# US 12,225,386 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 12,225,386 B2 - Document ID: US12225386B2 - Status: Granted - Kind: B2 - Application number: 18/783,025 - Filed: 2024-07-24 - Published: 2024-11-14 - Granted: 2025-02-11 - Earliest claimed: 2022-08-02 - Assignees: Digital Global Systems, Inc. - Applicants: Digital Global Systems, Inc. - Inventors: Armando Montalvo - Web page: https://patentgrep.com/patent/US12225386B2 ## CPC - H04W 16/10: Dynamic resource partitioning - H04W 24/02: Arrangements for optimising operational condition - H04W 24/08: Testing, {supervising or monitoring} using real traffic - H04W 24/10: Scheduling measurement reports {; Arrangements for measurement reports} - H04W 28/24: Negotiating SLA [Service Level Agreement]; Negotiating QoS [Quality of Service] - H04W 16/14: Spectrum sharing arrangements {between different networks} - H04W 24/04: Arrangements for maintaining operational condition - H04W 28/0268: {using specific QoS parameters for wireless networks, e.g. QoS class identifier [QCI] or guaranteed bit rate [GBR] (negotiating SLA or negotiating QoS H04W28/24)} - 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 utilization management in an electromagnetic environment comprising: at least one sensor unit configured to measure data; at least one data analysis engine configured to analyze the measured data to create analyzed data; a Multi-Access Edge Computing (MEC) layer in a network slice; a wireless network resource optimization application in the MEC layer; and a machine learning (ML) engine; wherein the ML engine is configured to receive the analyzed data from the at least one data analysis engine and make predictions about the electromagnetic environment based on the analyzed data; and wherein the wireless network resource optimization application is configured to create a vector ensemble class for at least one signal, analyze the at least one signal for possible interactions based on center frequency and/or bandwidth, and analyze the possible interactions, the vector ensemble class, and the predictions about the electromagnetic environment to create actionable data to optimize network resources of the wireless network based on customer goals. ### Claim 2 (depends on 1) The system of claim 1, wherein the customer goals are included in a binary vector. ### Claim 3 (depends on 1) The system of claim 1, wherein the MEC layer is configured to provide a recommendation for optimization of network resources for applications based on environmental conditions. ### Claim 4 (depends on 1) The system of claim 1, wherein the system is configured to perform network slicing to create the network slice. ### Claim 5 (depends on 1) The system of claim 1, wherein the wireless network resource optimization application utilizes a constraint vector. ### Claim 6 (depends on 1) The system of claim 1, wherein the network resources include physical layer resources. ### Claim 7 (depends on 1) The system of claim 1, wherein the network resources include resource blocks, modulation parameters, and bandwidth. ### Claim 8 (depends on 1) The system of claim 1, wherein the actionable data to optimize network resources of the wireless network is based on environmental conditions. ### Claim 9 (depends on 1) The system of claim 1, wherein the wireless network resource optimization application utilizes a constraint vector. ### Claim 10 A system for spectrum utilization management in an electromagnetic environment comprising: a Multi-Access Edge Computing (MEC) layer in a network slice; a wireless network resource optimization application in the MEC layer; at least one data analysis engine configured to analyze measured data to create analyzed data; and a machine learning (ML) engine; wherein the ML engine is configured to receive the analyzed data from the at least one data analysis engine and make predictions about the electromagnetic environment based on the analyzed data; wherein the wireless network resource optimization application is configured to create a vector ensemble class for at least one signal and analyze the vector ensemble class and the predictions about the electromagnetic environment to create actionable data to optimize network resources of the wireless network. ### Claim 11 (depends on 10) The system of claim 10, wherein the actionable data to optimize network resources of the wireless network is based on a binary customer goals index vector. ### Claim 12 (depends on 10) The system of claim 10, wherein the MEC layer is configured to provide a recommendation for optimization of physical layer resources for applications based on environmental conditions. ### Claim 13 (depends on 10) The system of claim 10, wherein the wireless network resource optimization application utilizes a constraint vector. ### Claim 14 A method for spectrum utilization management in an electromagnetic environment comprising: at least one sensor unit measuring data; at least one data analysis engine analyzing the measured data to create analyzed data; at least one machine learning (ML) engine making predictions about the electromagnetic environment based on the analyzed data; a wireless network resource optimization application creating a vector ensemble class for at least one signal; the wireless network resource optimization application analyzing the at least one signal for possible interactions; and the wireless network resource optimization application analyzing the possible interactions, the vector ensemble class, and the predictions about the electromagnetic environment to create actionable data to optimize network resources of the wireless network; wherein the wireless network resource optimization application is in a Multi-Access Edge Computing (MEC) layer. ### Claim 15 (depends on 14) The method of claim 14, wherein the actionable data to optimize network resources is based on customer goals. ### Claim 16 (depends on 14) The method of claim 14, wherein the wireless network resource optimization application analyzes the at least one signal for possible interactions based on center frequency and/or bandwidth. ### Claim 17 (depends on 14) The method of claim 14, further comprising the MEC layer providing a recommendation for optimizing physical layer resources for applications based on environmental conditions. ### Claim 18 (depends on 14) The method of claim 14, wherein the wireless network resource optimization application utilizes a constraint vector. ### Claim 19 (depends on 14) The method of claim 14, wherein a MEC host is deployed at an edge of a radio access network (RAN). ### Claim 20 (depends on 14) The method of claim 14, wherein the MEC layer is in a network slice.