# US 2025/0159486 A1 · 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 2025/0159486 A1 - Document ID: US20250159486A1 - Status: Published application - Kind: A1 - Application number: 19/016,139 - Filed: 2025-01-10 - Published: 2025-05-15 - Earliest claimed: 2022-08-02 - Assignees: Digital Global Systems, Inc. - Applicants: Digital Global Systems, Inc. - Inventors: Armando Montalvo; Khashayar Kotobi - Web page: https://patentgrep.com/patent/US20250159486A1 ## CPC - H04W 16/10: Dynamic resource partitioning - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 20/00: Machine learning - H04W 24/02: Arrangements for optimising operational condition - H04W 24/08: Testing, {supervising or monitoring} using real traffic - H04W 28/0925: {using policies} - H04W 28/0942: {based on measured or predicted load of entities- or links} - H04W 28/0967: {Quality of Service [QoS] parameters} - H04W 72/0453: Resources in frequency domain, e.g. a carrier in FDMA - H04W 16/14: Spectrum sharing arrangements {between different networks} - H04W 84/04: Large scale networks; Deep hierarchical 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 ## 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: at least one sensor operable to create measured data from the electromagnetic environment; a Multi-Access Edge Computing (MEC) layer in a network slice, wherein the MEC layer is in communication with a core network; at least one data analysis engine in the MEC layer operable to analyze the measured data; and an artificial intelligence (AI) agent in communication with the at least one data analysis engine; wherein the at least one data analysis engine uses an equalizer to reduce signal interference and/or distortion of at least one signal based on information from at least one other signal in a massive multiple inputs multiple outputs (MIMO) system; wherein the AI agent is operable to make predictions about the electromagnetic environment based on the analyzed data and/or a previous dataset; and wherein the equalizer is operable to use the predictions about the electromagnetic environment for equalization and/or mitigation of signal noise of the at least one signal as the electromagnetic environment changes. ### Claim 2 (depends on 1) The system of claim 1, wherein the network slice is administered by a mobile virtual network operator (MVNO). ### Claim 3 (depends on 1) The system of claim 1, wherein the equalizer reduces the signal interference and/or the distortion of the at least one signal using a Kullback Leibler (KL) Divergence value. ### Claim 4 (depends on 1) The system of claim 1, wherein the AI agent is operable to utilize at least one AI or machine learning (ML) algorithm to make predictions about the electromagnetic environment. ### Claim 5 (depends on 1) The system of claim 1, wherein slicing architecture of the MEC layer is based on a plurality of factors including geographic scope, specificity of services, flexible architecture, and/or implementation of MEC applications as part of a slice access point. ### Claim 6 (depends on 1) The system of claim 1, wherein the AI agent is operable to optimize L1, L2, and/or L3 network parameters in real time. ### Claim 7 (depends on 1) The system of claim 1, wherein the AI agent is operable to optimize performance over time based on a training model. ### Claim 8 (depends on 1) The system of claim 1, wherein the equalizer utilizes information theory. ### Claim 9 (depends on 1) The system of claim 1, wherein the MEC layer is in communication with at least one a radio access network (RAN). ### Claim 10 (depends on 9) The system of claim 9, wherein the MEC layer enables serverless computing by hosting Function as a Service (FaaS) at an edge of the RAN. ### Claim 11 (depends on 1) The system of claim 1, further comprising at least one radio unit, distributed unit, and/or central unit, wherein at least one of the at least one radio unit, distributed unit, and/or central unit includes at least one AI agent. ### Claim 12 A system for spectrum management in an electromagnetic environment comprising: at least one sensor operable to create measured data from the electromagnetic environment; a Multi-Access Edge Computing (MEC) layer in a network slice, wherein the MEC layer is in communication with a core network; at least one data analysis engine in the MEC layer operable to analyze the measured data; and an artificial intelligence (AI) agent in communication with the at least one data analysis engine; wherein the at least one data analysis engine uses an equalizer to reduce signal interference and/or distortion of at least one signal based on information from at least one other signal; wherein the AI agent is operable to make predictions about the electromagnetic environment based on the analyzed data and/or a previous dataset; wherein the equalizer is operable to use the predictions about the electromagnetic environment for equalization and/or mitigation of signal noise of the at least one signal; and wherein the MEC layer is operable to use the predictions from the AI agent to create actionable data for optimizing network resources. ### Claim 13 (depends on 12) The system of claim 12, wherein the AI agent is operable to provide for optimization of L1, L2, and/or L3 parameters. ### Claim 14 (depends on 12) The system of claim 12, wherein AI agent is operable to optimize performance over time based on a training model. ### Claim 15 (depends on 12) The system of claim 12, wherein the AI agent is operable to provide for validation of sensor fusion based on pattern recognition. ### Claim 16 (depends on 12) The system of claim 12, wherein the actionable data is based on customer goals. ### Claim 17 (depends on 12) The system of claim 12, wherein the equalizer utilizes information theory. ### Claim 18 A system for spectrum management in an electromagnetic environment comprising: at least one sensor operable to create measured data from the electromagnetic environment; a Multi-Access Edge Computing (MEC) layer in a network slice, wherein the MEC layer is in communication with a core network; at least one data analysis engine in the MEC layer operable to analyze the measured data; and an artificial intelligence (AI) agent in communication with the at least one data analysis engine; wherein the at least one data analysis engine uses an equalizer to reduce signal interference and/or distortion of at least one signal based on information from at least one other signal. wherein the AI agent is operable to make predictions about the electromagnetic environment based on the analyzed data and/or a previous dataset; wherein the equalizer is operable to use the predictions about the electromagnetic environment for equalization and/or mitigation of signal noise of the at least one signal; and wherein the at least one sensor and the at least one data analysis engine are provided in a single chip, a single chipset, or on a single circuit board. ### Claim 19 (depends on 18) The system of claim 18, wherein the equalizer reduces signal interference and/or distortion of at least one signal using a Kullback Leibler (KL) Divergence value. ### Claim 20 (depends on 18) The system of claim 18, wherein the AI agent is operable to provide recommendations for optimization of L1, L2, and/or L3 network parameters in real time.