the core network 130). That is, the IAB donor may refer to a RAN node having a wired or wireless connection to the core network 130. The IAB host may include a CU 160 and at least one DU 165 (e.g., and RU 170), in which case the CU 160 may communicate with the core network 130 via an interface (e.g., a backhaul link). The IAB host and the IAB node 104 may communicate via an F1 interface according to a protocol defining a signaling message (e.g., an F1AP protocol). Additionally or alternatively, CU 160 may communicate with the core network via an interface (which may be an example of part of a backhaul link) and may communicate with other CUs 160 (e.g., CU 160 associated with an alternative IAB host) via an Xn-C interface (which may be an example of part of a backhaul link).

[0086] An IAB node 104 may refer to a RAN node that provides IAB functionality (e.g., access for UE 115, wireless self-backhaul capabilities, etc.). DU 165 may act as a distributed scheduling node toward child nodes associated with IAB node 104, and IAB-MT may act as a scheduled node toward a parent node associated with IAB node 104. That is, an IAB donor may be referred to as a parent node that communicates with one or more child nodes (e.g., an IAB donor may relay for UE transmissions through one or more other IAB nodes 104). Additionally or alternatively, depending on the relay chain or configuration of the AN, an IAB node 104 may also be referred to as a parent node or child node of other IAB nodes 104. Thus, the IAB-MT entity of the IAB node 104 may provide a Uu interface for the child IAB node 104 to receive signaling from the parent IAB node 104, and a DU interface (eg, DU 165) may provide a Uu interface for the parent IAB node 104 to signal the child IAB node 104 or the UE 115.

[0087] For example, the IAB node 104 may be referred to as a parent node that supports communications for a child IAB node or as a child node associated with the IAB host, or both. The IAB donor may include a CU 160 having a wired or wireless connection (e.g., backhaul communication link 120) to the core network 130, and may act as a parent node of the IAB node 104. For example, the DU 165 of the IAB host may relay the transmission to the UE 115 through the IAB node 104, or may directly signal the transmission to the UE 115, or both. The CU 160 of the IAB donor may signal the IAB node 104 of the communication link establishment via the F1 interface, and the IAB node 104 may schedule the transmission (e.g., the transmission relayed from the IAB donor to the UE 115) through the DU 165. That is, data may be relayed to and from the IAB node 104 via signaling via the NRUu interface of the MT to the IAB node 104. Communications with the IAB node 104 may be scheduled by the DU 165 of the IAB donor, and communications with the IAB node 104 may be scheduled by the DU 165 of the IAB node 104 .

[0088] Where the techniques described herein are applied to the context of a decomposed RAN architecture, one or more components of the decomposed RAN architecture may be configured to support the model relationships and unified switching, activation, and deactivation as described herein. For example, some operations described as being performed by a UE 115 or a network entity 105 (e.g., a base station 140) may additionally or alternatively be performed by one or more components of the decomposed RAN architecture (e.g., an IAB node 104, a DU 165, a CU 160, a RU 170, a RIC 175, a SMO 180).

[0089] UE 115 may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable term, where a "device" may also be referred to as a unit, a station, a terminal, or a client, etc. UE 115 may also include or may be referred to as a personal electronic device, such as a cellular phone, a personal digital assistant (PDA), a tablet computer, a laptop computer, or a personal computer. In some examples, UE 115 may include or may be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communication (MTC) device, etc., which may be implemented in various objects such as appliances or vehicles, meters, etc.

[0090] The UE 115 described herein may be capable of communicating with various types of devices, such as other UEs 115, which may sometimes act as relays, as well as network entities 105 and network equipment including macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations, etc. Figure 1 shown.

[0091] The UE 115 and the network entity 105 may use resources associated with one or more carriers to wirelessly communicate with each other via one or more communication links 125 (e.g., access links). The term "carrier" may refer to a collection of RF spectrum resources having a physical layer structure defined for supporting the communication link 125. For example, a carrier for the communication link 125 may include a portion of an RF spectrum band (e.g., a bandwidth portion (BWP)) that operates according to one or more physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each physical layer channel may carry acquisition signaling (e.g., synchronization signals, system information), control signaling for coordinating carrier operations, user data, or other signaling. The wireless communication system 100 may support communications with the UE 115 using carrier aggregation or multi-carrier operation. Depending on the carrier aggregation configuration, the UE 115 may be configured with multiple downlink component carriers and one or more uplink component carriers. Carrier aggregation may be used for both frequency division duplex (FDD) and time division duplex (TDD) component carriers. Communication between the network entity 105 and other devices may refer to communication between a device and any portion (e.g., entity, sub-entity) of the network entity 105. For example, the terms "send," "receive," or "communicate" when referring to the network entity 105 may refer to any portion of a network entity 105 (e.g., base station 140, CU 160, DU 165, RU 170) of a RAN communicating with another device (e.g., directly or via one or more other network entities 105).

[0092] In some examples, such as in a carrier aggregation configuration, a carrier may also have acquisition signaling or control signaling that coordinates the operation of other carriers. A carrier may be associated with a frequency channel (e.g., an Evolved Universal Mobile Telecommunications System Terrestrial Radio Access (E-UTRA) Absolute RF Channel Number (EARFCN)) and may be identified based on a channel raster for discovery by UE 115. A carrier may operate in a standalone mode, in which case initial acquisition and connection may be made by UE 115 via the carrier, or a carrier may operate in a non-standalone mode, in which case the connection is anchored using a different carrier (e.g., a different carrier of the same or different radio access technology).

[0093] The communication link 125 shown in the wireless communication system 100 may include downlink transmissions (e.g., forward link transmissions) from the network entity 105 to the UE 115, uplink transmissions (e.g., return link transmissions) from the UE 115 to the network entity 105, or both, among other transmission configurations. A carrier may carry either downlink communications or uplink communications (e.g., in FDD mode), or may be configured to carry both downlink communications and uplink communications (e.g., in TDD mode).

[0094] A carrier may be associated with a particular bandwidth of the RF spectrum, and in some examples, the carrier bandwidth may be referred to as a "system bandwidth" of the carrier or wireless communication system 100. For example, the carrier bandwidth may be one of a set of bandwidths (e.g., 1.4 megahertz (MHz), 3 MHz, 5 MHz, 10 MHz, 15 MHz, 20 MHz, 40 MHz, or 80 MHz) of a carrier of a particular radio access technology. A device (e.g., a network entity 105, a UE 115, or both) of the wireless communication system 100 may have a hardware configuration that supports communications using a particular carrier bandwidth, or may be capable of being configured to support communications using one of a set of carrier bandwidths. In some examples, the wireless communication system 100 may include a network entity 105 or a UE 115 that supports concurrent communications using carriers associated with multiple carrier bandwidths. In some examples, each served UE 115 may be configured to operate using a portion (e.g., a subband, a BWP) or all of a carrier bandwidth.

[0095] The signal waveform transmitted via the carrier may include multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques, such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM)). In a system using MCM techniques, a resource element may refer to a resource of one symbol period (e.g., the duration of one modulation symbol) and one subcarrier, in which case the symbol period and the subcarrier spacing may be inversely related. The number of bits carried by each resource element may depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both), so that a relatively high number of resource elements (e.g., in the transmission duration) and a relatively high order modulation scheme may correspond to a relatively high communication rate. Wireless communication resources may refer to a combination of RF spectrum resources, time resources, and spatial resources (e.g., spatial layers, beams), and the use of multiple spatial resources may increase the data rate or data integrity used for communication with UE 115.

[0096] One or more parameter sets for a carrier may be supported, and the parameter sets may include subcarrier spacing (Δf) and cyclic prefixes. A carrier may be divided into one or more BWPs with the same or different parameter sets. In some examples, a UE 115 may be configured with multiple BWPs. In some examples, a single BWP for a carrier may be active at a given time, and communications of a UE 115 may be constrained to one or more active BWPs.

[0097] The time interval for the network entity 105 or the UE 115 may be expressed in multiples of a basic time unit, which may be, for example, a sampling period T s =1 / (Δf max ·N f ) seconds, where Δf max It can represent the supported subcarrier spacing, and N f The supported discrete Fourier transform (DFT) size may be indicated. The time intervals of the communication resources may be organized according to radio frames, each having a specified duration (e.g., 10 milliseconds (ms)). Each radio frame may be identified by a system frame number (SFN) (e.g., ranging from 0 to 1023).

[0098] 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, the frame may be divided into subframes (e.g., in the time domain), and each subframe may be further divided into a certain number of time slots. Alternatively, each frame may include a variable number of time slots, and the number of time slots may depend on the subcarrier spacing. Each time slot may include a certain number of symbol periods (e.g., depending on the length of the cyclic prefix appended in front of each symbol period). In some wireless communication systems 100, the time slot may be further divided into a plurality of 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., N f The duration of a symbol period may depend on the subcarrier spacing or the operating frequency band.

[0099] A subframe, a time slot, a mini-time slot, or a symbol may be the smallest scheduling unit (e.g., in the time domain) of the wireless communication system 100 and may be referred to as a transmission time interval (TTI). In some examples, the TTI duration (e.g., the number of symbol periods in a TTI) may be variable. Additionally or alternatively, the smallest scheduling unit of the wireless communication system 100 may be dynamically selected (e.g., in a burst of a shortened TTI (sTTI)).

[0100] Physical channels may be multiplexed according to various techniques in order to communicate using a carrier. For example, physical control channels and physical data channels may be multiplexed using one or more of a time division multiplexing (TDM) technique, a frequency division multiplexing (FDM) technique, or a hybrid TDM-FDM technique to signal via a downlink carrier. A control region (e.g., a control resource set (CORESET)) of a physical control channel may be defined by a set of symbol periods and may extend across a system bandwidth of a carrier or a subset of that system bandwidth. One or more control regions (e.g., CORESETs) may be configured for a set of UEs 115. For example, one or more UEs in UE115 may monitor or search a control region to obtain control information according to one or more search space sets, and each search space set may include one or more control channel candidates in one or more aggregation levels arranged in a cascaded manner. The aggregation level of a control channel candidate may 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 sets may include a common search space set configured for transmitting control information to a plurality of UEs 115 , and a UE-specific search space set for transmitting control information to a specific UE 115 .

[0101] The network entity 105 may provide communication coverage via one or more cells (e.g., macro cells, small cells, hot spots, or other types of cells, or any combination thereof). The term "cell" may refer to a logical communication entity used to communicate with the network entity 105 (e.g., using a carrier), and may be associated with an identifier (e.g., a physical cell identifier (PCID), a virtual cell identifier (VCID), or other identifier) ​​used to distinguish adjacent cells. In some examples, a cell may also refer to a coverage area 110 or a portion of a coverage area 110 (e.g., a sector) on which the logical communication entity operates. Depending on various factors such as the capabilities of the network entity 105, such cells may range from smaller areas (e.g., structures, subsets of structures) to larger areas. For example, a cell may be or may include a building, a subset of a building, or an external space between or overlapping coverage areas 110, and the like.

[0102] A macro cell generally covers a relatively large geographic area (e.g., several kilometers in radius) and may allow unrestricted access to UEs 115 that have service subscriptions with a network provider that supports the macro cell. A small cell may be associated with a lower power network entity 105 (e.g., a lower power base station 140) (compared to a macro cell), and the small cell may operate using the same or different (e.g., licensed, unlicensed) frequency band as the macro cell. A small cell may provide unrestricted access to UEs 115 that have service subscriptions with a network provider, or may provide restricted access to UEs 115 associated with a small cell (e.g., a UE 115 in a closed subscriber group (CSG), a UE 115 associated with a user in a home or office). A network entity 105 may support one or more cells, and may also use one or more component carriers to support communications via one or more cells.

[0103] In some examples, a carrier may support multiple cells, and different cells may be configured according to different protocol types (e.g., MTC, narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB)) that may provide access to different types of devices.

[0104] In some examples, the network entities 105 (e.g., base stations 140, RUs 170) may be mobile and thus provide communication coverage for mobile coverage areas 110. In some examples, different coverage areas 110 associated with different technologies may overlap, but the different coverage areas 110 may be supported by the same network entity 105. In some other examples, overlapping coverage areas 110 associated with different technologies may be supported by different network entities 105. The wireless communication system 100 may include, for example, a heterogeneous network in which different types of network entities 105 provide coverage for various coverage areas 110 using the same or different radio access technologies.

[0105] The wireless communication system 100 may support synchronous or asynchronous operation. For synchronous operation, the network entities 105 (e.g., base stations 140) may have similar frame timing, and transmissions from different network entities 105 may be approximately aligned in time. For asynchronous operation, the network entities 105 may have different frame timing, and in some examples, transmissions from different network entities 105 may not be aligned in time. The techniques described herein may be used for synchronous or asynchronous operation.

[0106] Some UEs 115, such as MTC or IoT devices, may be low-cost or low-complexity devices and may provide automated communication between machines (e.g., via machine-to-machine (M2M) communication). M2M communication or MTC may refer to data communication technology that allows devices to communicate with each other or with a network entity 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 capture information and relay such information to a central server or application that uses the information or presents the information to a person interacting with the application. Some UEs 115 may be designed to collect information or implement automated behavior of machines or other devices. Examples of applications for MTC devices include: smart metering, inventory monitoring, water level monitoring, equipment monitoring, health care monitoring, wildlife monitoring, weather and geographic event monitoring, queue management and tracking, remote security sensing, physical access control, and transaction-based commercial charging.

[0107] Some UEs 115 may be configured to employ a reduced power consumption mode of operation, such as half-duplex communication (e.g., a mode that supports one-way communication via transmission or reception but does not transmit and receive concurrently). In some examples, half-duplex communication may be performed at a reduced peak rate. Other power saving techniques for UEs 115 include entering a power saving deep sleep mode when not engaged in active communications, operating using limited bandwidth (e.g., according to narrowband communications), or a combination of these techniques. For example, some UEs 115 may be configured to operate using a narrowband protocol type that is associated with a defined portion or range (e.g., a subcarrier or resource block (RB) set) within a carrier, within a guard band of a carrier, or outside a carrier.

[0108] The wireless communication system 100 can be configured to support ultra-reliable communication or low-latency communication or various combinations thereof. For example, the wireless communication system 100 can be configured to support ultra-reliable low-latency communication (URLLC). UE 115 can be designed to support ultra-reliable, low-latency or critical functions. Ultra-reliable communication may include private communication or group communication, and may be supported by one or more services (such as push-to-talk, video or data). Support for ultra-reliable, low-latency functions may include prioritization of services, and such services may be used for public safety or general commercial applications. The terms ultra-reliable, low-latency and ultra-reliable low-latency may be used interchangeably herein.

[0109] In some examples, the UE 115 may be configured to support communication directly with other UEs 115 via a device-to-device (D2D) communication link 135 (e.g., according to a peer-to-peer (P2P), D2D, or sidelink protocol). In some examples, one or more UEs 115 in a group that are performing D2D communication may be within a coverage area 110 of a network entity 105 (e.g., a base station 140, a RU 170), which may support aspects of such D2D communication configured (e.g., scheduled) by the network entity 105. In some examples, one or more UEs 115 in such a group may be outside of the coverage area 110 of the network entity 105, or may otherwise be unable or not configured to receive transmissions from the network entity 105. In some examples, a group of UEs 115 communicating via D2D communication may support a one-to-many (1:M) system, in which each UE 115 transmits to each of the other UEs 115 in the group. In some examples, network entity 105 may facilitate scheduling of resources for D2D communications. In some other examples, D2D communications may be performed between UEs 115 without involving network entity 105.

[0110] In some systems, the D2D communication link 135 can be an example of a communication channel (such as a sidelink communication channel) between vehicles (e.g., UE 115). In some examples, the vehicles can communicate using vehicle-to-everything (V2X) communication, vehicle-to-vehicle (V2V) communication, or some combination of these items. The vehicles can signal information related to traffic conditions, signal scheduling, weather, safety, emergency situations, or any other information related to the V2X system. In some examples, vehicles in the V2X system can communicate with roadside infrastructure (such as roadside units), or communicate with the network via one or more network nodes (e.g., network entity 105, base station 140, RU 170) using vehicle-to-network (V2N) communication, or both.

[0111] The core network 130 may provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing or mobility functions. The core network 130 may be an evolved packet core (EPC) or a 5G core (5GC), which may include at least one control plane entity for managing access and mobility (e.g., a mobility management entity (MME), an access and mobility management function (AMF)) and at least one user plane entity for routing packets or interconnecting to an external network (e.g., a serving gateway (S-GW)), a packet data network (PDN) gateway (P-GW) or a user plane function (UPF)). The control plane entity may manage non-access stratum (NAS) functions such as mobility, authentication, and bearer management of UE 115 served by a network entity 105 (e.g., a base station 140) associated with the core network 130. User IP packets may be transferred through the user plane entity, which may provide IP address allocation and other functions. The user plane entity may be connected to the IP service 150 of one or more network operators. IP services 150 may include access to the Internet, an intranet, an IP Multimedia Subsystem (IMS), or packet-switched streaming services.

[0112] The wireless communication system 100 may operate using one or more frequency bands that may be in the range of 300 megahertz (MHz) to 300 gigahertz (GHz). In general, the region from 300 MHz to 3 GHz is referred to as the ultra-high frequency (UHF) region or decimeter band because the wavelength range is about one decimeter to one meter. UHF waves may be blocked or redirected by buildings and environmental features (which may be referred to as clusters), but these waves may be sufficient to penetrate structures so that macro cells provide services to UEs 115 located indoors. Communications using UHF waves may be associated with smaller antennas and shorter ranges (e.g., less than 100 kilometers) compared to communications using lower frequencies and longer wavelengths of the high frequency (HF) or very high frequency (VHF) portions of the spectrum below 300 MHz.

[0113] The wireless communication system 100 may also operate using a super high frequency (SHF) region, which may be in the range of 3 GHz to 30 GHz (also known as a centimeter band), or an extremely high frequency (EHF) region of the spectrum (e.g., from 30 GHz to 300 GHz) (also known as a millimeter band). In some examples, the wireless communication system 100 may support millimeter wave (mmW) communications between the UE 115 and the network entity 105 (e.g., base station 140, RU 170), and the EHF antennas of the corresponding devices may be smaller and closer together than UHF antennas. In some examples, such techniques may facilitate the use of antenna arrays within the device. However, the propagation of EHF transmissions may be affected by even greater attenuation and shorter range than SHF or UHF transmissions. The techniques disclosed herein may be employed across transmissions using one or more different frequency regions, and the use of frequency bands specified across these frequency regions may vary by country or regulatory agency.

[0114] The wireless communication system 100 may utilize licensed and unlicensed RF spectrum bands. For example, the wireless communication system 100 may use unlicensed bands (such as the 5 GHz Industrial, Scientific, and Medical (ISM) band) to employ license assisted access (LAA), LTE unlicensed (LTE-U) radio access technology, or NR technology. When operating using unlicensed RF spectrum bands, devices such as network entity 105 and UE 115 may employ carrier sensing for conflict detection and avoidance. In some examples, operations performed using unlicensed bands may be based on carrier aggregation configuration (e.g., LAA) in conjunction with component carriers operating using licensed bands. Operations performed using unlicensed spectrum may include downlink transmissions, uplink transmissions, P2P transmissions, or D2D transmissions, among others.

[0115] The network entity 105 (e.g., base station 140, RU 170) or UE 115 may be equipped with multiple antennas, which can be used to employ technologies such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communication, or beamforming. The antennas of the network entity 105 or UE 115 may be located in one or more antenna arrays or antenna panels, which may support MIMO operations or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be co-located at an antenna assembly, such as an antenna tower. In some examples, antennas or antenna arrays associated with the network entity 105 may be located at different geographical locations. The network entity 105 may include an antenna array having a collection of multiple rows and columns of antenna ports that the network entity 105 can use to support beamforming for communications with the UE 115. Similarly, the UE 115 may include one or more antenna arrays that may support various MIMO or beamforming operations. Additionally or alternatively, the antenna panel may support RF beamforming for signals sent via the antenna ports.

[0116] The network entity 105 or UE 115 may use MIMO communication to utilize multipath signal propagation and improve spectral efficiency by sending or receiving multiple signals via different spatial layers. Such a technology may be referred to as spatial multiplexing. Multiple signals may be, for example, sent by a transmitting device via different antennas or different combinations of antennas. Similarly, multiple signals may be received by a receiving device via different antennas or different combinations of antennas. Each of the multiple signals may be referred to as a separate spatial stream and may carry information associated with the same data stream (e.g., the same codeword) or different data streams (e.g., different codewords). Different spatial layers may be associated with different antenna ports for channel measurement and reporting. MIMO technologies include single-user MIMO (SU-MIMO), for which multiple spatial layers are sent to the same receiving device; and multi-user MIMO (MU-MIMO), for which multiple spatial layers are sent to multiple devices.

[0117] Beamforming (which may also be referred to as spatial filtering, directional transmission, or directional reception) is a signal processing technique that may be used at a transmitting device or a receiving device (e.g., a network entity 105, a UE 115) to shape or direct an antenna beam (e.g., a transmit beam, a receive beam) along a spatial path between the transmitting device and the receiving device. Beamforming may be achieved by combining signals communicated via antenna elements of an antenna array so that some signals propagating in a particular direction relative to the antenna array experience constructive interference, while other signals experience destructive interference. Adjustments to signals communicated via antenna elements may include the transmitting device or the receiving device applying an amplitude offset, a phase offset, or both to signals carried via antenna elements associated with the device. Adjustments associated with each of these antenna elements may be defined by a set of beamforming weights associated with a particular direction (e.g., relative to the antenna array of the transmitting device or the receiving device or relative to some other direction).

[0118] The network entity 105 or the UE 115 may use beam scanning techniques as part of a beamforming operation. For example, the network entity 105 (e.g., the base station 140, the RU 170) may use multiple antennas or antenna arrays (e.g., antenna panels) to perform beamforming operations for directional communication with the UE 115. Some signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signals) may be sent multiple times by the network entity 105 along different directions. For example, the network entity 105 may send signals according to different beamforming weight sets associated with different transmission directions. Transmissions along different beam directions may be used to identify (e.g., by a transmitting device such as the network entity 105, or by a receiving device such as the UE 115) beam directions for later transmission or reception by the network entity 105.

[0119] Some signals, such as data signals associated with a particular receiving device, may be sent by a transmitting device (e.g., transmitting network entity 105, transmitting UE 115) along a single beam direction (e.g., a direction associated with a receiving device, such as receiving network entity 105 or receiving UE 115). In some examples, a beam direction associated with transmission along a single beam direction may be determined based on signals sent along one or more beam directions. For example, UE 115 may receive one or more of the signals sent by network entity 105 along different directions, and may report to network entity 105 an indication of the signal received by UE 115 with the highest signal quality or other acceptable signal quality.

[0120] In some examples, transmission by a device (e.g., by network entity 105 or UE 115) may be performed using multiple beam directions, and the device may use a combination of digital precoding or beamforming to generate a combined beam for transmission (e.g., from network entity 105 to UE 115). UE 115 may report feedback indicating precoding weights for one or more beam directions, and the feedback may correspond to a configured set of beams across a system bandwidth or one or more subbands. Network entity 105 may send a reference signal (e.g., a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS)), which may be precoded or unprecoded. UE 115 may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., a multi-panel codebook, a linear combination codebook, a port selection codebook). Although these techniques are described with reference to signals sent along one or more directions by a network entity 105 (e.g., base station 140, RU 170), UE 115 may use similar techniques to send signals multiple times along different directions (e.g., to identify a beam direction for subsequent transmission or reception by UE 115), or to send signals along a single direction (e.g., to send data to a receiving device).

[0121] When receiving various signals (such as synchronization signals, reference signals, beam selection signals, or other control signals) from a receiving device (e.g., network entity 105), the receiving device (e.g., UE 115) may perform receiving operations (e.g., directional listening) according to multiple receiving configurations. For example, the receiving device may perform reception according to multiple receiving directions by: receiving via different antenna subarrays, processing the received signals according to different antenna subarrays, receiving according to different receiving beamforming weight sets (e.g., different directional listening weight sets) applied to signals received at multiple antenna elements of an antenna array, or processing the received signals according to different receiving beamforming weight sets applied to signals received at multiple antenna elements of an antenna array, any of which may be referred to as "listening" according to different receiving configurations or receiving directions. In some examples, the receiving device may use a single receiving configuration to receive along a single beam direction (e.g., when receiving a data signal). A single receive configuration may be aligned along a beam direction determined based on listening according to different receive configuration directions (e.g., a beam direction determined to have the highest signal strength, highest signal-to-noise ratio (SNR), or other acceptable signal quality based on listening according to multiple beam directions).

[0122] The wireless communication system 100 may be a packet-based network that operates according to a layered protocol stack. In the user plane, the communication at the bearer or PDCP layer may be IP-based. The RLC layer may perform packet segmentation and reassembly to communicate via logical channels. The MAC layer may perform priority processing and multiplexing of logical channels into transport channels. The MAC layer may also implement error detection techniques, error correction techniques, or both to support retransmission to improve link efficiency. In the control plane, the RRC layer may provide the establishment, configuration, and maintenance of an RRC connection that supports a radio bearer for user plane data between the UE 115 and the network entity 105 or the core network 130. The PHY layer may map a transport channel to a physical channel.

[0123] UE 115 and network entity 105 may support retransmission of data to increase the likelihood of data being successfully received. Hybrid automatic repeat request (HARQ) feedback is a technique for increasing the likelihood of correctly receiving data via a communication link (e.g., communication link 125, D2D communication link 135). HARQ may include a combination of error detection (e.g., using a cyclic redundancy check (CRC)), forward error correction (FEC), and retransmission (e.g., automatic repeat request (ARQ)). HARQ may improve throughput at the MAC layer under poor radio conditions (e.g., low signal-to-noise ratio conditions). In some examples, a device may support same-slot HARQ feedback, in which case the device may provide HARQ feedback in a specific slot for data received via a previous symbol in the slot. In some other examples, the device may provide HARQ feedback in a subsequent slot or according to some other time interval.

[0124] UE 115 may identify a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by UE 115, each machine learning model corresponding to a condition for each function to be implemented by UE 115. UE 115 may receive an indication to switch from a first group to a second group in the set of groups based at least in part on a threshold change in the condition. UE 115 may switch each associated function implemented by UE 115 from a first machine learning model associated with the first group to a second machine learning model associated with the second group based at least in part on the indication to switch.

[0125] The network entity 105 may identify a set of groups for the UE 115, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE 115, each machine learning model corresponding to a condition for implementation of each function by the UE 115. The network entity 105 may determine that the condition for implementation of each function by the UE 115 has changed by at least a threshold change. The network entity 105 may send an indication that the UE 115 switches from a first group to a second group in the set of groups based at least in part on the threshold change in the condition.

[0126] UE 115 may identify a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by UE 115, each machine learning model corresponding to a condition for implementation of each function by UE 115. UE 115 may determine that a function implemented by UE 115 according to a first machine learning model in a first group fails to meet a performance threshold. UE 115 may send a report to network entity 105 indicating that the first group fails to meet the performance threshold.

[0127] The network entity 105 may identify a set of groups for the UE 115, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE 115, each machine learning model corresponding to a condition for implementation of each function by the UE 115. The network entity 105 may receive a report from the UE 115 indicating that a first group in the set of groups failed to meet a performance threshold.

[0128] Figure 2 An example of a wireless communication system 200 that supports model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is illustrated. The wireless communication system 200 may implement aspects of the wireless communication system 100. The wireless communication system 200 may include a UE 205 and a network entity 210, which may be examples of corresponding devices described herein.

[0129] Wireless networks typically use various functions to monitor, manage and improve network performance. UE 205 and network entity 210 can implement functions related to physical channel (e.g., wireless channel) performance, business mode, space management, time management, etc. Models are usually used to perform each function. Models in this context broadly refer to specific technologies, rules, processes, procedures, etc., in which inputs are received and encoded (e.g., interpreted and transmitted), encoded signals are exchanged within the wireless network, encoded signals are decoded (e.g., recovered and interpreted), and outputs (e.g., decisions, parameters, configurations, etc.) utilized within the wireless network are provided. References to models may include any machine learning model, such as using AI modeling techniques. Examples of such functions may include, but are not limited to, CSI feedback functions, channel optimization functions, beam management functions, CSI-RS transmission and channel estimation functions, DMRS channel estimation functions, physical layer functions, timing functions, synchronization functions, space functions, power management functions, interference management functions, positioning functions, or any other functions implemented within a wireless network.

[0130] The wireless network may also experience various conditions. Such conditions may generally define the environment of the network, such as the environment in which a specific function is performed. The condition may be applicable to the network range (e.g., related to all nodes within the network), to a specific node (e.g., an applicable UE 205), to a communication pair (e.g., between a UE and a network entity, between two UEs, between two network entities), to a link (e.g., a specific wireless channel or port), etc. Examples of such conditions may include, but are not limited to, Doppler conditions, angular velocity conditions, delay spread conditions, indoor or outdoor conditions, LOS or NLOS conditions, travel direction conditions, travel speed conditions, antenna layout conditions, digital / analog pre-coding conditions, beam width conditions, beam conditions, power conditions, interference conditions, traffic load conditions, traffic mode conditions, traffic type conditions, reference signal configuration conditions, location conditions, antenna configuration conditions, coverage conditions, connection conditions, changes in one or more conditions, or any other conditions under which the wireless network operates.

[0131] In summary, each model utilized by various functions implemented with the network can be changed according to the conditions of the wireless network. For example, a first model used for a function under a first condition may not be optimal under a second condition. Therefore, the model used to implement a function under the first condition may be different from the model used to implement the function under the second condition. The differences between models used to perform the same function under different conditions may be small (e.g., adjusting one or more variables, parameters, weighting factors), large (e.g., using a model with a completely different approach), or any degree in between (e.g., considering additional or fewer features). Therefore, the network can utilize the differences between models based on the conditions of the network to configure each model configured to support a function.

[0132] For example, a family of models may be trained, tested, and compiled. The model is then registered with the network using the corresponding model ID. Each model is designed or developed for a certain set of scenarios (e.g., conditions). Additional examples of such conditions include urban micro, urban macro, and indoor hotspot conditions, various bandwidth configuration conditions, various payloads suitable for different UE locations (cell center UEs may report high payloads for high resolution CSI, cell edges may have to report low payloads for low resolution due to coverage issues), various antenna settings (e.g., antenna / transmit / receive chains, TxRUs, layouts (4x4, 2x8, etc.), and antenna element to TxRU mappings.

[0133] As an example, the CSI feedback function may report the CSI of the wireless channel so that the network entity 210 knows the appropriate pre-decoder, rank, MCS, and appropriate resource allocation for downlink transmission to increase throughput. In some networks, the CSI reporting configuration may include the UE using a bit sequence to report a pre-coding matrix indicator (PMI). For example, the CSI reporting configuration may include a codebook that is used as a PMI dictionary (e.g., a table or generation method for each component of the PMI codebook) from which the UE reports its best codeword (e.g., based on channel performance characteristics measured using CSI-RS). Model methods (e.g., AI-based CSI feedback) may replace the codebook with a CSI encoder and decoder. The encoder in this context may be similar to a PMI search algorithm, and the decoder may be similar to a PMI codebook used to convert CSI report bits into PMI codewords. The output of the decoder may include a downlink channel matrix (H) (the original or whitened downlink channel filtered based on interference measurements), a transmit covariance matrix, a downlink pre-decoder (V), an interference covariance matrix (R nn), rank indicator (RI), PMI, channel quality indicator (CQI), or an indication for the entire channel. In this CSI feedback function example, the UE 205 can measure or otherwise quantify aspects of channel performance (e.g., eigenvectors on each subband of one or more layers), encode this information (e.g., encoded as H, V, R nn etc.) for delivery to a network (e.g., determining what information to indicate and how to indicate the information), which network can decode the information to provide output designed to maintain or improve wireless communications within the network.

[0134] Additional examples of such functionality may include beam management functionality or other related information for predicting beams to be used in future time instances or in the spatial domain. This may include the network entity 210 transmitting N CSI-RS ports via N beams {b1, b2, b3, ..., bN}. The UE 205 may use the CSI-RS transmission to predict, identify, or otherwise determine the best beam to be used for future communications (e.g., based on its current trajectory, which may be a condition in this example). For example, the UE 205 may measure the power {P1(t0), P2(t0), ...PN(t)} during its current time slot t0, and use this information as input to the AI ​​model. Alternatively, the UE 205 may use the received signals of the N ports as input to the model (e.g., {y1(t0), y2(t0), ..., yN(t0)} as input). The UE 205 may predict, identify, or otherwise determine the power, the main beam, or both during time slot t0+t, or predict the power of N beams during time slot t0+t. For spatial beam prediction functions (e.g., functions that handle beam management, spatial features, etc.), this may include the UE 205 using beam set B as an input to a model and obtaining one or more main beams of beam set A as an output of the model. Beam set B may be a wider beam relative to beam set A, or beam set B may be a subset of beam set A.

[0135] Another function may include CSI-RS optimization and channel estimation functions. For example, the network entity 210 may use a reduced density (e.g., using fewer resources, thereby generating fewer instances of CSI-RS) to send CSI-RS. Reduced density may be achieved via a sparse mode (e.g., using only L ports out of Nt ports for CSI-RS transmission, transmitting on only K RBs out of N resource blocks (RBs), etc.). The AI ​​model method for CSI-RS optimization (e.g., reduced density) may be based on an AI-based cover code that multiplexes Nt ports on L resource elements (REs) per RB and transmits CSI-RS during only K RBs out of N RBs. The UE 205 may use an NN-based channel estimation model to recover all Nt ports on all N RBs. The demodulation reference signal (DMRS) optimization function may use an NN-based channel estimation model to recover channels on all ports and on all REs.

[0136] Thus, the wireless network may define various models to evaluate input information within a given context and render decisions that improve corresponding functions, such as providing outputs.After training, a family of models is registered in the network, each with a different identifier (model ID).

[0137] However, the wireless network uses individual signaling to configure, activate, or deactivate a given model for each function. For example, the network may deploy (e.g., activate) a model by signaling a model ID to the UE, where the UE then accesses a model server (e.g., a server or function storing registered models) to download the model for the specific implementation of the function). Therefore, each function implemented within the wireless network may have a registered family of models. For each model registered within a conventional network, individual signaling is used to activate and deactivate the model (e.g., based on the model ID). When multiple functions are implemented within a wireless network (e.g., by individual network entities and UEs) and conditions change, separate signaling may be used to update the model for each function. This approach is resource intensive and inefficient.

[0138] Therefore, the technology described herein relates to improved methods, systems, devices and apparatuses that support model relationships and unified switching, activation and deactivation. For example, the described technology provides for grouping models (e.g., machine learning, AI or any other modeling) from different functions according to the conditions of the wireless network.

[0139] For example, the UE 205 and the network entity 210 may identify or otherwise determine a set of groups. Each group may generally include models for one or more functions implemented by the UE 205 and the network entity 210 (e.g., a first model for a first function, a second model for a second function, etc.). In each group, the models for different functions included in the group may each correspond to or otherwise be associated with a specific condition (e.g., a given Doppler data condition, a network feature, etc.).

[0140] In some examples, the UE 205 may define a grouping and register the grouping with the network. For example, the model grouping of cross-functional features described herein may generally provide that the UE 205 defines a model grouping / relationship and reports the model grouping / relationship to the network entity 210. For example, the UE 205 may send or otherwise provide an indication of a group set to the network entity 210 to register the group set (e.g., to register the model with the model server). There are one or more models trained for a function (e.g., under different conditions). When registering each model (e.g., a model family) for a function, the UE 205 may include information about the model (e.g., such as a group ID).

[0141] One example may include that the UE 205 includes a group identifier (e.g., group ID) for each model that is unique for each group in the group set. In this way, a shared group identifier between models can define the group. That is, registered models with the same or shared group ID can indicate that the models are registered as a group in the group set. This means that the models within the same group are related to each other, and they are trained and deployed or activated for the same scenario or condition or configuration. The models within each group can be updated (e.g., modified, added, deleted, enabled, disabled) based on the models updated with the same group ID. As a non-limiting example, when registering a model with the network entity 210, the UE 205 can provide the model with a group ID. Models with the same group ID are considered (e.g., grouped) into the same group. If the group ID of a model (e.g., for a given model ID) is set to none or blank, this can signal that the registered model is not associated with any other model (e.g., not included in the group). More specifically, the indication may include CSI feedback (CSF) model 1 registered with group 1, CSF model 2 registered with group 2; reference signal (RS) model 1 registered with group 1, RS model 2 registered with group 2, etc. In this example, CSF model 1 and RS model 1 are associated with each other (e.g., forming a first group in a set of groups), while CSF model 2 and RS model 2 are also associated with each other, but in different groups (e.g., forming a second group in a set of groups).

[0142] Another example may include that the UE 205 includes an associated model or function identifier for each model. In this way, the associated model or function identifier may define a group as including the model and the associated model or function corresponding to the associated model or function identifier. For example, when registering a model with the network entity 210, the UE 205 may provide an associated model ID and / or application ID. If a model is neither registered with an associated model nor registered as an associated model for another model, the model may not be bound to any other model (e.g., may not be included in any group). More specifically, the indication may include CSF model 1 associated with RS model 1 and CSF model 2 associated with RS model 2. This means that CSF model 1 and RS model 1 may be associated with each other (e.g., CSF model 1 to RS model 1 are included in a first group), while CSF model 2 and RS model 2 may be associated with each other (e.g., included in a second group).

[0143] In other examples, the network entity 210 may define and register groups, and notify the UE 205 of these groups. For inspection, the network entity 210 may send or otherwise provide (and the UE 205 may receive or otherwise obtain) an indication of a set of groups. For example, the network entity 210 may (e.g., alone or in collaboration with other nodes within the wireless network, functions within the core network, or both) configure, identify, or otherwise determine the grouping / relationships for the model of the UE 205. The UE 205 may identify the set of groups based on the indication of the set of groups from the network entity 210.

[0144] One example may include the network entity 210 including a group identifier for each model that is unique for each group in the set of groups. In this way, a shared or common (e.g., identical) group identifier between models may again define each group. That is, the network entity 210 may configure the group identifier or associated model to the UE 205. The network entity 210 may configure the group ID or associated model to the UE 205 using RRC signaling or in a medium access control-control element (MAC-CE). For example, the network entity 210 may configure the group ID or associated model ID to the UE 205 via an RRC configuration or a MACCE. In some examples, the network entity 210 may use a MAC-CE to update the grouping / association.

[0145] Another example may include the network entity 210 including an associated model or function identifier for each model. In this way, each group may again be defined as a model and an associated model or function corresponding to the associated model or function identifier.

[0146] Another example may include that the network entity 210 includes a first model list for a first function and a second model list for a second function. For example, the MAC-CE may include a cell / carrier ID, a first list of model IDs for function 1 and a second list of corresponding associated model IDs for function 2, and a third list of associated model IDs for function 3. Based on the order of the models in the list, there may be a 1-to-1 mapping (1-to-1-to-1 mapping) between the models, which means that the mapped models are grouped or associated. For example, a first group may be defined as including a first model from a first list for a first function, a first model from a second list for a second function, and a first model from a third list for a third function. A second group may be defined as including a second model from a first list, a second list, and a third list, respectively. Finally, a third group may be defined as including a third model from a first list, a second list, and a third list, respectively. UE 205 may also use signaling to send an uplink MAC-CE to indicate grouping information to the network entity 210.

[0147] In some examples, this may additionally or alternatively enable the network to rely on a unified signaling technique, wherein the network switches the UE 205 from the first group to the second group based on a change in condition. For example, the network entity 210 may send or otherwise provide (and the UE 205 may receive or otherwise obtain) unified signaling 215. The unified signaling 215 may be configured to carry or otherwise convey an indication that the UE 205 is switched from the first group in the set of groups to the second group. In some aspects, an indication of switching may be provided in response to a threshold change in a condition. That is, the network entity 210 may identify or otherwise determine that the condition has changed within the wireless network, for the UE 205, or both, and send an indication of switching from the first group in the set of groups to the second group.

[0148] Thus, at 220, the UE 205 may switch each associated function implemented by the UE 205 from a first model associated with the first group to a second model associated with the second group. As a non-limiting example, the UE 205 may perform, participate in, or otherwise implement two functions according to the first group. The first group may include a model corresponding to the first function and another model corresponding to the second function. The second group may include a second model for the first function and a second model for the second function. Likewise, the groups may be based on conditions, so that the model in the first group may be used to perform the two functions under the first condition, and the model in the second group may be used to perform the two functions under the second condition. The two functions may be performed under the current condition. Based on a change in the current condition (e.g., from the first condition to the second condition), the network entity 210 may use unified signaling 215 to switch the UE 205 from using the model in the first group corresponding to the current condition to using the model in the second group corresponding to the changed condition (e.g., the second condition). That is, registering models for different functions but corresponding to a given condition in a group may enable the network to configure, activate, and deactivate models for multiple functions based on conditions updated within the network using unified signaling 215. In addition, additional functions can also be configured, activated or deactivated using unified signaling 215. For example, a grouping of models for function 3 and function 4 can be defined so that the second field in unified signaling 215 can be used to switch the groups defined under function 3 and function 4.

[0149] Additionally or alternatively, aspects of the techniques described herein may include a model failure report 225. For example, the UE 205 may detect, identify, or otherwise determine that a function implemented by the UE 205 according to a first model in the first group fails to meet a performance threshold. For example, the UE 205 may use the first group to implement the function, which means that each function is implemented according to an associated model in the first group. However, the UE 205 may determine that at least one of these models is outputting results that do not improve wireless communication (e.g., failing to output results that meet various performance criteria). This may indicate that conditions have changed at least to some extent within the wireless network. Since the changed conditions no longer correspond to a model included in the first group, this may indicate that a change in the active group from the group set may be warranted. Therefore, the UE 205 may send or otherwise provide a report indicating that the first group fails to meet a performance threshold to the network entity 210.

[0150] In some examples, the model failure report 225 may identify a preferred group (e.g., it may indicate that the UE 205 is requesting to switch from the first group to the second group). Thus, the UE 205 may report a group model failure event, and in some examples further indicate a recommended model group to switch to. In the report (e.g., in the model failure report 225), the UE 205 may report a first signaling or field indicating a group model failure, wherein the signaling has one or more fields and each field is for a functional group. For example, the indication may include an indication of a model for a first function and a second function grouped into a first group and a second group. The indication may use a first field to indicate whether the current group has failed. When the models for the third function and the fourth function are grouped into a third group and a fourth group, respectively, the UE 205 may use a second field to indicate whether the current model group for the third function and the fourth function has failed. In the model failure report 225, the UE 205 may report a second field or indication indicating a recommended new group, wherein the signaling has one or more fields and each field is for a group. For example, model group 1 is currently used for function 1 and function 2. The signaling may use the first field to indicate a preference for the first function and the second function. For example, a third group currently used for functions three and four may use the second field of the signaling to indicate a preference for switching to a fourth group for functions three and four. If the field in the signaling corresponding to the current group is empty or otherwise set to a (pre)configured value, this may indicate that there is no model failure for the current group.

[0151] It should be understood that different aspects of the techniques described herein may be performed separately or in combination. For example, group identification and configuration / reporting may be performed separately or in combination with unified signaling techniques, model failure reporting techniques, or both. Similarly, unified signaling techniques may be performed separately or in combination with group identification and configuration / reporting techniques, model failure reporting techniques, or both. Finally, model failure reporting techniques may be performed separately or in combination with group identification and configuration / reporting techniques, unified signaling techniques, or both. In some examples, group model failure reporting may occur before group model switching. This means that the gNB may perform a switching decision and send a switching command per group model failure reporting.

[0152] Figure 3 An example of a group configuration 300 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is illustrated. The group configuration may implement various aspects of the wireless communication system 100 or the wireless communication system 200. Various aspects of the group configuration may be implemented at or by a UE or a network entity, which may be an example of a corresponding device described herein.

[0153] As discussed above, aspects of the techniques described herein provide for grouping models across functions according to shared conditions. That is, the UE and the network entity may identify or otherwise determine a set of groups, wherein each group in the set of groups includes a model for each function implemented by the UE. Each model may correspond to a condition for implementing a function. Such model grouping techniques may support unified signaling techniques, model failure reporting techniques, both techniques, or neither of these techniques.

[0154] Grouping configuration 300 illustrates a non-limiting example of a set of groups formed according to the techniques described herein. According to grouping configuration 300, by way of example only, a UE may implement three functions. For example, a UE may implement a first function 305, a second function 310, and a third function 315. Each function may be associated with a model family. First function 305 may be associated with model 320, model 325, and model 330. Second function 310 may be associated with model 335, model 340, and model 345. Third function 315 may be associated with model 350, model 355, and model 360. As discussed above, each model for a function within a model family may correspond to a different condition for implementing the function.

[0155] Thus, the techniques described herein provide for grouping models from different functions but corresponding to conditions into groups that can then be activated or deactivated using a single signal (e.g., a unified signaling technique). For example, a first group 365 in a set of groups may include a model 320 for a first function 305, a model 335 for a second function 310, and a model 350 for a third function 315. A second group 370 in a set of groups may include a model 325 for a first function 305, a model 340 for a second function 310, and a model 355 for a third function 315. A third group 375 in a set of groups may include a model 330 for a first function 305, a model 345 for a second function 310, and a model 360 for a third function 315.

[0156] Thus, the first group 365 may be activated for the UE using unified signaling techniques. When conditions change for the wireless network, the unified signaling techniques may be used to switch the UE from the first group 365 to the second group 370 (or to some other group). When one or more models within the first group 365 fail to meet a performance threshold, a model failure reporting technique may be used to report the failed model (e.g., on a group basis). This may result in the group with the failed model being simply deactivated by the UE and the network, or may result in the unified signaling techniques being applied to switch the UE and the network to a different group.

[0157] Therefore, the UE, the network entity, or both can determine the model grouping (e.g., relationship or association), and can receive unified (common) signaling to trigger group switching, activation or deactivation of switching models for multiple functions with a single indication. The UE can also report unified signaling for model failure reporting or performance monitoring. Models for different functions (e.g., CSF, beam management, beam prediction, CSI-RS optimization, DMRS optimization, etc.) are grouped / associated together. Models in the same group are trained with data with similar statistics (e.g., under the same conditions). Unified signaling technology can enable a single (common) command to be used to trigger switching / activation / deactivation of grouped function models. For example, when the current group includes {CSF model 1, beam management model 1, CSI-RS model 1}, a single signaling can be used to switch the UE to another model group {CSF model 2, beam management model 2, CSI-RS model 2}, instead of using three separate signals for each function.

[0158] Figure 4A and Figure 4B An example of a signaling configuration 400 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is illustrated. The signaling configuration 400 may implement aspects of the wireless communication system 100, the wireless communication system 200, or the packet configuration 300. Various aspects of the signaling configuration 400 may be implemented at or by a UE or a network entity, which may be an example of a corresponding device described herein.

[0159] As discussed above, aspects of the techniques described herein provide for grouping models across functions according to shared conditions. That is, the UE and the network entity may identify or otherwise determine a set of groups, wherein each group in the set of groups includes a model for each function implemented by the UE. Each model may correspond to a condition for implementing a function. Such model grouping techniques may support a unified signaling technique, a model failure reporting technique, both techniques, or neither technique. Signaling configuration 400 illustrates an example of a unified signaling technique according to the techniques described herein.

[0160] That is, the network entity may send an indication of switching from a first group in a set of groups to a second group. The indication may be provided based at least to some extent on a change in the conditions for performing the function. For example, the identification of the set of groups in the indication of the switch may include one or more fields corresponding to groups and functions associated in different ways. As a non-limiting example, this may include a first field in the indication of the switch, which indicates that the UE switches from the first group to the second group for a set of functions implemented by the UE (e.g., a first set of functions). In some examples, the indication of the switch may also include a second field for identifying an indication of switching from a third group to a fourth group for a second set of functions implemented by the UE. In some examples, the indication of the switch may include a flag, field, bit, etc. indicating whether the group is activated or deactivated. In some examples, unified signaling may be conveyed in a UE-specific DCI, a group-common DCI, or a MAC-CE.

[0161] First reference Figure 4A In the signaling configuration 400-a, a dedicated field may be included in a UE-specific DCI. The DCI may be a downlink DCI (e.g., DCI 1_x) or an uplink DCI (e.g., DCI 0_x). A dedicated segment may be used for group switching. The dedicated segment may include one or more fields, each field being used to switch a group under a functional group. For example, models for function 1 and function 2 may be grouped into group 1 and group 2. An indication of switching may use the first field 410 of the group switching segment to perform group-based switching of function 1 and function 2. Models for function 3 and function 4 may be grouped into group 3 and group 4. An indication of switching may use the second field 415 of the group switching segment to perform group-based model switching of function 3 and function 4. In some examples, the method may be applied to group activation or deactivation, and a dedicated bit (e.g., flag 405) may be added to be used for (e.g., signaling) activating or deactivating the corresponding group in each field. For example, the flag 405 being set to "Switch" may indicate activation of a new model group, and being set to "Deactivate" may indicate deactivation of the current model group.

[0162] In some examples, the UE-specific MAC-CE may be used to convey an indication of a handover to the UE. A dedicated MAC-CE may be used for group handover, group activation and deactivation, etc. The UE-specific MAC-CE may be used to Figure 4A Signaling configuration 400-a is similar to UE-specific DCI.

[0163] Next reference Figure 4BSignaling configuration 400-b, a dedicated group common DCI (DCI sent for a UE group) may be used to convey an indication of switching. The group common DCI may contain multiple segments, each segment for a specific UE. Each UE may be (pre) configured with a start bit to be read and the length of the bit to be read in the group common DCI. This may signal the corresponding segment of the group common DCI to the UE. Each segment may contain one or more fields, such as discussed in the UE-specific DCI. Therefore, the indication of switching conveyed in the unified signaling technology may include a first segment set 420 associated with the first UE and a second segment set 425 associated with the second UE. The first segment set 420 may include two segments, and the second segment set 425 may include four segments, but each segment set may include some other number of segments. For each UE, the starting segment (or field or bit) to be read and the length of the segment (or field or bit) to be read are configured via RRC. Each segment (or field) is used to perform group-based mode switching / activation / deactivation on a set of functions. In this example, seg1 is used to switch between group 1 and group 2 defined under functions 1 and 2 for UE1, and seg2 is used to switch between group 3 and group 4 defined under functions 3 and 4 for UE1. Similarly, for UE2, seg1 is used to switch between group 1 and group 2 defined under functions 1 and 2 for UE2; seg2 is used to switch between group 3 and group 4 defined under functions 3 and 4 for UE2; seg3 is used to switch between group 5 and group 6 defined under functions 5 and 6 for UE2; and seg4 is used to switch between group 7 and group 8 defined under functions 7 and 8 for UE2.

[0164] Therefore, the UE may switch a model in the first group for functions to a model in the second group for functions based on the indication of switching constructed according to the signaling configuration 400 .

[0165] Figure 5 A block diagram 500 of a device 505 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The device 505 can be an example of aspects of the UE 115 as described herein. The device 505 may include a receiver 510, a transmitter 515, and a communication manager 520. The device 505 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0166] Receiver 510 may provide means for receiving information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels related to model relationships and unified switching, activation, and deactivation, data channels, information channels). The information may be passed to other components of device 505. Receiver 510 may utilize a single antenna or a collection of multiple antennas.

[0167] The transmitter 515 may provide means for transmitting signals generated by other components of the device 505. For example, the transmitter 515 may transmit information associated with various information channels (e.g., control channels related to model relationships and unified switching, activation and deactivation, data channels, information channels), such as packets, user data, control information, or any combination thereof. In some examples, the transmitter 515 may be co-located with the receiver 510 in a transceiver module. The transmitter 515 may utilize a single antenna or a collection of multiple antennas.

[0168] The communication manager 520, receiver 510, transmitter 515, or various combinations thereof or various components thereof may be examples of means for performing various aspects of model relationships and unified switching, activation, and deactivation as described herein. For example, the communication manager 520, receiver 510, transmitter 515, or various combinations thereof or components thereof may support methods for performing one or more of the functions described herein.

[0169] In some examples, the communication manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be implemented in hardware (e.g., in a communication management circuit). The hardware may include a processor, a digital signal processor (DSP), a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a microcontroller, discrete gate or transistor logic, discrete hardware components, or any combination thereof that is configured as or otherwise supports components for performing the functions described in the present disclosure. In some examples, a processor and a memory coupled to the processor may be configured to perform one or more of the functions described herein (e.g., by executing instructions stored in the memory by the processor).

[0170] Additionally or alternatively, in some examples, the communication manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be implemented in code executed by a processor (e.g., as communication management software or firmware). If implemented in code executed by a processor, the functionality of the communication manager 520, the receiver 510, the transmitter 515, or various combinations or components thereof may be performed by a general purpose processor (e.g., configured as or otherwise supporting components for performing the functions described in the present disclosure), a DSP, a CPU, an ASIC, an FPGA, a microcontroller, or any combination of these or other programmable logic devices.

[0171] In some examples, communication manager 520 can be configured to perform various operations (e.g., receive, obtain, monitor, output, send) using or otherwise cooperating with receiver 510, transmitter 515, or both. For example, communication manager 520 can receive information from receiver 510, transmit information to transmitter 515, or be integrated in conjunction with receiver 510, transmitter 515, or both to obtain information, output information, or perform various other operations as described herein.

[0172] According to examples as disclosed herein, the communication manager 520 may support wireless communications at a UE. For example, the communication manager 520 may be configured to or otherwise support a component for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The communication manager 520 may be configured to or otherwise support a component for receiving an indication of switching from a first group in the set of groups to a second group based on a threshold change of the condition. The communication manager 520 may be configured to or otherwise support a component for switching each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based on the indication of the switch.

[0173] Additionally or alternatively, according to examples as disclosed herein, the communication manager 520 may support wireless communications at the UE. For example, the communication manager 520 may be configured to or otherwise support a component for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The communication manager 520 may be configured to or otherwise support a component for determining that a function implemented by the UE according to a first machine learning model in a first group fails to meet a performance threshold. The communication manager 520 may be configured to or otherwise support a component for sending a report to a network entity indicating that the first group fails to meet the performance threshold.

[0174] By including or configuring the communication manager 520 according to the examples described herein, the device 505 (e.g., a processor that controls or otherwise couples with the receiver 510, the transmitter 515, the communication manager 520, or a combination thereof) can support techniques for grouping models for different network functions into groups based on conditions for performing the functions. This can enable group-based training and registration, group-based switching using unified signaling, group-based activation and deactivation, and group-based model failure reporting.

[0175] Figure 6 A block diagram 600 of a device 605 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The device 605 can be an example of aspects of the device 505 or UE 115 as described herein. The device 605 may include a receiver 610, a transmitter 615, and a communication manager 620. The device 605 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0176] The receiver 610 may provide means for receiving information (such as packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels related to model relationships and unified switching, activation, and deactivation, data channels, information channels). The information may be passed to other components of the device 605. The receiver 610 may utilize a single antenna or a collection of multiple antennas.

[0177] The transmitter 615 may provide means for transmitting signals generated by other components of the device 605. For example, the transmitter 615 may transmit information associated with various information channels (e.g., control channels related to model relationships and unified switching, activation and deactivation, data channels, information channels), such as packets, user data, control information, or any combination thereof. In some examples, the transmitter 615 may be co-located with the receiver 610 in a transceiver module. The transmitter 615 may utilize a single antenna or a collection of multiple antennas.

[0178] Device 605 or its various components can be examples of parts for performing various aspects of model relationship and unified switching, activation and deactivation as described herein. For example, communication manager 620 may include group identification manager 625, switching manager 630, group failure report manager 635 or any combination thereof. Communication manager 620 can be an example of various aspects of communication manager 520 as described herein. In some examples, communication manager 620 or its various components can be configured to use or otherwise cooperate with receiver 610, transmitter 615 or both to perform various operations (e.g., receive, obtain, monitor, output, send). For example, communication manager 620 can receive information from receiver 610, transmit information to transmitter 615, or integrate with receiver 610, transmitter 615 or both in combination to obtain information, output information or perform various other operations as described herein.

[0179] According to examples as disclosed herein, the communication manager 620 may support wireless communications at a UE. The group identification manager 625 may be configured to or otherwise support components for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The switching manager 630 may be configured to or otherwise support components for receiving an indication to switch from a first group in the set of groups to a second group based on a threshold change of the condition. The switching manager 630 may be configured to or otherwise support components for switching each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based on the indication of the switch.

[0180] Additionally or alternatively, the communication manager 620 may support wireless communications at the UE according to examples as disclosed herein. The group identification manager 625 may be configured to or otherwise support components for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The group failure reporting manager 635 may be configured to or otherwise support components for determining that a function implemented by the UE according to a first machine learning model in a first group failed to meet a performance threshold. The group failure reporting manager 635 may be configured to or otherwise support components for sending a report to a network entity indicating that the first group failed to meet the performance threshold.

[0181] Figure 7A block diagram 700 of a communication manager 720 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The communication manager 720 may be an example of aspects of the communication manager 520, the communication manager 620, or both as described herein. The communication manager 720 or its various components may be examples of components for performing various aspects of the model relationships and unified switching, activation, and deactivation as described herein. For example, the communication manager 720 may include a group identification manager 725, a handover manager 730, a group failure reporting manager 735, a UE group registration manager 740, a network group registration manager 745, a unified signaling manager 750, or any combination thereof. Each of these components may communicate with each other directly or indirectly (e.g., via one or more buses).

[0182] According to examples as disclosed herein, the communication manager 720 may support wireless communications at a UE. The group identification manager 725 may be configured to or otherwise support components for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The switching manager 730 may be configured to or otherwise support components for receiving an indication to switch from a first group in the set of groups to a second group based on a threshold change of the condition. In some examples, the switching manager 730 may be configured to or otherwise support components for switching each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based on the indication of the switch.

[0183] In some examples, the UE group registration manager 740 may be configured to or otherwise support components for sending an indication of a set of groups to a network entity to register the set of groups, wherein an indication of a switch is received based on the registration. In some examples, the indication of the set of groups includes a group identifier for each model, the group identifier being unique for each group in the set of groups. In some examples, a shared group identifier between one or more machine learning models defines a group. In some examples, the indication of the set of groups includes an associated model or function identifier for each machine learning model. In some examples, the associated model or function identifier defines the group as including the machine learning model and an associated model or function corresponding to the associated model or function identifier.

[0184] In some examples, the network group registration manager 745 may be configured or otherwise support components for receiving an indication of a set of groups from a network entity, wherein the identification is based on the indication of the set of groups. In some examples, the network group registration manager 745 may be configured or otherwise support components for identifying a group identifier for each machine learning model based on the indication of the set of groups, the group identifier being unique for each group in the set of groups, wherein each group is defined by a shared group identifier between one or more machine learning models.

[0185] In some examples, the network group registration manager 745 may be configured to or otherwise support components for identifying an associated model or function identifier for each model based on an indication of a set of groups, wherein the associated model or function identifier defines the group as including a machine learning model and an associated model or function corresponding to the associated model or function identifier. In some examples, to support identifying a set of groups, the network group registration manager 745 may be configured to or otherwise support components for identifying a first list of machine learning models corresponding to a first function and a second list of machine learning models corresponding to a second function. In some examples, to support identifying a set of groups, the network group registration manager 745 may be configured to or otherwise support components for: identifying a first group based on a first machine learning model from a first list of machine learning models and a first machine learning model from a second list of machine learning models; and identifying a second group based on a second machine learning model from the first list of machine learning models and a second machine learning model from a second list of machine learning models. In some examples, the indication is received in RRC signaling or in MAC-CE.

[0186] In some examples, to support identifying a set of groups, the unified signaling manager 750 may be configured to or otherwise support a component for identifying an indication of switching from a first group to a second group for a first set of functions implemented by the UE based on a first field in an indication of switching. In some examples, to support identifying a set of groups, the unified signaling manager 750 may be configured to or otherwise support a component for identifying an indication of switching from a third group to a fourth group for a second set of functions implemented by the UE based on a second field in an indication of switching. In some examples, the indication of switching indicates that the first group is deactivated, the second group is activated, or both. In some examples, the indication of switching is received in a UE-specific DCI, in a group-common DCI, or in a MAC-CE.

[0187] In some examples, the group failure reporting manager 735 may be configured to or otherwise support components for determining that the functionality implemented by the UE according to the first machine learning model in the first group failed to meet a performance threshold. In some examples, the group failure reporting manager 735 may be configured to or otherwise support components for sending a report to a network entity indicating that the first group failed to meet the performance threshold, wherein the indication of the handover is based on the report.

[0188] In some examples, the group failure reporting manager 735 may be configured to or otherwise support components for determining that functionality implemented by the UE according to a second machine learning model in a second group satisfies a performance threshold, wherein the report identifies the second group as a preferred group.

[0189] In some examples, the function includes at least one of the following: CSI feedback function, channel optimization function, beam management function, CSI-RS optimization function, DMRS function, physical layer function, timing function, synchronization function, space function, power management function, interference management function or positioning function. In some examples, the condition includes at least one of the following: Doppler condition, angular velocity condition, direction of travel condition, travel speed condition, beam width condition, beam condition, power condition, interference condition, service load condition, service mode condition, service type condition, reference signal configuration condition, location condition, antenna configuration condition, coverage condition, connection condition or change of one or more conditions.

[0190] Additionally or alternatively, the communication manager 720 may support wireless communications at the UE according to examples as disclosed herein. In some examples, the group identification manager 725 may be configured to or otherwise support components for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The group failure reporting manager 735 may be configured to or otherwise support components for determining that a function implemented by the UE according to a first machine learning model in a first group failed to meet a performance threshold. In some examples, the group failure reporting manager 735 may be configured to or otherwise support components for sending a report to a network entity indicating that the first group failed to meet a performance threshold.

[0191] In some examples, the unified signaling manager 750 may be configured to or otherwise support components for receiving an indication of switching from a first group in the set of groups to a second group based on the report. In some examples, the unified signaling manager 750 may be configured to or otherwise support components for switching functionality implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based on the indication of the switching.

[0192] In some examples, UE group registration manager 740 may be configured or otherwise support components for sending an indication of a group set to a network entity to register the group set.

[0193] In some examples, network group registration manager 745 may be configured or otherwise support components for receiving an indication of a set of groups from a network entity, wherein the identification is based on the indication of the set of groups.

[0194] In some examples, the handover manager 730 may be configured to or otherwise support components for determining that functionality implemented by the UE according to a second machine learning model in a second group satisfies a performance threshold, wherein the report identifies the second group as a preferred group.

[0195] Figure 8 A diagram of a system 800 including a device 805 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The device 805 may be an example of a device 505, a device 605, or a UE 115 as described herein, or include components thereof. The device 805 may communicate with one or more network entities 105, one or more UEs 115, or any combination thereof (e.g., wirelessly). The device 805 may include components for two-way voice and data communications, including components for sending and receiving communications, such as a communication manager 820, an input / output (I / O) controller 810, a transceiver 815, an antenna 825, a memory 830, a code 835, and a processor 840. These components may be electronically communicated or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus 845).

[0196] I / O controller 810 can manage input signals and output signals of device 805. I / O controller 810 can also manage peripheral devices that are not integrated into device 805. In some cases, I / O controller 810 can represent a physical connection or port to an external peripheral device. In some cases, I / O controller 810 can utilize an operating system such as 、MS- 、MS- 、OS / or another known operating system. Additionally or alternatively, I / O controller 810 may represent or interact with a modem, keyboard, mouse, touch screen, or similar device. In some cases, I / O controller 810 may be implemented as part of a processor, such as processor 840. In some cases, a user may interact with device 805 via I / O controller 810 or via hardware components controlled by I / O controller 810.

[0197] In some cases, the device 805 may include a single antenna 825. However, in some other cases, the device 805 may have more than one antenna 825, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The transceiver 815 may communicate bidirectionally via one or more antennas 825, wired or wireless links, as described herein. For example, the transceiver 815 may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The transceiver 815 may also include a modem for: modulating packets; providing the modulated packets to one or more antennas 825 for transmission; and demodulating packets received from one or more antennas 825. The transceiver 815 or the transceiver 815 and one or more antennas 825 may be examples of transmitters 515, transmitters 615, receivers 510, receivers 610, or any combination thereof or components thereof as described herein.

[0198] The memory 830 may include random access memory (RAM) and read-only memory (ROM). The memory 830 may store computer-readable, computer-executable code 835 including instructions that, when executed by the processor 840, cause the device 805 to perform various functions described herein. The code 835 may be stored in a non-transitory computer-readable medium, such as a system memory or another type of memory. In some cases, the code 835 may not be directly executable by the processor 840, but may (e.g., when compiled and executed) cause the computer to perform the functions described herein. In some cases, the memory 830 may include, among other things, a basic I / O system (BIOS), which may control basic hardware or software operations, such as interaction with peripheral components or devices.

[0199] The processor 840 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, the processor 840 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated into the processor 840. The processor 840 may be configured to execute computer-readable instructions stored in a memory (e.g., a memory 830) to enable the device 805 to perform various functions (e.g., functions or tasks that support model relationships and unified switching, activation, and deactivation). For example, the device 805 or a component of the device 805 may include a processor 840 and a memory 830 coupled to or coupled to the processor 840, and the processor 840 and the memory 830 are configured to perform the various functions described herein.

[0200] According to examples as disclosed herein, the communication manager 820 may support wireless communications at a UE. For example, the communication manager 820 may be configured to or otherwise support a component for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The communication manager 820 may be configured to or otherwise support a component for receiving an indication of switching from a first group in the set of groups to a second group based on a threshold change of the condition. The communication manager 820 may be configured to or otherwise support a component for switching each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based on the indication of the switch.

[0201] Additionally or alternatively, according to examples as disclosed herein, the communication manager 820 may support wireless communications at the UE. For example, the communication manager 820 may be configured to or otherwise support a component for identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The communication manager 820 may be configured to or otherwise support a component for determining that a function implemented by the UE according to a first machine learning model in a first group fails to meet a performance threshold. The communication manager 820 may be configured to or otherwise support a component for sending a report to a network entity indicating that the first group fails to meet the performance threshold.

[0202] By including or configuring the communication manager 820 according to the examples described herein, the device 805 can support a technique for grouping models for different network functions into groups based on the conditions for performing the functions. This can enable group-based training and registration, group-based switching using unified signaling, group-based activation and deactivation, and group-based model failure reporting.

[0203] In some examples, the communication manager 820 may be configured to perform various operations (e.g., receive, monitor, transmit) using or otherwise cooperating with the transceiver 815, one or more antennas 825, or any combination thereof. Although the communication manager 820 is illustrated as a separate component, in some examples, one or more functions described with reference to the communication manager 820 may be supported or performed by the processor 840, the memory 830, the code 835, or any combination thereof. For example, the code 835 may include instructions that may be executed by the processor 840 to cause the device 805 to perform various aspects of the model relationship and unified switching, activation, and deactivation as described herein, or the processor 840 and the memory 830 may be otherwise configured to perform or support such operations.

[0204] Fig. 9 A block diagram 900 of a device 905 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The device 905 can be an example of aspects of the network entity 105 as described herein. The device 905 may include a receiver 910, a transmitter 915, and a communication manager 920. The device 905 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0205] The receiver 910 may provide means for obtaining (e.g., receiving, determining, identifying) information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). The information may be passed to other components of the device 905. In some examples, the receiver 910 may support obtaining information by receiving signals via one or more antennas. Additionally or alternatively, the receiver 910 may support obtaining information by receiving signals via one or more wired (e.g., electrical, optical) interfaces, wireless interfaces, or any combination thereof.

[0206] The transmitter 915 may provide a means for outputting (e.g., sending, providing, conveying, transmitting) information generated by other components of the device 905. For example, the transmitter 915 may output information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). In some examples, the transmitter 915 may support outputting information by sending signals via one or more antennas. Additionally or alternatively, the transmitter 915 may support outputting information by sending signals via one or more wired (e.g., electrical, optical) interfaces, wireless interfaces, or any combination thereof. In some examples, the transmitter 915 and the receiver 910 may be co-located in a transceiver, which may include a modem or be coupled to a modem.

[0207] The communication manager 920, receiver 910, transmitter 915, or various combinations thereof or various components thereof may be examples of means for performing various aspects of model relationships and unified switching, activation, and deactivation as described herein. For example, the communication manager 920, receiver 910, transmitter 915, or various combinations thereof or components thereof may support methods for performing one or more of the functions described herein.

[0208] In some examples, the communication manager 920, the receiver 910, the transmitter 915, or various combinations or components thereof may be implemented in hardware (e.g., in a communication management circuit). The hardware may include a processor, a DSP, a CPU, an ASIC, an FPGA or other programmable logic device, a microcontroller, discrete gate or transistor logic, discrete hardware components, or any combination thereof that is configured as or otherwise supports components for performing the functions described in the present disclosure. In some examples, a processor and a memory coupled to the processor may be configured to perform one or more of the functions described herein (e.g., by executing instructions stored in the memory by the processor).

[0209] Additionally or alternatively, in some examples, the communication manager 920, the receiver 910, the transmitter 915, or various combinations or components thereof may be implemented in code executed by a processor (e.g., as communication management software or firmware). If implemented in code executed by a processor, the functionality of the communication manager 920, the receiver 910, the transmitter 915, or various combinations or components thereof may be performed by a general purpose processor (e.g., configured as or otherwise supporting components for performing the functions described in the present disclosure), a DSP, a CPU, an ASIC, an FPGA, a microcontroller, or any combination of these or other programmable logic devices.

[0210] In some examples, the communication manager 920 may be configured to perform various operations (e.g., receive, obtain, monitor, output, send) using or otherwise cooperating with the receiver 910, the transmitter 915, or both. For example, the communication manager 920 may receive information from the receiver 910, transmit information to the transmitter 915, or be integrated with the receiver 910, the transmitter 915, or both to obtain information, output information, or perform various other operations as described herein.

[0211] According to examples as disclosed herein, the communication manager 920 may support wireless communications at a network entity. For example, the communication manager 920 may be configured to or otherwise support a component for identifying a set of groups for a UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The communication manager 920 may be configured to or otherwise support a component for determining that a condition for implementing each function by the UE has changed by at least a threshold change. The communication manager 920 may be configured to or otherwise support a component for sending an indication that the UE switches from a first group in the set of groups to a second group based on a threshold change in the condition.

[0212] Additionally or alternatively, according to examples as disclosed herein, the communication manager 920 may support wireless communications at a network entity. For example, the communication manager 920 may be configured to or otherwise support components for identifying a set of groups for the UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE. The communication manager 920 may be configured to or otherwise support components for receiving a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold.

[0213] By including or configuring the communication manager 920 according to the examples described herein, the device 905 (e.g., a processor that controls or otherwise couples with the receiver 910, the transmitter 915, the communication manager 920, or a combination thereof) can support techniques for grouping models for different network functions into groups based on conditions for performing the functions. This can enable group-based training and registration, group-based switching using unified signaling, group-based activation and deactivation, and group-based model failure reporting.

[0214] Fig.10 A block diagram 1000 of a device 1005 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The device 1005 can be an example of aspects of the device 905 or network entity 105 as described herein. The device 1005 may include a receiver 1010, a transmitter 1015, and a communication manager 1020. The device 1005 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0215] Receiver 1010 may provide means for obtaining (e.g., receiving, determining, identifying) information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). The information may be passed to other components of device 1005. In some examples, receiver 1010 may support obtaining information by receiving signals via one or more antennas. Additionally or alternatively, receiver 1010 may support obtaining information by receiving signals via one or more wired (e.g., electrical, optical) interfaces, wireless interfaces, or any combination thereof.

[0216] The transmitter 1015 may provide a means for outputting (e.g., sending, providing, conveying, transmitting) information generated by other components of the device 1005. For example, the transmitter 1015 may output information associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack), such as user data, control information, or any combination thereof (e.g., I / Q samples, symbols, packets, protocol data units, service data units). In some examples, the transmitter 1015 may support outputting information by sending signals via one or more antennas. Additionally or alternatively, the transmitter 1015 may support outputting information by sending signals via one or more wired (e.g., electrical, optical) interfaces, wireless interfaces, or any combination thereof. In some examples, the transmitter 1015 and the receiver 1010 may be co-located in a transceiver, which may include a modem or be coupled to a modem.

[0217] Device 1005 or its various components can be examples of parts for performing various aspects of model relationship and unified switching, activation and deactivation as described herein. For example, communication manager 1020 may include group identification manager 1025, condition manager 1030, switching manager 1035, group failure report manager 1040 or any combination thereof. Communication manager 1020 can be an example of various aspects of communication manager 920 as described herein. In some examples, communication manager 1020 or its various components can be configured to use or otherwise cooperate with receiver 1010, transmitter 1015 or both to perform various operations (e.g., receive, obtain, monitor, output, send). For example, communication manager 1020 can receive information from receiver 1010, transmit information to transmitter 1015, or integrate with receiver 1010, transmitter 1015 or both to obtain information, output information or perform various other operations as described herein.

[0218] According to examples as disclosed herein, the communication manager 1020 may support wireless communications at a network entity. The group identification manager 1025 may be configured to or otherwise support components for identifying a set of groups for a UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The condition manager 1030 may be configured to or otherwise support components for determining that a condition for implementing each function by the UE has changed by at least a threshold change. The handover manager 1035 may be configured to or otherwise support components for sending an indication that the UE is switching from a first group in the set of groups to a second group based on a threshold change in the condition.

[0219] Additionally or alternatively, according to examples as disclosed herein, the communication manager 1020 may support wireless communications at a network entity. The group identification manager 1025 may be configured to or otherwise support components for identifying a set of groups for the UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementation of each function by the UE. The group failure report manager 1040 may be configured to or otherwise support components for receiving a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold.

[0220] Fig.11 A block diagram 1100 of a communication manager 1120 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The communication manager 1120 can be an example of aspects of the communication manager 920, the communication manager 1020, or both as described herein. The communication manager 1120 or its various components can be examples of components for performing various aspects of the model relationships and unified switching, activation, and deactivation as described herein. For example, the communication manager 1120 may include a group identification manager 1125, a condition manager 1130, a handover manager 1135, a group failure reporting manager 1140, a network registration manager 1145, a UE registration manager 1150, a unified signaling manager 1155, or any combination thereof. Each of these components may communicate directly or indirectly with each other (e.g., via one or more buses), which communication may include communication within a protocol layer of a protocol stack, communication associated with a logical channel of a protocol stack (e.g., between protocol layers of a protocol stack, within a device, component, or virtualized component associated with the network entity 105, between devices, components, or virtualized components associated with the network entity 105), or any combination thereof.

[0221] According to examples as disclosed herein, the communication manager 1120 may support wireless communications at a network entity. The group identification manager 1125 may be configured to or otherwise support components for identifying a set of groups for a UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE. The condition manager 1130 may be configured to or otherwise support components for determining that a condition for each function to be implemented by the UE has changed by at least a threshold change. The switching manager 1135 may be configured to or otherwise support components for sending an indication that the UE is switching from a first group in the set of groups to a second group based on a threshold change in the condition.

[0222] In some examples, the network registration manager 1145 may be configured to or otherwise support components for sending an indication of a set of groups to a UE to register the set of groups. In some examples, the indication of the set of groups includes a group identifier for each machine learning model that is unique to each group in the set of groups. In some examples, a shared group identifier between one or more machine learning models defines a group. In some examples, the indication of the set of groups includes an associated model or function identifier for each machine learning model. In some examples, the associated model or function identifier defines the group as including the machine learning model and an associated model or function corresponding to the associated model or function identifier.

[0223] In some examples, the UE registration manager 1150 may be configured or otherwise support components for receiving an indication of a set of groups from the UE, wherein the identification is based on the indication of the set of groups. In some examples, the UE registration manager 1150 may be configured or otherwise support components for identifying a group identifier for each machine learning model based on the indication of the set of groups, the group identifier being unique for each group in the set of groups, wherein each group is defined by a shared group identifier between one or more machine learning models. In some examples, the UE registration manager 1150 may be configured or otherwise support components for identifying an associated model or function identifier for each model based on the indication of the set of groups, wherein the associated model or function identifier defines the group as including the machine learning model and an associated model or function corresponding to the associated model or function identifier.

[0224] In some examples, to support identifying a set of groups, the UE registration manager 1150 may be configured to or otherwise support components for identifying a first list of machine learning models corresponding to a first function and a second list of machine learning models corresponding to a second function. In some examples, to support identifying a set of groups, the UE registration manager 1150 may be configured to or otherwise support components for: identifying a first group based on a first machine learning model from the first list of machine learning models and a first machine learning model from the second list of machine learning models; and identifying a second group based on a second machine learning model from the first list of machine learning models and a second machine learning model from the second list of machine learning models. In some examples, an indication is received in RRC signaling or in MAC-CE.

[0225] In some examples, to support sending an indication of a handover, the unified signaling manager 1155 may be configured to or otherwise support a component for sending a first field and a second field in an indication of a handover, the first field indicating a handover from a first group to a second group for a first set of functions implemented by the UE, and the second field indicating a handover from a third group to a fourth group for a second set of functions implemented by the UE. In some examples, the indication of the handover indicates that the first group is deactivated, the second group is activated, or both. In some examples, the indication of the handover is sent in a UE-specific DCI, in a group-common DCI, or in a MAC-CE.

[0226] In some examples, the group failure reporting manager 1140 may be configured or otherwise support a component for receiving a report from the UE indicating that the functionality implemented by the UE according to the first machine learning model in the first group failed to meet a performance threshold, wherein the indication to switch to the second group is based on the report. In some examples, the group failure reporting manager 1140 may be configured or otherwise support a component for determining, based on the report, that the functionality implemented by the UE according to the second machine learning model in the second group meets the performance threshold, wherein the indication to switch to the second group is sent based on the report.

[0227] In some examples, the function includes at least one of the following: CSI feedback function, channel optimization function, beam management function, CSI-RS optimization function, DMRS function, physical layer function, timing function, synchronization function, space function, power management function, interference management function or positioning function. In some examples, the condition includes at least one of the following: Doppler condition, angular velocity condition, direction of travel condition, travel speed condition, beam width condition, beam condition, power condition, interference condition, service load condition, service mode condition, service type condition, reference signal configuration condition, location condition, antenna configuration condition, coverage condition, connection condition or change of one or more conditions.

[0228] Additionally or alternatively, according to examples as disclosed herein, the communication manager 1120 may support wireless communications at a network entity. In some examples, the group identification manager 1125 may be configured to or otherwise support components for identifying a set of groups for the UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE. The group failure report manager 1140 may be configured to or otherwise support components for receiving a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold.

[0229] In some examples, the unified signaling manager 1155 may be configured to or otherwise support components for sending an indication based on a report that the UE switches from a first group to a second group in a set of groups, wherein functionality implemented by the UE switches from a first machine learning model associated with the first group to a second machine learning model associated with the second group.

[0230] In some examples, UE registration manager 1150 may be configured or otherwise support components for receiving an indication of a set of groups from a UE to register the set of groups, wherein the identification is based on the indication of the set of groups.

[0231] In some examples, network registration manager 1145 may be configured or otherwise support components for sending an indication of a group set to a UE.

[0232] In some examples, the handover manager 1135 may be configured to or otherwise support components for determining that functionality implemented by the UE based on a second machine learning model in a second group satisfies a performance threshold, wherein the report identifies the second group as a preferred group.

[0233] In some examples, the handover manager 1135 may be configured to or otherwise support components for determining, based on the report, that functionality implemented by the UE according to the first machine learning model in the first group fails to meet a performance threshold.

[0234] Fig.12 A diagram of a system 1200 including a device 1205 supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The device 1205 may be an example of a device 905, a device 1005, or a network entity 105 as described herein, or include components thereof. The device 1205 may communicate with one or more network entities 105, one or more UEs 115, or any combination thereof, which may include communication through one or more wired interfaces, through one or more wireless interfaces, or any combination thereof. The device 1205 may include components that support output and acquisition of communications, such as a communication manager 1220, a transceiver 1210, an antenna 1215, a memory 1225, a code 1230, and a processor 1235. These components may be electronically communicated or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., bus 1240).

[0235] The transceiver 1210 may support bidirectional communication via a wired link, a wireless link, or both, as described herein. In some examples, the transceiver 1210 may include a wired transceiver and may communicate bidirectionally with another wired transceiver. Additionally or alternatively, in some examples, the transceiver 1210 may include a wireless transceiver and may communicate bidirectionally with another wireless transceiver. In some examples, the device 1205 may include one or more antennas 1215, which may be capable of (e.g., concurrently) sending or receiving wireless transmissions. The transceiver 1210 may also include a modem for: modulating a signal; providing the modulated signal for transmission (e.g., via one or more antennas 1215, via a wired transmitter); receiving the modulated signal (e.g., from one or more antennas 1215, from a wired receiver); and demodulating the signal. In some implementations, the transceiver 1210 may include one or more interfaces, such as one or more interfaces coupled to one or more antennas 1215 configured to support various receiving or obtaining operations, or one or more interfaces coupled to one or more antennas 1215 configured to support various sending or outputting operations, or a combination thereof. In some implementations, the transceiver 1210 may include or be configured to be coupled to one or more processors or memory components, which are operable to perform or support operations based on received or obtained information or signals, or to generate information or other signals for transmission or other output, or any combination thereof. In some implementations, the transceiver 1210, or the transceiver 1210 and one or more antennas 1215, or the transceiver 1210 and one or more antennas 1215 and one or more processors or memory components (e.g., processor 1235 or memory 1225 or both) may be included in a chip or chip assembly installed in the device 1205. In some examples, the transceiver may be operable to support communications via one or more communication links (eg, communication link 125, backhaul communication link 120, midhaul communication link 162, fronthaul communication link 168).

[0236] Memory 1225 may include RAM and ROM. Memory 1225 may store computer-readable, computer-executable code 1230 including instructions that, when executed by processor 1235, cause device 1205 to perform various functions described herein. Code 1230 may be stored in a non-transitory computer-readable medium, such as system memory or another type of memory. In some cases, code 1230 may not be directly executable by processor 1235, but may (e.g., when compiled and executed) cause a computer to perform the functions described herein. In some cases, memory 1225 may include, among other things, a BIOS that may control basic hardware or software operations, such as interaction with peripheral components or devices.

[0237] Processor 1235 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, an ASIC, a CPU, an FPGA, a microcontroller, a programmable logic device, a discrete gate or transistor logic element, a discrete hardware component, or any combination thereof). In some cases, processor 1235 may be configured to operate a memory array using a memory controller. In some other cases, the memory controller may be integrated into processor 1235. Processor 1235 may be configured to execute computer-readable instructions stored in a memory (e.g., memory 1225) to enable device 1205 to perform various functions (e.g., functions or tasks that support model relationships and unified switching, activation, and deactivation). For example, device 1205 or a component of device 1205 may include processor 1235 and memory 1225 coupled to processor 1235, and processor 1235 and memory 1225 are configured to perform various functions described herein. The processor 1235 may be an example of a cloud computing platform (e.g., one or more physical nodes and supporting software (such as an operating system, a virtual machine, or a container instance)) that can host functions (e.g., by executing code 1230) to perform functions of the device 1205. The processor 1235 may be any one or more suitable processors capable of executing scripts or instructions of one or more software programs stored in the device 1205 (such as within the memory 1225). In some specific implementations, the processor 1235 may be a component of a processing system. A processing system may generally refer to a system or series of machines or components that receive inputs and process these inputs to produce a set of outputs (which may be passed to, for example, other systems or components of the device 1205). For example, the processing system of the device 1205 may refer to a system that includes various other components or subcomponents of the device 1205 (such as the processor 1235, or the transceiver 1210, or the communication manager 1220, or other components or combinations of components of the device 1205). The processing system of device 1205 can be docked with other components of device 1205, and can process information (such as input or signal) received from other components or output information to other components. For example, the chip or modem of device 1205 may include a processing system and one or more interfaces for outputting information or for obtaining information or both. One or more interfaces may be implemented as or otherwise include a first interface configured to output information and a second interface configured to obtain information, or the same interface configured to output information and obtain information, as well as other specific implementations. In some specific implementations, one or more interfaces may refer to an interface between a processing system of a chip or modem and a transmitter, so that device 1205 can send information output from a chip or modem. Additionally or alternatively, in some specific implementations, one or more interfaces may refer to an interface between a processing system of a chip or modem and a receiver, so that device 1205 can obtain information or signal input, and the information can be passed to the processing system.One of ordinary skill in the art will readily recognize that the first interface may also obtain information or signal input, and the second interface may also output information or signal output.

[0238] In some examples, bus 1240 may support communications of a protocol layer (e.g., within a protocol layer) of a protocol stack. In some examples, bus 1240 may support communications associated with a logical channel of a protocol stack (e.g., between protocol layers of a protocol stack), which may include communications performed within a component of device 1205, or communications performed between different components of device 1205 that may be co-located or located in different locations (e.g., where device 1205 may refer to a system in which one or more of communication manager 1220, transceiver 1210, memory 1225, code 1230, and processor 1235 may be located in one of the different components or divided between the different components).

[0239] In some examples, communication manager 1220 may manage aspects of communications with core network 130 (e.g., via one or more wired or wireless backhaul links). For example, communication manager 1220 may manage transfer of data communications for client devices, such as one or more UEs 115. In some examples, communication manager 1220 may manage communications with other network entities 105 and may include a controller or scheduler for controlling communications with UE 115 in collaboration with other network entities 105. In some examples, communication manager 1220 may support an X2 interface within an LTE / LTE-A wireless communication network technology to provide communications between network entities 105.

[0240] According to examples as disclosed herein, the communication manager 1220 may support wireless communications at a network entity. For example, the communication manager 1220 may be configured to or otherwise support a component for identifying a set of groups for a UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The communication manager 1220 may be configured to or otherwise support a component for determining that a condition for implementing each function by the UE has changed by at least a threshold change. The communication manager 1220 may be configured to or otherwise support a component for sending an indication that the UE switches from a first group in the set of groups to a second group based on a threshold change in the condition.

[0241] Additionally or alternatively, according to examples as disclosed herein, the communication manager 1220 may support wireless communications at a network entity. For example, the communication manager 1220 may be configured to or otherwise support components for identifying a set of groups for the UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE. The communication manager 1220 may be configured to or otherwise support components for receiving a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold.

[0242] By including or configuring the communication manager 1220 according to the examples described herein, the device 1205 can support a technique for grouping models for different network functions into groups based on the conditions for performing the functions. This can enable group-based training and registration, group-based switching using unified signaling, group-based activation and deactivation, and group-based model failure reporting.

[0243] In some examples, the communication manager 1220 may be configured to perform various operations (e.g., receive, obtain, monitor, output, transmit) using or otherwise cooperating with the transceiver 1210, one or more antennas 1215 (e.g., where applicable), or any combination thereof. Although the communication manager 1220 is illustrated as a separate component, in some examples, one or more functions described with reference to the communication manager 1220 may be supported or performed by the transceiver 1210, the processor 1235, the memory 1225, the code 1230, or any combination thereof. For example, the code 1230 may include instructions that are executable by the processor 1235 to cause the device 1205 to perform various aspects of the model relationship and unified switching, activation, and deactivation as described herein, or the processor 1235 and the memory 1225 may be otherwise configured to perform or support such operations.

[0244] Fig.13 A flow chart illustrating a method 1300 for supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The operations of the method 1300 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1300 may be implemented by a UE or a component thereof as described in reference to Figures 1 to 8 The described UE 115 may be performed. In some examples, the UE may execute an instruction set to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0245] At 1305, the method may include identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The operations of 1305 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1305 may be performed as described in reference to Figure 7 The described group identification manager 725 is performed.

[0246] At 1310, the method may include receiving an indication to switch from a first group in the set of groups to a second group based on a threshold change of a condition. The operations of 1310 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1310 may be performed as described in reference to Figure 7 The described switching manager 730 is performed.

[0247] At 1315, the method may include switching each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based on the indication of the switch. The operations of 1315 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1315 may be performed as described in reference to Figure 7 The described switching manager 730 is performed.

[0248] Fig.14 A flow chart illustrating a method 1400 for supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The operations of the method 1400 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 1400 may be implemented by a network entity or a component thereof as described in reference to Figure 1 To Figure 4 and Figures 9 to 12 In some examples, the network entity may execute an instruction set to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0249] At 1405, the method may include identifying a set of groups for the UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The operations of 1405 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1405 may be performed as described in reference Fig.11 The described group identification manager 1125 is performed.

[0250] At 1410, the method may include determining that a condition for each function to be implemented by the UE has changed by at least a threshold change. The operations of 1410 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1410 may be performed as described in reference to Fig.11 The described condition manager 1130 is executed.

[0251] At 1415, the method may include sending an indication that the UE switches from a first group in the set of groups to a second group based on a threshold change of the condition. The operations of 1415 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1415 may be performed as described in reference to Fig.11 The described switching manager 1135 is executed.

[0252] Fig.15 A flow chart illustrating a method 1500 for supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The operations of the method 1500 may be implemented by a UE or a component thereof as described herein. For example, the operations of the method 1500 may be implemented by a UE or a component thereof as described in reference to Figures 1 to 8 The described UE 115 may be performed. In some examples, the UE may execute an instruction set to control the functional elements of the UE to perform the described functions. Additionally or alternatively, the UE may use dedicated hardware to perform various aspects of the described functions.

[0253] At 1505, the method may include identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The operations of 1505 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1505 may be performed as described in reference to Figure 7 The described group identification manager 725 is performed.

[0254] At 1510, the method may include determining that the function implemented by the UE according to the first machine learning model in the first group fails to meet the performance threshold. The operation of 1510 can be performed according to the examples disclosed herein. In some examples, aspects of the operation of 1510 can be performed as described in reference Figure 7 The described group failure reporting manager 735 is executed.

[0255] At 1515, the method may include sending a report to a network entity indicating that the first group failed to meet the performance threshold. The operations of 1515 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1515 may be performed as described in reference to Figure 7 The described group failure reporting manager 735 is executed.

[0256] Fig.16 A flow chart illustrating a method 1600 for supporting model relationships and unified switching, activation, and deactivation according to one or more aspects of the present disclosure is shown. The operations of the method 1600 may be implemented by a network entity or a component thereof as described herein. For example, the operations of the method 1600 may be implemented by a network entity or a component thereof as described in reference to Figure 1 To Figure 4 and Figures 9 to 12 In some examples, the network entity may execute an instruction set to control the functional elements of the network entity to perform the described functions. Additionally or alternatively, the network entity may use dedicated hardware to perform various aspects of the described functions.

[0257] At 1605, the method may include identifying a set of groups for the UE, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE. The operations of 1605 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1605 may be performed as described in reference to Fig.11 The described group identification manager 1125 is performed.

[0258] At 1610, the method may include receiving a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold. The operations of 1610 may be performed according to examples as disclosed herein. In some examples, aspects of the operations of 1610 may be performed as described in reference to Fig.11 The described group failure reporting manager 1140 is executed.

[0259] The following provides an overview of various aspects of the disclosure:

[0260] Aspect 1: A method for performing wireless communications at a UE, comprising: identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; receiving an indication to switch from a first group in the set of groups to a second group based at least in part on a threshold change of the condition; and switching each associated function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based at least in part on the indication of the switch.

[0261] Aspect 2: The method according to aspect 1 further comprises: sending an indication of the group set to a network entity to register the group set, wherein the indication of switching is received at least in part based on the registration.

[0262] Aspect 3: A method according to Aspect 2, wherein the indication of the set of groups includes a group identifier for each model, the group identifier is unique for each group in the set of groups, and a shared group identifier between one or more machine learning models defines the group.

[0263] Aspect 4: A method according to any one of Aspects 2 to 3, wherein the indication of the group set includes, for each machine learning model, an associated model or function identifier, and the associated model or function identifier defines the group as including the machine learning model and an associated model or function corresponding to the associated model or function identifier.

[0264] Aspect 5: The method according to any one of aspects 1 to 4, further comprising: receiving an indication of the group set from a network entity, wherein the identification is based at least in part on the indication of the group set.

[0265] Aspect 6: The method according to Aspect 5 further includes: identifying a group identifier for each machine learning model based at least in part on the indication of the group set, wherein the group identifier is unique for each group in the group set, wherein each group is defined by a shared group identifier between one or more machine learning models.

[0266] Aspect 7: The method according to any one of Aspects 5 to 6 further includes: identifying an associated model or function identifier for each model based at least in part on the indication of the group set, wherein the associated model or function identifier defines the group as including the machine learning model and the associated model or function corresponding to the associated model or function identifier.

[0267] Aspect 8: A method according to any one of Aspects 5 to 7, wherein identifying the group set includes: identifying a first list of machine learning models corresponding to a first function and a second list of machine learning models corresponding to a second function; and identifying the first group based at least in part on a first machine learning model from the first list of machine learning models and the first machine learning model from the second list of machine learning models, and identifying the second group based at least in part on a second machine learning model from the first list of machine learning models and the second machine learning model from the second list of machine learning models.

[0268] Aspect 9: The method according to any one of aspects 5 to 8, wherein the indication is received in RRC signaling or in MAC-CE.

[0269] Aspect 10: A method according to any one of Aspects 1 to 9, wherein identifying the group set includes: identifying the indication of switching from the first group to the second group for a first set of functions implemented by the UE based at least in part on a first field in the indication of switching; and identifying the indication of switching from a third group to a fourth group for a second set of functions implemented by the UE based at least in part on a second field in the indication of switching.

[0270] Aspect 11: The method according to aspect 10, wherein the indication of switching indicates that the first group is deactivated, the second group is activated, or both.

[0271] Aspect 12: The method according to any one of aspects 10 to 11, wherein the indication of the handover is received in a UE-specific DCI, in a group common DCI or in a MAC-CE.

[0272] Aspect 13: The method according to any one of Aspects 1 to 12 further includes: determining that the function implemented by the UE according to the first machine learning model in the first group fails to meet a performance threshold; and sending a report to a network entity indicating that the first group fails to meet the performance threshold, wherein the indication of switching is at least partially based on the report.

[0273] Aspect 14: The method according to Aspect 13 also includes: determining that the function implemented by the UE according to the second machine learning model in the second group satisfies the performance threshold, wherein the report identifies the second group as a preferred group.

[0274] Aspect 15: A method according to any one of Aspects 1 to 14, wherein the function includes at least one of the following: CSI feedback function, channel optimization function, beam management function, CSI-RS optimization function, DMRS function, physical layer function, timing function, synchronization function, spatial function, power management function, interference management function or positioning function.

[0275] Aspect 16: A method according to any one of Aspects 1 to 15, wherein the condition includes at least one of the following: Doppler condition, angular velocity condition, direction of travel condition, speed of travel condition, beam width condition, beam condition, power condition, interference condition, traffic load condition, traffic mode condition, traffic type condition, reference signal configuration condition, location condition, antenna configuration condition, coverage condition, connection condition or a change of one or more conditions.

[0276] Aspect 17: A method for performing wireless communications at a network entity, comprising: identifying a set of groups for UEs, each group in the set of groups comprising a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; determining that the condition for each function to be implemented by the UE has changed by at least a threshold change; and sending an indication for the UE to switch from a first group to a second group in the set of groups based at least in part on the threshold change of the condition.

[0277] Aspect 18: The method according to aspect 17 further includes: sending an indication of the group set to the UE to register the group set.

[0278] Aspect 19: A method according to Aspect 18, wherein the indication of the group set includes a group identifier for each machine learning model, the group identifier is unique for each group in the group set, and a shared group identifier between one or more machine learning models defines the group.

[0279] Aspect 20: A method according to any one of Aspects 18 to 19, wherein the indication of the group set includes, for each machine learning model, an associated model or function identifier, and the associated model or function identifier defines the group as including the machine learning model and the associated model or function corresponding to the associated model or function identifier.

[0280] Aspect 21: The method according to any one of aspects 17 to 20 further includes: receiving an indication of the group set from the UE, wherein the identification is based at least in part on the indication of the group set.

[0281] Aspect 22: The method according to Aspect 21 further includes: identifying a group identifier for each machine learning model based at least in part on the indication of the group set, wherein the group identifier is unique for each group in the group set, wherein each group is defined by a shared group identifier between one or more machine learning models.

[0282] Aspect 23: The method according to any one of Aspects 21 to 22 further includes: identifying an associated model or function identifier for each model based at least in part on the indication of the group set, wherein the associated model or function identifier defines the group as including the machine learning model and the associated model or function corresponding to the associated model or function identifier.

[0283] Aspect 24: A method according to any one of Aspects 21 to 23, wherein identifying the group set includes: identifying a first list of machine learning models corresponding to a first function and a second list of machine learning models corresponding to a second function; and identifying the first group based at least in part on a first machine learning model from the first list of machine learning models and the first machine learning model from the second list of machine learning models, and identifying the second group based at least in part on a second machine learning model from the first list of machine learning models and the second machine learning model from the second list of machine learning models.

[0284] Aspect 25: A method according to any one of aspects 21 to 24, wherein the indication is received in RRC signaling or MAC-CE.

[0285] Aspect 26: A method according to any one of Aspects 17 to 25, wherein sending the indication of switching includes: sending a first field and a second field in the indication of switching, the first field indicating switching from the first group to the second group for a first set of functions implemented by the UE, and the second field indicating switching from the third group to the fourth group for a second set of functions implemented by the UE.

[0286] Aspect 27: The method according to aspect 26, wherein the indication of switching indicates that the first group is deactivated, the second group is activated, or both.

[0287] Aspect 28: A method according to any one of aspects 26 to 27, wherein the indication of the handover is sent in a UE-specific DCI, in a group common DCI or in a MAC-CE.

[0288] Aspect 29: The method according to any one of Aspects 17 to 28 further includes: receiving a report from the UE indicating that the function implemented by the UE according to the first machine learning model in the first group fails to meet a performance threshold, wherein the indication to switch to the second group is at least partially based on the report.

[0289] Aspect 30: The method according to Aspect 29 also includes: determining, at least in part based on the report, that the UE satisfies the performance threshold by implementing the function according to the second machine learning model in the second group, wherein the indication of switching to the second group is sent at least in part based on the report.

[0290] Aspect 31: A method according to any one of Aspects 17 to 30, wherein the function includes at least one of the following: CSI feedback function, channel optimization function, beam management function, CSI-RS optimization function, DMRS function, physical layer function, timing function, synchronization function, spatial function, power management function, interference management function or positioning function.

[0291] Aspect 32: A method according to any one of Aspects 17 to 31, wherein the condition includes at least one of the following: Doppler condition, angular velocity condition, direction of travel condition, speed of travel condition, beam width condition, beam condition, power condition, interference condition, traffic load condition, traffic mode condition, traffic type condition, reference signal configuration condition, location condition, antenna configuration condition, coverage condition, connection condition or a change of one or more conditions.

[0292] Aspect 33: A method for performing wireless communication at a UE, comprising: identifying a set of groups, each group in the set of groups including a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; determining that a function implemented by the UE according to a first machine learning model in a first group fails to meet a performance threshold; and sending a report to a network entity indicating that the first group fails to meet the performance threshold.

[0293] Aspect 34: The method according to Aspect 33 also includes: receiving an indication of switching from the first group in the group set to a second group based at least in part on the report; and switching the function implemented by the UE from a first machine learning model associated with the first group to a second machine learning model associated with the second group based at least in part on the indication of the switching.

[0294] Aspect 35: The method according to any one of aspects 33 to 34, further comprising: sending an indication of the group set to the network entity to register the group set.

[0295] Aspect 36: The method according to any one of aspects 33 to 35, further comprising: receiving an indication of the set of groups from the network entity, wherein the identification is based at least in part on the indication.

[0296] Aspect 37: The method according to any one of Aspects 33 to 36 further includes: determining that the function implemented by the UE according to a second machine learning model in the second group satisfies the performance threshold, wherein the report identifies the second group as a preferred group.

[0297] Aspect 38: A method for performing wireless communications at a network entity, comprising: identifying a set of groups for UEs, each group in the set of groups comprising a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; and receiving a report from the UE indicating that a first group in the set of groups failed to meet a performance threshold.

[0298] Aspect 39: The method according to Aspect 38 also includes: sending an indication that the UE switches from the first group in the group set to a second group based at least in part on the report, wherein the function implemented by the UE switches from a first machine learning model associated with the first group to a second machine learning model associated with the second group.

[0299] Aspect 40: The method according to any one of aspects 38 to 39 further includes: receiving an indication of the group set from the UE to register the group set, wherein the identification is based at least in part on the indication.

[0300] Aspect 41: The method according to any one of aspects 38 to 40 further includes: sending an indication of the group set to the UE.

[0301] Aspect 42: The method according to any one of Aspects 38 to 41 further includes: determining that the function implemented by the UE according to a second machine learning model in the second group satisfies the performance threshold, wherein the report identifies the second group as a preferred group.

[0302] Aspect 43: The method according to any one of Aspects 38 to 42 also includes: determining, at least in part based on the report, that the function implemented by the UE according to the first machine learning model in the first group fails to meet the performance threshold.

[0303] Aspect 44: An apparatus for performing wireless communications at a UE, comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform a method according to any one of Aspects 1 to 16.

[0304] Aspect 45: An apparatus for wireless communication at a UE, comprising: at least one component for performing a method according to any one of aspects 1 to 16.

[0305] Aspect 46: A non-transitory computer-readable medium storing code for wireless communication at a UE, the code comprising instructions executable by a processor to perform the method according to any one of aspects 1 to 16.

[0306] Aspect 47: An apparatus for wireless communication at a network entity, comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform a method according to any one of Aspects 17 to 32.

[0307] Aspect 48: An apparatus for wireless communication at a network entity, comprising: at least one component for performing a method according to any one of aspects 17 to 32.

[0308] Aspect 49: A non-transitory computer-readable medium storing code for wireless communication at a network entity, the code comprising instructions executable by a processor to perform the method according to any one of aspects 17 to 32.

[0309] Aspect 50: An apparatus for wireless communication at a UE, comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform a method according to any one of Aspects 33 to 37.

[0310] Aspect 51: An apparatus for wireless communication at a UE, comprising: at least one component for performing a method according to any one of aspects 33 to 37.

[0311] Aspect 52: A non-transitory computer-readable medium storing code for wireless communication at a UE, the code comprising instructions executable by a processor to perform a method according to any one of aspects 33 to 37.

[0312] Aspect 53: An apparatus for wireless communication at a network entity, comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform a method according to any one of Aspects 38 to 43.

[0313] Aspect 54: An apparatus for wireless communication at a network entity, comprising: at least one component for performing a method according to any one of aspects 38 to 43.

[0314] Aspect 55: A non-transitory computer-readable medium storing code for wireless communication at a network entity, the code comprising instructions executable by a processor to perform the method according to any one of aspects 38 to 43.

[0315] It should be noted that the methods described herein describe possible implementations, and that the various operations and steps may be rearranged or otherwise modified and that other implementations are possible. Furthermore, aspects from two or more methods may be combined.

[0316] Although aspects of LTE, LTE-A, LTE-A Pro, or NR systems may be described for example purposes, and LTE, LTE-A, LTE-A Pro, or NR terminology may be used in much of the description, the techniques described herein may also be applicable to networks other than LTE, LTE-A, LTE-A Pro, or NR networks. For example, the techniques described may be applicable to various other wireless communication systems, such as Ultra Mobile Broadband (UMB), Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, and other systems and radio technologies not explicitly mentioned herein.

[0317] The information and signals described herein may be represented using any of a variety of different technologies and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the specification may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0318] The various illustrative blocks and components described in conjunction with the disclosure herein may be implemented or performed using a general purpose processor, a DSP, an ASIC, a CPU, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0319] The functions described herein may be implemented using hardware, software executed by a processor, firmware, or any combination thereof. If implemented using software executed by a processor, the functions may be stored as one or more instructions or codes of a computer-readable medium, or sent using one or more instructions or codes of 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 functions described herein may be implemented using software executed by a processor, hardware, firmware, hard wiring, or a combination of any of these items. Features that implement the functions may also be physically located at different locations, including being distributed so that the functions are implemented at different physical locations.

[0320] Computer-readable medium includes both non-transient computer storage medium and communication medium, and the communication medium includes any medium that promotes computer program to be transferred from one position to another position.Non-transient storage medium can be any available medium that can be accessed by general or special-purpose computer.By way of example and not limitation, non-transient computer-readable medium can include RAM, ROM, electrically erasable programmable ROM (EEPROM), flash memory, compact disc (CD) ROM or other optical disc storage device, disk storage device or other magnetic storage device, or can be used for carrying or storing desired program code parts and can be accessed by general or special-purpose computer or general or special-purpose processor in the form of instruction or data structure Any other non-transient medium.Moreover, any connection is appropriately referred to as computer-readable medium.For example, if software is sent from website, server or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technology such as infrared, radio and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL or wireless technology such as infrared, radio and microwave are included in the definition of computer-readable medium. As used herein, disks and optical disks include CDs, laser disks, optical disks, digital versatile disks (DVDs), floppy disks, and Blu-ray disks. Disks can reproduce data magnetically, and optical disks can reproduce data optically using lasers. Combinations of the above are also included within the scope of computer-readable media.

[0321] As used herein (including in the claims), "or" used in a list of items (e.g., a list of items followed by a phrase such as "at least one of" or "one or more of") indicates an inclusive list, so that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). In addition, as used herein, the phrase "based on" should not be interpreted as a reference to a closed set of conditions. For example, an example step described as "based on condition A" can be based on both condition A and condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase "based on" should be interpreted in the same manner as the phrase "based at least in part on."

[0322] The term "determining" encompasses a variety of actions, and thus, "determining" may include calculating, computing, processing, deriving, investigating, looking up (such as via looking up in a table, database or other data structure), ascertaining, and the like. Furthermore, "determining" may include receiving (e.g., receiving information), accessing (e.g., accessing data stored in a memory), etc. Additionally, "determining" may include parsing, obtaining, selecting, choosing, establishing, and other such similar actions.

[0323] In the drawings, similar components or features may have the same reference label. In addition, various components of the same type may be distinguished by following the reference label with a dash and a second label to distinguish between the similar components. If only the first reference label is used in the specification, the description may apply to any of the similar components having the same first reference label, regardless of the second or other subsequent reference labels.

[0324] The description set forth herein in conjunction with the accompanying drawings describes example configurations and does not represent all examples that may be implemented or within the scope of the claims. The term "example" as used herein means "used as an example, instance, or illustration," rather than "preferred" or "having advantages over other examples." The specific implementation includes specific details to provide an understanding of the described techniques. However, these techniques may be practiced without these specific details. In some cases, known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0325] The description herein is provided to enable one of ordinary skill in the art to implement or use the present disclosure. Various modifications to the present disclosure will be apparent to one of ordinary skill in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. Thus, the present 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. An apparatus for wireless communication at a user equipment (UE), comprising: processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to: identifying a set of groups, each group in the set of groups comprising a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; receiving an indication to switch from a first group to a second group in the set of groups based at least in part on a threshold change in the condition; as well as Based at least in part on the indication of switching, each associated function implemented by the UE is switched from a first machine learning model associated with the first group to a second machine learning model associated with the second group.

2. The apparatus of claim 1 , wherein the instructions are further executable by the processor to cause the apparatus to: An indication of the group set is sent to a network entity to register the group set, wherein the indication of switching is received based at least in part on the registration.

3. The device according to claim 2, wherein: The indication of the set of groups includes, for each model, a group identifier, the group identifier being unique for each group in the set of groups, and A shared group identifier between one or more machine learning models defines the group.

4. The device according to claim 2, wherein: The indication of the set of groups includes, for each machine learning model, an associated model or function identifier, and The associated model or function identifier defines the group as including the machine learning model and the associated model or function corresponding to the associated model or function identifier.

5. The apparatus of claim 1, wherein the instructions are further executable by the processor to cause the apparatus to: An indication of the set of groups is received from a network entity, wherein the identification is based at least in part on the indication of the set of groups.

6. The apparatus of claim 5, wherein the instructions are further executable by the processor to cause the apparatus to: A group identifier for each machine learning model is identified based at least in part on the indication of the set of groups, the group identifier being unique to each group in the set of groups, wherein each group is defined by a shared group identifier between one or more machine learning models.

7. The apparatus of claim 5, wherein the instructions are further executable by the processor to cause the apparatus to: An associated model or function identifier is identified for each model based at least in part on the indication of the set of groups, wherein the associated model or function identifier defines the group as including the machine learning model and the associated model or function corresponding to the associated model or function identifier.

8. The apparatus of claim 5, wherein the instructions for identifying the set of groups are executable by the processor to cause the apparatus to: identifying a first list of machine learning models corresponding to the first functionality and a second list of machine learning models corresponding to the second functionality; and The first group is identified based at least in part on a first machine learning model from the first list of machine learning models and the first machine learning model from the second list of machine learning models, and the second group is identified based at least in part on a second machine learning model from the first list of machine learning models and the second machine learning model from the second list of machine learning models.

9. The apparatus of claim 5, wherein the indication is received in radio resource control (RRC) signaling or in a medium access control-control element (MAC-CE).

10. The apparatus of claim 1, wherein the instructions for identifying the set of groups are executable by the processor to cause the apparatus to: identifying, based at least in part on a first field in the indication of switching, the indication for switching a first set of functions implemented by the UE from the first group to the second group; and An indication to switch from a third group to a fourth group for a second set of functions implemented by the UE is identified based at least in part on a second field in the indication of the switch.

11. The apparatus of claim 10, wherein the indication of switching indicates that the first group is deactivated, the second group is activated, or both.

12. The apparatus of claim 10, wherein the indication of the handover is received in UE-specific downlink control information (DCI), in a group common DCI, or in a medium access control-control element (MAC-CE).

13. The apparatus of claim 1, wherein the instructions are further executable by the processor to cause the apparatus to: determining that the function implemented by the UE according to the first machine learning model in the first group fails to meet a performance threshold; and A report is sent to a network entity indicating that the first group fails to meet the performance threshold, wherein the indication to switch is based at least in part on the report.

14. The apparatus of claim 13, wherein the instructions are further executable by the processor to cause the apparatus to: Determining that implementing the function by the UE according to a second machine learning model in the second group satisfies the performance threshold, wherein the report identifies the second group as a preferred group.

15. The apparatus according to claim 1, wherein the function comprises at least one of the following: a channel state information (CSI) feedback function, a channel optimization function, a beam management function, a CSI-reference signal (RS) optimization function, a demodulation reference signal (DMRS) function, a physical layer function, a timing function, a synchronization function, a spatial function, a power management function, an interference management function or a positioning function.

16. The apparatus according to claim 1, wherein the condition comprises at least one of the following: a Doppler condition, an angular velocity condition, a traveling direction condition, a traveling speed condition, a beam width condition, a beam condition, a power condition, an interference condition, a service load condition, a service mode condition, a service type condition, a reference signal configuration condition, a location condition, an antenna configuration condition, a coverage condition, a connection condition, or a change of one or more conditions.

17. An apparatus for wireless communication at a network entity, comprising: processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to: identifying a set of groups for a user equipment (UE), each group in the set of groups comprising a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; determining that the condition for each function to be implemented by the UE has changed by at least a threshold; as well as An indication is sent for the UE to switch from a first group to a second group in the set of groups based at least in part on the threshold change in the condition.

18. The apparatus of claim 17, wherein the instructions are further executable by the processor to cause the apparatus to: An indication of the group set is sent to the UE to register the group set.

19. The apparatus of claim 18, wherein: The indication of the set of groups includes, for each machine learning model, a group identifier, the group identifier being unique for each group in the set of groups, and A shared group identifier between one or more machine learning models defines the group.

20. The apparatus of claim 18, wherein: The indication of the set of groups includes, for each machine learning model, an associated model or function identifier, and The associated model or function identifier defines the group as including the machine learning model and the associated model or function corresponding to the associated model or function identifier.

21. The apparatus of claim 17, wherein the instructions are further executable by the processor to cause the apparatus to: An indication of the group set is received from the UE, wherein the identification is based at least in part on the indication of the group set.

22. The apparatus of claim 21, wherein the instructions are further executable by the processor to cause the apparatus to: A group identifier for each machine learning model is identified based at least in part on the indication of the set of groups, the group identifier being unique to each group in the set of groups, wherein each group is defined by a shared group identifier between one or more machine learning models.

23. The apparatus of claim 21, wherein the instructions are further executable by the processor to cause the apparatus to: An associated model or function identifier is identified for each model based at least in part on the indication of the set of groups, wherein the associated model or function identifier defines the group as including the machine learning model and the associated model or function corresponding to the associated model or function identifier.

24. The apparatus of claim 21, wherein the instructions for identifying the set of groups are executable by the processor to cause the apparatus to: identifying a first list of machine learning models corresponding to the first functionality and a second list of machine learning models corresponding to the second functionality; and The first group is identified based at least in part on a first machine learning model from the first list of machine learning models and the first machine learning model from the second list of machine learning models, and the second group is identified based at least in part on a second machine learning model from the first list of machine learning models and the second machine learning model from the second list of machine learning models.

25. The apparatus of claim 21, wherein the indication is received in radio resource control (RRC) signaling or in a medium access control - control element (MAC-CE).

26. The apparatus of claim 17, wherein the instructions for sending the indication of switching are executable by the processor to cause the apparatus to: A first field and a second field are sent in the indication of the switching, the first field indicating switching from the first group to the second group for a first set of functions implemented by the UE, and the second field indicating switching from a third group to a fourth group for a second set of functions implemented by the UE.

27. The apparatus of claim 26, wherein the indication of switching indicates that the first group is deactivated, the second group is activated, or both.

28. The apparatus of claim 26, wherein the indication of the handover is sent in UE-specific downlink control information (DCI), in a group common DCI, or in a medium access control - control element (MAC-CE).

29. The apparatus of claim 17, wherein the instructions are further executable by the processor to cause the apparatus to: Receiving a report from the UE indicating that the functionality implemented by the UE according to the first machine learning model in the first group failed to meet a performance threshold, wherein the indication to switch to the second group is based at least in part on the report.

30. The apparatus of claim 29, wherein the instructions are further executable by the processor to cause the apparatus to: Determining, based at least in part on the report, that the UE satisfies the performance threshold by implementing the function according to the second machine learning model in the second group, wherein the indication of switching to the second group is sent based at least in part on the report.

31. An apparatus according to claim 17, wherein the function includes at least one of the following: a channel state information-reference signal (CSI-RS) feedback function, a channel optimization function, a beam management function, a CSI-RS optimization function, a demodulation reference signal (DMRS) function, a physical layer function, a timing function, a synchronization function, a spatial function, a power management function, an interference management function or a positioning function.

32. An apparatus according to claim 17, wherein the condition comprises at least one of the following: a Doppler condition, an angular velocity condition, a traveling direction condition, a traveling speed condition, a beam width condition, a beam condition, a power condition, an interference condition, a service load condition, a service mode condition, a service type condition, a reference signal configuration condition, a location condition, an antenna configuration condition, a coverage condition, a connection condition, or a change of one or more conditions.

33. A method for wireless communication at a user equipment (UE), comprising: identifying a set of groups, each group in the set of groups comprising a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for implementing each function by the UE; receiving an indication to switch from a first group to a second group in the set of groups based at least in part on a threshold change in the condition; as well as Based at least in part on the indication of switching, each associated function implemented by the UE is switched from a first machine learning model associated with the first group to a second machine learning model associated with the second group.

34. The method of claim 33, further comprising: An indication of the group set is sent to a network entity to register the group set, wherein the indication of switching is received based at least in part on the registration.

35. A method for wireless communication at a network entity, comprising: identifying a set of groups for a user equipment (UE), each group in the set of groups comprising a machine learning model for each of one or more functions implemented by the UE, each machine learning model corresponding to a condition for each function to be implemented by the UE; determining that the condition for each function to be implemented by the UE has changed by at least a threshold; as well as An indication is sent for the UE to switch from a first group to a second group in the set of groups based at least in part on the threshold change in the condition.

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