# US 12,225,390 B2 · Machine learning model sharing between wireless nodes > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,225,390 B2 - Document ID: US12225390B2 - Status: Granted - Kind: B2 - Application number: 17/454,096 - Filed: 2021-11-09 - Published: 2022-05-12 - Granted: 2025-02-11 - Earliest claimed: 2020-11-11 - Assignees: QUALCOMM Incorporated - Applicants: QUALCOMM Incorporated - Inventors: Hamed Pezeshki; Tao Luo; Yan Zhou - Web page: https://patentgrep.com/patent/US12225390B2 ## CPC - H04W 24/02: Arrangements for optimising operational condition - H04W 24/08: Testing, {supervising or monitoring} using real traffic - H04L 41/145: {involving simulating, designing, planning or modelling of a network} - H04L 41/16: using machine learning or artificial intelligence - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Certain aspects of the present disclosure provide techniques for sharing machine learning models and an indication of transmission and reception points (TRPs) for which the machine learning models are applicable between wireless nodes such as user equipments (UEs) and base stations (BSs). For example, a UE receives an indication of one or more machine learning models associated with at least one TRP of one or more TRPs from a BS. The UE then uses the at least one of the one or more machine learning models for applications related to the at least one TRP. The applications may include a channel state information (CSI) compression, a cross frequency channel prediction, and a beam selection. ## Claims ### Claim 1 An apparatus for wireless communications by a user equipment (UE), comprising: one or more memories comprising instructions; and one or more processors configured, individually or in any combination, to execute the instructions and cause the apparatus to: transmit, to a network entity, an indication that identifies one or more machine learning models that are associated with one or more transmission and reception points (TRPs) of a plurality of TRPs, wherein the indication includes, for each of the one or more machine learning models, an identifier associated with a corresponding machine learning model of the one or more machine learning models; and transmit a notification to switch from a first machine learning model of the one or more machine learning models, the first machine learning model associated with a first TRP of the plurality of TRPs, to a second machine learning model of the one or more machine learning models, the second machine learning model associated with a second TRP of the plurality of TRPs. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein the indication further identifies a single TRP of the plurality of TRPs which is associated with each of the one or more machine learning models. ### Claim 3 (depends on 1) The apparatus of claim 1, wherein the notification is transmitted via a medium access control (MAC) control element (CE) or an uplink control information (UCI). ### Claim 4 (depends on 1) The apparatus of claim 1, wherein the one or more processors are configured, individually or in any combination, to execute the instructions and cause the apparatus to: enable the first machine learning model associated with the first TRP if the first machine learning model has sufficient training for operation; or disable the first machine learning model associated with the first TRP if the first machine learning model does not have the sufficient training for operation. ### Claim 5 (depends on 1) The apparatus of claim 1, wherein the indication further identifies that the one or more machine learning models are associated with all of the plurality of TRPs, and wherein the plurality of TRPs are associated with a cell to which the UE is communicatively coupled. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein the indication further identifies that each of the one or more machine learning models is associated with a subset of the plurality of TRPs. ### Claim 7 An apparatus for wireless communications by a user equipment (UE), comprising: one or more memories comprising instructions; and one or more processors configured, individually or in any combination, to execute the instructions and cause the apparatus to: receive, from a network entity, an indication of one or more machine learning models associated with two or more transmission and reception points (TRPs), wherein the indication includes, for each of the one or more machine learning models, an identifier associated with a corresponding machine learning model of the one or more machine learning models; use at least one machine learning model for applications related to a TRP; receive a notification to switch from a first machine learning model of the one or more machine learning models, the first machine learning model associated with a first TRP of the two or more TRPs, to a second machine learning model of the one or more machine learning models, the second machine learning model associated with a second TRP of the two or more TRPs; and use the second machine learning model associated with the second TRP based on the identifier. ### Claim 8 (depends on 7) The apparatus of claim 7, wherein the indication further identifies a single TRP of the two or more TRPs which is associated with each of the one or more machine learning models. ### Claim 9 (depends on 7) The apparatus of claim 7, wherein the notification is received via a medium access control (MAC) control element (CE) or a downlink control information (DCI). ### Claim 10 (depends on 7) The apparatus of claim 7, wherein the one or more processors are configured, individually or in any combination, to execute the instructions and cause the apparatus to: enable the first machine learning model associated with the first TRP if the first machine learning model has sufficient training for operation; or disable the first machine learning model associated with the first TRP if the first machine learning model does not have the sufficient training for operation. ### Claim 11 (depends on 7) The apparatus of claim 7, wherein the indication further identifies that the one or more machine learning models are associated with all of the two or more TRPs, and wherein the two or more TRPs are associated with a cell to which the UE is communicatively coupled. ### Claim 12 (depends on 7) The apparatus of claim 7, wherein the indication further identifies that each of the one or more machine learning models is associated with a subset of the two or more TRPs. ### Claim 13 An apparatus for wireless communications by a wireless node, comprising: one or more memories comprising instructions; and one or more processors configured, individually or in any combination, to execute the instructions and cause the apparatus to: receive, from a user equipment (UE), an indication that identifies one or more machine learning models as being associated with at least one transmission and reception point (TRP) of a plurality of TRPs supported by the wireless node, wherein the indication includes, for each of the one or more machine learning models, an identifier associated with a corresponding machine learning model of the one or more machine learning models; associate the one or more machine learning models with the at least one TRP; and transmit, to the UE, a notification to switch from a first machine learning model of the one or more machine learning models, the first machine learning model associated with a first TRP of the plurality of TRPs, to a second machine learning model of the one or more machine learning models, the second machine learning model associated with a second TRP of the plurality of TRPs. ### Claim 14 (depends on 13) The apparatus of claim 13, wherein the indication further identifies a single TRP of the plurality of TRPs which is associated with each of the one or more machine learning models. ### Claim 15 (depends on 13) The apparatus of claim 13, wherein the indication further identifies that the one or more machine learning models are associated with all of the plurality of TRPs, and wherein the plurality of TRPs are associated with a cell. ### Claim 16 (depends on 13) The apparatus of claim 13, wherein the indication further identifies that each of the one or more machine learning models is associated with a subset of the plurality of TRPs. ### Claim 17 An apparatus for wireless communications by a wireless node, comprising: one or more memories comprising instructions; and one or more processors configured, individually or in any combination, to execute the instructions and cause the apparatus to: transmit, to a user equipment (UE), an indication of at least one machine learning model of one or more machine learning models, wherein the indication identifies, for each of the one or more machine learning models, an identifier associated with a corresponding machine learning model of the one or more machine learning models; and transmit, to the UE, a notification to switch from a first machine learning model of the one or more machine learning models, the first machine learning model associated with a first transmission and reception point (TRP) of two or more TRPs to a second machine learning model of the one or more machine learning models, the second machine learning model associated with a second TRP of the two or more TRPs. ### Claim 18 (depends on 17) The apparatus of claim 17, wherein the indication further identifies a single TRP of the two or more TRPs which is associated with each of the one or more machine learning models. ### Claim 19 (depends on 17) The apparatus of claim 17, wherein the indication further identifies that the one or more machine learning models are associated with all of the two or more TRPs, and wherein the two or more TRPs are associated with a cell. ### Claim 20 (depends on 17) The apparatus of claim 17, wherein the indication further identifies that each of the one or more machine learning models is associated with a subset of the two or more TRPs. ### Claim 21 (depends on 13) The apparatus of claim 13, wherein the one or more processors are configured, individually or in any combination, to execute the instructions and cause the apparatus to: enable the first machine learning model associated with the first TRP if the first machine learning model has sufficient training for operation; or disable the first machine learning model associated with the first TRP if the first machine learning model does not have the sufficient training for operation. ### Claim 22 (depends on 17) The apparatus of claim 17, wherein the one or more processors are configured, individually or in any combination, to execute the instructions and cause the apparatus to: enable the first machine learning model associated for the first TRP if the first machine learning model has sufficient training for operation; or disable the first machine learning model associated for the first TRP if the first machine learning model does not have the sufficient training for operation. ### Claim 23 (depends on 1) The apparatus of claim 1, wherein the one or more processors are configured, individually or in any combination, to execute the instructions and cause the apparatus to select the one or more machine learning models that are associated with the one or more TRPs of the plurality of TRPs. ### Claim 24 (depends on 7) The apparatus of claim 7, wherein the one or more processors are configured, individually or in any combination, to execute the instructions and cause the apparatus to select the at least one machine learning model from the one or more machine learning models, for the applications related to the TRP, based at least in part on a connection state with the TRP. ### Claim 25 (depends on 17) The apparatus of claim 17, wherein the one or more processors are configured, individually or in any combination, to execute the instructions and cause the apparatus to select the at least one machine learning model, from the one or more machine learning models associated with the two or more TRPs supported by the wireless node, for applications related to a TRP, based at least in part on a connection state with the TRP. ### Claim 26 (depends on 1) The apparatus of claim 1, wherein the identifier comprises a TRP index. ### Claim 27 (depends on 1) The apparatus of claim 1, wherein the notification is associated with mobility of the UE between the first TRP and the second TRP. ### Claim 28 (depends on 7) The apparatus of claim 7, wherein the identifier comprises a TRP index, and wherein the notification is associated with mobility of the UE between the first TRP and the second TRP. ### Claim 29 (depends on 13) The apparatus of claim 13, wherein the identifier comprises a TRP index, and wherein the notification is associated with mobility of the UE between the first TRP and the second TRP. ### Claim 30 (depends on 17) The apparatus of claim 17, wherein the identifier comprises a TRP index, and wherein the notification is associated with mobility of the UE between the first TRP and the second TRP.