Granted Patent B2

US 12,294,865 · App. 18/738,760 · Granted May 6, 2025

Inventor: Armando Montalvo (Winter Garden, FL)

Assignee: Digital Global Systems, Inc.

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

Abstract

Claims (53)

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

at least one monitoring sensor operable to create measured data from the electromagnetic environment;

at least one data analysis engine operable to analyze the 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 a core network; and

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

wherein the AI agent is operable to optimize a multiple inputs multiple outputs (MI MO) system;

wherein the MIMO System is a massive MIMO System;

wherein the at least one data analysis engine is operable to analyze the measured data;

wherein the analyzed data is represented in a vector ensemble class for each signal in the electromagnetic environment;

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 AI agent is included in a RAN Intelligent Controller (RIC); and

wherein the AI agent is operable to dynamically optimize network parameters based on the analyzed data and at least one customer goal in a customer goals index vector, wherein the customer goals index vector is a vector of binary values.

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 operable to optimize the MIMO system using Kullback Leibler (KL) Divergence.

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

5. 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.

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

7. The system of claim 1 , wherein the AI agent is operable 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 at least one AI agent.

11. The system of claim 1 , wherein the core network includes at least one AI agent.

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

at least one monitoring sensor operable to create measured data from the electromagnetic environment;

at least one data analysis engine operable to analyze the measured data;

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 core network; 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 operable to optimize a multiple inputs multiple outputs (MIMO) system;

wherein the MIMO System is a massive MIMO System;

wherein the at least one data analysis engine is operable to analyze the measured data; and

wherein the analyzed data is represented in a vector ensemble class for each signal in the electromagnetic environment;

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

wherein the AI agent is operable to dynamically optimize network parameters based on the analyzed data and at least one customer goal in a customer goals index vector, wherein the customer goals index vector is a vector of binary values.

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

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

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

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

17. The system of claim 12 , wherein the AI agent is operable to include a machine learning algorithm.

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

at least one monitoring sensor operable to create measured data from the electromagnetic environment;

at least one data analysis engine operable to analyze the 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 a core network;

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

wherein the AI agent is operable to optimize a multiple inputs multiple outputs (MIMO) system;

wherein the MIMO System is a massive MIMO System;

wherein the at least one data analysis engine is operable to analyze the measured data;

wherein the analyzed data is represented in a vector ensemble class for each signal in the electromagnetic environment;

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 AI agent is operable to dynamically optimize network parameters based on the analyzed data and at least one customer goal in a customer goals index vector, wherein the customer goals index vector is a vector of binary values; and

wherein the at least one monitoring sensor and the at least one data analysis engine are provided in a single chip, a single chipset, or on a single circuit board.

19. The system of claim 18 , wherein the core network includes at least one AI agent.

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

Assignments (1)

Continuity (13)

References Cited (219)

Cited By (2)

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