Granted Patent B1

US 12,483,897 · App. 19/184,673 · Granted Nov 25, 2025

Inventor: Armando Montalvo (Winter Garden, FL)

Assignee: Digital Global Systems, Inc.

H04W16/10H04W24/02H04W24/08H04W28/0925H04W28/0967H04W72/0453H04W16/14

Abstract

Claims (41)

1 . A system for spectrum management in an electromagnetic environment comprising:

at least one sensor configured to measure at least one signal from the electromagnetic environment to create measured 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, wherein the MEC layer is in communication with a radio access network (RAN); and

an artificial intelligence (AI) agent in communication with the at least one data analysis engine;

wherein the AI agent is configured to optimize a multiple inputs multiple outputs (MIMO) system based on a conditional entropy value derived from interference between the at least one signal and at least one second signal from the electromagnetic environment;

wherein the MIMO system is a massive MIMO system; and

wherein the AI agent is configured to use the analyzed data and the optimized MIMO system for the equalization or mitigation of signal noise of the at least one signal as the electromagnetic environment changes.

2 . The system of claim 1 , wherein the network slice is administered by a mobile virtual network operator (MVNO).

3 . The system of claim 1 , wherein the AI agent is configured to optimize the MIMO system using a Kullback-Leibler (KL) divergence value.

4 . The system of claim 1 , wherein the AI agent includes a machine learning algorithm.

5 . The system of claim 1 , wherein a slicing architecture implemented in 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.

6 . The system of claim 1 , wherein the AI agent is configured to optimize L1, L2, and/or L3 network parameters in real time.

7 . The system of claim 1 , wherein the AI agent is configured to optimize performance over time based on a training model.

8 . The system of claim 1 , wherein the MIMO system utilizes information theory.

9 . The system of claim 1 , wherein the MEC layer enables serverless computing by hosting Function as a Service (FaaS) at an edge of the RAN.

10 . 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 the AI agent.

11 . The system of claim 1 , further comprising a core network in communication with the MEC layer.

12 . A system for spectrum management in an electromagnetic environment comprising:

at least one data analysis engine configured to analyze measured data from the electromagnetic environment to create analyzed data, wherein the measured data includes at least one signal from the electromagnetic environment;

an artificial intelligence (AI) agent in communication with the at least one data analysis engine;

a Multi-Access Edge Computing (MEC) layer in a network slice, wherein the MEC layer is in communication with a radio access network (RAN); and

a wireless network resource optimization application in the MEC layer, wherein the wireless network resource optimization application includes at least one rule and/or at least one policy;

wherein the AI agent is configured to optimize a multiple inputs multiple outputs (MIMO) system based on a conditional entropy value derived from interference between the at least one signal and at least one second signal from the electromagnetic environment;

wherein the AI agent is configured to use the analyzed data and the optimized MIMO system for the mitigation of signal noise of the at least one signal; and

wherein the wireless network resource optimization application is configured to use the analyzed data and the AI agent to create actionable data for optimizing network resources.

13 . The system of claim 12 , wherein the AI agent is configured to provide for optimization of L1, L2, and/or L3 parameters.

14 . The system of claim 12 , wherein the AI agent is configured to optimize performance over time based on a training model.

15 . The system of claim 12 , wherein the AI agent is configured to provide for validation of sensor fusion based on pattern recognition.

16 . The system of claim 12 , wherein the AI agent is configured to provide for optimization of the network resources based on customer goals.

17 . The system of claim 12 , wherein the AI agent includes a machine learning algorithm.

18 . A system for spectrum management in an electromagnetic environment comprising:

at least one sensor configured to measure at least one signal from the electromagnetic environment to create measured data;

a Multi-Access Edge Computing (MEC) layer in a network slice, wherein the MEC layer is in communication with a radio access network (RAN); and

an artificial intelligence (AI) agent;

wherein the AI agent is configured to optimize a multiple inputs multiple outputs (MIMO) system based on a conditional entropy value of the at least one signal and/or at least one second signal from the electromagnetic environment;

wherein the MIMO system is a massive MIMO system;

wherein the AI agent is configured to use the measured data and the optimized MIMO system for the mitigation of signal noise of the at least one signal as the electromagnetic environment changes; and

wherein the at least one sensor and the AI agent are provided in a single chip, a single chipset, or on a single circuit board.

19 . The system of claim 18 , further comprising a core network in communication with the MEC layer.

20 . The system of claim 18 , wherein the AI agent is configured to include a machine learning algorithm.

Assignments (1)

Continuity (14)

References Cited (270)