# US 12,604,201 B2 · System, method, and apparatus for providing optimized network resources > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,604,201 B2 - Document ID: US12604201B2 - Status: Granted - Kind: B2 - Application number: 19/301,230 - Filed: 2025-08-15 - Published: 2025-12-04 - Granted: 2026-04-14 - Earliest claimed: 2022-08-02 - Assignees: Digital Global Systems, Inc. - Applicants: Digital Global Systems, Inc. - Inventors: Armando Montalvo - Web page: https://patentgrep.com/patent/US12604201B2 ## CPC - H04W 16/10: Dynamic resource partitioning - H04W 16/14: Spectrum sharing arrangements {between different networks} - H04W 24/02: Arrangements for optimising operational condition - H04W 24/04: Arrangements for maintaining operational condition - H04W 24/08: Testing, {supervising or monitoring} using real traffic - H04W 28/0925: {using policies} - H04W 28/0967: {Quality of Service [QoS] parameters} - H04W 72/0453: Resources in frequency domain, e.g. a carrier in FDMA - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and/or user defined policies and rules to extract actionable information to help the customer optimize the network resources. ## Claims ### Claim 1 A system for spectrum management in an electromagnetic environment comprising: a Multi-Access Edge Computing (MEC) layer in a network slice or a subnetwork, wherein the MEC layer is in communication with a radio access network (RAN); at least one data analysis engine configured to analyze the electromagnetic environment, including measured data from the electromagnetic environment to create analyzed data; a wireless network resource optimization application in the MEC layer; wherein the wireless network resource optimization application analyzes detected signal information from the measured data, wherein the detected signal information includes a center frequency and bandwidth of each signal in the electromagnetic environment; wherein the wireless network resource optimization application is configured to obtain statistical information and analyze possible interactions based on the center frequency and the bandwidth of each signal in the electromagnetic environment; wherein the wireless network resource application is configured to use the analyzed data from the data analysis engine, the statistical information, and the possible interactions to create actionable data; and at least one RAN intelligent controller (RIC) within the RAN configured to facilitate radio resource management (RRM) and provide data for optimizing at least one RAN parameter; wherein the at least one RIC uses an artificial intelligence (AI) and/or machine learning (ML) model to further facilitate the RRM which provides for loading balancing, handover control, interference management, and/or resource block management; wherein the wireless network resource optimization application is configured to generate and communicate at least one RAN command to the RAN to change the at least one RAN parameter based on the actionable data and the data for optimizing the at least one RAN parameter. ### Claim 2 (depends on 1) The system of claim 1, wherein the MEC layer is configured to communicate with an Access and Mobility Management Function (AMF) configured to control a RAN control plane. ### Claim 3 (depends on 1) The system of claim 1, wherein the actionable data relates to use of spectrum in a Citizens Broadband Radio Service (CBRS) band. ### Claim 4 (depends on 1) The system of claim 1, wherein the at least one data analysis engine includes a machine learning (ML) engine. ### Claim 5 (depends on 1) The system of claim 1, wherein the actionable data relating to an interference event is provided for reallocation of spectrum. ### Claim 6 (depends on 5) The system of claim 5, wherein the reallocation of spectrum is for a private network. ### Claim 7 (depends on 1) The system of claim 1, wherein the at least one RIC is a near-real time (near-RT) RIC. ### Claim 8 A system for spectrum management in an electromagnetic environment comprising: a Multi-Access Edge Computing (MEC) layer in a network slice or a subnetwork, wherein the MEC layer is in communication with a radio access network (RAN); at least one data analysis engine configured to analyze the electromagnetic environment, including measured data from at least one monitoring sensor to create analyzed data; and at least one RAN intelligent controller (RIC) within the RAN configured to use an artificial intelligence (AI) and/or machine learning (ML) model to analyze network state data to provide data for optimizing at least one RAN parameter; and a wireless network resource optimization application in the MEC layer; wherein the wireless network resource optimization application analyzes detected signal information from the measured data, wherein the detected signal information includes a center frequency and bandwidth of each signal in the electromagnetic environment; wherein the wireless network resource optimization application is configured to obtain statistical information and analyze possible interactions based on the center frequency and the bandwidth of each signal in the electromagnetic environment; and wherein the wireless network resource optimization application is configured to generate and communicate at least one RAN command to the RAN to change the at least one RAN parameter based on the analyzed data from the data analysis engine, the statistical information, the possible interactions, and the data from the RIC for optimizing the at least one RAN parameter; wherein the at least one RIC uses the AI and/or ML model to facilitate radio resource management (RRM) which provides for loading balancing, handover control, interference management, and/or resource block management; and wherein the measured data relates to use of spectrum in a Citizens Broadband Radio Service (CBRS) band. ### Claim 9 (depends on 8) The system of claim 8, wherein the wireless network resource optimization application reallocates the spectrum based on Spectrum Access System (SaS) methodology. ### Claim 10 (depends on 8) The system of claim 8, wherein the at lest one data analysis engine includes a ML engine. ### Claim 11 A method for spectrum management in an electromagnetic environment comprising: at least one data analysis engine analyzing the electromagnetic environment, including measured data from the electromagnetic environment; at least one radio access network (RAN) intelligent controller (RIC) within a RAN using an artificial intelligence (AI) and/or machine learning (ML) model to analyze network state data to provide data for optimizing at least one RAN parameter; at least one wireless network resource optimization application in a Multi-Access Edge Computing (MEC) layer; the wireless network resource optimization application analyzing detected signal information from the measured data, wherein the detected signal information includes a center frequency and bandwidth of at each signal in the electromagnetic environment; and the wireless network resource optimization application obtaining statistical information and analyzing possible interactions based on the center frequency and the bandwidth of each signal in the electromagnetic environment; the wireless network resource optimization application generating and communicating at least one RAN command to the RAN to change the at least one RAN parameter based on the analyzed data from the data analysis engine, the statistical information, the possible interactions, and the data from the RIC for optimizing the at least one RAN parameter; and wherein the at least one RIC uses the AI and/or ML model to facilitate radio resource management (RRM), providing for loading balancing, handover control, interference management, and/or resource block management.