# US 2023/0276263 A1 · Managing a wireless device that is operable to connect to a communication network > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0276263 A1 - Document ID: US20230276263A1 - Status: Published application - Kind: A1 - Application number: 18/015,978 - Filed: 2021-07-09 - Published: 2023-08-31 - Earliest claimed: 2020-07-13 - Assignees: Telefonaktiebolaget LM Ericsson (publ) - Applicants: Telefonaktiebolaget LM Ericsson (publ) - Inventors: Henrik Rydén; Pablo Soldati - Web page: https://patentgrep.com/patent/US20230276263A1 ## CPC - H04W 24/02: Arrangements for optimising operational condition - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0495: Quantised networks; Sparse networks; Compressed networks - G06N 3/098: Distributed learning, e.g. federated learning - G06N 20/00: Machine learning - H04L 41/16: using machine learning or artificial intelligence - H04W 8/22: Processing or transfer of terminal data, e.g. status or physical capabilities - G06N 3/045: Combinations of networks - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A method is disclosed for managing a wireless device that is operable to connect to a communication network. The communication network comprises a RAN, and the wireless device has available for execution multiple ML models each operable to provide an output, on the basis of which at least one RAN operation performed by the wireless device may be configured. The method, performed by the wireless device, comprises determining which of said available ML models should be stored in the wireless device. The method further comprises, in response to determining that at least one of said available ML models should be stored in the wireless device, storing said at least one of said available ML models, and, in response to determining that at least one of said available ML models should not be stored in the wireless device, deleting said at least one of said available ML models. ## Claims ### Claim 1 A method for managing a wireless device that is operable to connect to a communication network, wherein the communication network comprises a Radio Access Network, RAN, and wherein the wireless device has available for execution a plurality of Machine Learning, ML, models that are each operable to provide an output, on the basis of which at least one RAN operation performed by the wireless device may be configured, the method, performed by the wireless device, comprising: determining which of said available ML models should be stored in the wireless device ; in response to determining that at least one of said available ML models should be stored in the wireless device, storing said at least one of said available ML models in a first memory in the wireless device; and in response to determining that at least one of said available ML models should not be stored in the wireless device, deleting said at least one of said available ML models from the first memory in the wireless device. ### Claim 2 (canceled) ### Claim 3 (canceled) ### Claim 4 (depends on 1) The method as claimed in claim 1, further comprising transmitting to at least one RAN node information identifying said at least one of said available ML models stored in the first memory in the wireless device. ### Claim 5 (depends on 4) The method as claimed in claim 4, further comprising transmitting to the at least one RAN node information indicating a reason for storing said at least one of said available ML models in the first memory in the wireless device. ### Claim 6 (depends on 1) The method as claimed in claim 1, further comprising transmitting to at least one RAN node information identifying said at least one of said available ML models deleted from the first memory in the wireless device. ### Claim 7 (depends on 6) The method as claimed in claim 6, further comprising transmitting to the at least one RAN node information indicating a reason for deleting said at least one of said available ML models from the first memory in the wireless device. ### Claim 8 (depends on 7) The method as claimed in claim 7, wherein the information indicating a reason for deleting said at least one of said available ML models from the first memory in the wireless device comprises an indication of a reason selected from a group comprising at least one of: the ML model being too big; the ML model performance being inadequate; the ML model execution time being too long; and the ML model battery consumption being too high. ### Claim 9 (depends on 1) The method as claimed in claim 1, further comprising, in response to determining that at least one of said available ML models should not be stored in the wireless device, storing said at least one of said available ML models in a second memory of the wireless device separate from the first memory of the wireless device . ### Claim 10 (depends on 1) The method as claimed in claim 1, comprising determining which of said available ML models should be stored in the wireless device based on a number of times that the wireless device has been in a specific area of the RAN. ### Claim 11 (depends on 1) The method as claimed in claim 1, comprising determining whether a specific one of said available ML models should be stored in the wireless device based on a number of times that the wireless device has been configured to use said specific one of said available ML models. ### Claim 12 (depends on 1) The method as claimed in claim 1, wherein the step of determining which of said available ML models should be stored in the wireless device comprises selecting a number of said available ML models that the wireless device has been configured to use most often. ### Claim 13 (canceled) ### Claim 14 (canceled) ### Claim 15 (depends on 1) The method as claimed in claim 1, comprising performing the step of determining which of said available ML models should be stored in the wireless device in response to one of: determining that the first memory of the wireless device is full to a predetermined level; being configured to receive a new model; receiving an indication that one of said available ML models is outdated; determining that a validity time associated with one of said available ML models has expired; a change in a Radio Resource Control, RRC, state of the wireless device; the wireless device handing over to a new RAN node; or a change in a tracking area, operator, or country code. ### Claim at position 16 16-24. (canceled) ### Claim 25 A method for managing a wireless device that is operable to connect to a communication network, wherein the communication network comprises a Radio Access Network, RAN, the method, performed by a RAN node of the communication network, comprising: receiving, from the wireless device, information identifying at least one Machine Learning, ML, model that is operable to provide an output on the basis of which at least one RAN operation performed by the wireless device may be configured, said information indicating that said at least one ML model has been deleted from a first memory in the wireless device . ### Claim 26 (depends on 25) The method as claimed in claim 25, further comprising receiving from the wireless device information indicating a reason for deleting said at least one ML model from the first memory in the wireless device. ### Claim 27 (depends on 26) The method as claimed in claim 26, wherein the information indicating a reason for deleting said at least one of said available ML models from the first memory in the wireless device comprises an indication of a reason selected from a group comprising at least one of: the ML model being too big; the ML model performance being inadequate; the ML model execution time being too long; and the ML model battery consumption being too high. ### Claim 28 (depends on 25) The method as claimed in claim 25, further comprising receiving, from the wireless device, information identifying at least one ML model that is operable to provide an output on the basis of which at least one RAN operation performed by the wireless device, said information indicating that said at least one ML model has been stored in the first memory in the wireless device. ### Claim 29 (canceled) ### Claim 30 (depends on 25) The method as claimed in claim 25, further comprising, as initial steps: sending an ML model to the wireless device; and requesting the wireless device to inform the RAN node in the event that the ML model is deleted from the first memory in the wireless device. ### Claim 31 (depends on 25) The method as claimed in claim 25, further comprising, in response to receiving information identifying at least one ML model that has been deleted from the first memory in the wireless device, creating a new ML model based on the received information. ### Claim 32 (depends on 31) The method as claimed in claim 31, comprising creating the new ML model in response to receiving information from a number of wireless devices indicating that a specific ML model has been deleted from respective memories in the wireless devices, and wherein the number of wireless devices exceeds a threshold number. ### Claim at position 25 33-35. (canceled) ### Claim 36 A wireless device that is operable to connect to a communication network, wherein the communication network comprises a Radio Access Network, RAN, the wireless device comprising processing circuitry configured to cause the wireless device to: determine which of a plurality of available ML models should be stored in the wireless device; in response to determining that at least one of said available ML models should be stored in the wireless device, store said at least one of said available ML models in a first memory in the wireless device; and in response to determining that at least one of said available ML models should not be stored in the wireless device, delete said at least one of said available ML models from the first memory in the wireless device. ### Claim 37 (canceled) ### Claim 38 A Radio Access Network, RAN, node of a communication network comprising a RAN, wherein the RAN node is for managing a wireless device that is operable to connect to the communication network, and wherein the RAN node comprises processing circuitry configured to cause the RAN node to: receive, from the wireless device, information identifying at least one Machine Learning, ML, model that is operable to provide an output on the basis of which at least one RAN operation performed by the wireless device may be configured, said information indicating that said at least one ML model has been deleted from a first memory in the wireless device. ### Claim 39 (canceled)