# US 12,120,524 B1 · Radio exposure function for telecommunications networks > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,120,524 B1 - Document ID: US12120524B1 - Status: Granted - Kind: B1 - Application number: 18/506,992 - Filed: 2023-11-10 - Granted: 2024-10-15 - Earliest claimed: 2023-11-10 - Assignees: T-Mobile USA, Inc. - Applicants: T-Mobile USA, Inc. - Inventors: Marouane Balmakhtar; Lyle Walter Paczkowski - Web page: https://patentgrep.com/patent/US12120524B1 ## CPC - H04W 12/37: Managing security policies for mobile devices or for controlling mobile applications - G06N 20/00: Machine learning - H04W 28/0215: {based on user or device properties, e.g. MTC-capable devices (services for machine-to-machine communication [M2M] or machine type communication [MTC] H04W4/70; wireless resource selection or allocation plan definition based on terminal or device properties H04W72/51)} - H04W 72/543: based on requested quality, e.g. QoS - H04W 28/16: Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service] - 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 ## Abstract A programmable radio exposure function switches between real-time and near-real-time modes of operation for a radio access network (RAN) intelligent controller of a telecommunications system. An application programming interface is exposed by the radio exposure function and performs radio resource management for the telecommunications system. The application programming interface communicates with services and/or applications to control RAN functions, and allocates RAN resources of the telecommunications system to a user equipment for the services and/or applications. A machine learning module is embedded within the radio exposure function and trained to identify network slices of the telecommunications system for the services and/or applications. The machine learning module analyzes data received from the user equipment to detect that the user equipment has violated a security constraint of the telecommunications system. ## Claims ### Claim 1 A telecommunications system comprising: a programmable radio exposure function configured to: implement real-time and near-real-time modes of operation of a radio access network (RAN) intelligent controller of the telecommunications system, wherein the radio exposure function is distinct from a network exposure function of the telecommunications system; an application programming interface exposed by the radio exposure function to at least one service and/or application, wherein the application programming interface is configured to: perform at least one radio resource management function of the telecommunications system; provide access to at least one RAN function of the telecommunications system to the at least one service and/or application; and allocate at least one RAN resource of the telecommunications system to at least one user equipment for executing the at least one service and/or application; and a machine learning module embedded within the radio exposure function, wherein the machine learning module is trained to: identify at least one network slice of the telecommunications system for the at least one service and/or application to access the telecommunications system; and analyze data received from the at least one user equipment to detect that the at least one user equipment has violated a security constraint of the telecommunications system. ### Claim 2 (depends on 1) The telecommunications system of claim 1, wherein the radio exposure function is located within a 5G Core of the telecommunications system. ### Claim 3 (depends on 1) The telecommunications system of claim 1, wherein the telecommunications system is configured to: process information received from multiple user equipments in a batch mode; and allocate RAN resources of the telecommunications system to the multiple user equipments in the batch mode. ### Claim 4 (depends on 1) The telecommunications system of claim 1, wherein the machine learning module is trained to: determine the at least one RAN resource for the application programming interface to allocate based on the at least one service and/or application and the data received from the at least one user equipment. ### Claim 5 (depends on 1) The telecommunications system of claim 1, wherein the application programming interface is configured to: configure the at least one user equipment to communicate with the radio exposure function, wherein the user equipment is configured to access a citizens broadband radio service. ### Claim 6 (depends on 1) The telecommunications system of claim 1, wherein the application programming interface is configured to: configure a computer device to communicate with the at least one user equipment via the telecommunications system, wherein the computer device lacks access to a citizens broadband radio service. ### Claim 7 (depends on 1) The telecommunications system of claim 1, wherein the application programming interface is configured to: prevent the at least one user equipment from accessing the at least one RAN resource in response to detecting that the at least one user equipment has violated the security constraint. ### Claim 8 At least one non-transitory computer-readable storage medium storing instructions, which, when executed by at least one data processor of a telecommunications system, cause the telecommunications system to execute: a radio exposure function configured to: implement real-time and near-real-time modes of operation of a radio access network (RAN) intelligent controller of the telecommunications system, wherein the radio exposure function is distinct from a network exposure function of the telecommunications system; an application programming interface exposed by the radio exposure function and configured to: provide access to at least one RAN function of the telecommunications system to at least one service and/or application; and allocate at least one RAN resource of the telecommunications system to at least one user equipment for executing the at least one service and/or application; and a machine learning module embedded within the radio exposure function and trained to: identify at least one network slice of the telecommunications system for the at least one service and/or application to access the telecommunications system. ### Claim 9 (depends on 8) The non-transitory computer-readable storage medium of claim 8, wherein the application programming interface is configured to: perform at least one radio resource management function of the telecommunications system. ### Claim 10 (depends on 8) The non-transitory computer-readable storage medium of claim 8, wherein the machine learning module is trained to: analyze data received from the at least one user equipment to detect that the at least one user equipment has violated a security constraint of the telecommunications system. ### Claim 11 (depends on 8) The non-transitory computer-readable storage medium of claim 8, wherein the application programming interface is configured to: prevent the at least one user equipment from accessing the at least one RAN resource in response to detecting that the at least one user equipment has violated a security constraint of the telecommunications system. ### Claim 12 (depends on 8) The non-transitory computer-readable storage medium of claim 8, wherein the at least one service and/or application comprises an online gaming application, and wherein the at least one network slice comprises a high-speed data slice for the online gaming application. ### Claim 13 (depends on 8) The non-transitory computer-readable storage medium of claim 8, wherein the at least one service and/or application comprises an industrial application, and wherein the at least one network slice comprises a low-latency data slice for the industrial application. ### Claim 14 (depends on 8) The non-transitory computer-readable storage medium of claim 8, wherein allocating the at least one RAN resource to the at least one user equipment based on the real-time and near-real-time modes reduces greenhouse gas emissions compared to ad hoc allocation of the at least one RAN resource. ### Claim 15 A computer-implemented method comprising: implementing, by a radio exposure function of a telecommunications system, real-time and near-real-time modes of operation of a radio access network (RAN) intelligent controller of the telecommunications system, wherein the radio exposure function is distinct from a network exposure function of the telecommunications system; allocating, by an application programming interface exposed by the radio exposure function, at least one RAN resource of the telecommunications system to at least one user equipment for executing at least one service and/or application; performing, by the application programming interface, at least one radio resource management function of the telecommunications system; and identifying, by a machine learning module embedded within the radio exposure function, at least one network slice of the telecommunications system for the at least one service and/or application to access the telecommunications system. ### Claim 16 (depends on 15) The method of claim 15, comprising: providing, by the application programming interface, access to the at least one service and/or application to at least one RAN function of the telecommunications system. ### Claim 17 (depends on 15) The method of claim 15, comprising: analyzing, by the machine learning module, data received from the at least one user equipment to detect that the at least one user equipment has violated a security constraint of the telecommunications system. ### Claim 18 (depends on 15) The method of claim 15, wherein the radio exposure function is located outside a 5G Core of the telecommunications system. ### Claim 19 (depends on 15) The method of claim 15, comprising: implementing, by the radio exposure function, the real-time and near-real-time modes of operation in accordance with an open RAN architecture. ### Claim 20 (depends on 15) The method of claim 15, comprising: determining, by the machine learning module, the at least one RAN resource for the application programming interface to allocate based on the at least one service and/or application and data received from the at least one user equipment.