Granted Patent B2

US 12,587,866 · App. 19/299,912 · Granted Mar 24, 2026

Inventor: Armando Montalvo (Winter Garden, FL)

Assignee: Digital Global Systems, Inc.

H04W16/10H04W16/14H04W24/02H04W24/04H04W24/08H04W28/0925H04W28/0967H04W72/0453

Abstract

Claims (46)

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

at least one data analysis engine configured to receive measured data from the electromagnetic environment;

wherein the measured data includes detected signal information for at least one signal in the electromagnetic environment;

wherein the detected signal information includes a center frequency and bandwidth of at least one signal in the electromagnetic environment;

a Multi-Access Edge Computing (MEC) layer in a network slice or a subnetwork, wherein the MEC layer is in communication with an open radio access network (ORAN);

a machine learning (ML) engine programmed according to customer goals regarding a customer application; and

a wireless network resource optimization application in the MEC layer;

wherein the wireless network resource optimization application is configured to obtain statistical information of the detected signal information and analyze possible interactions based on the center frequency and the bandwidth of the at least one signal in the electromagnetic environment;

wherein the at least one data analysis engine is configured to use the ML engine to analyze the measured data to identify relevant information required by the customer application to create analyzed data;

wherein the wireless network resource optimization application is configured to use the analyzed data from the data analysis engine, the statistical information, and the possible interactions to create actionable data for optimizing ORAN resources; and

wherein the wireless network resource optimization application is configured to optimize the ORAN resources based on the actionable data.

2 . The system of claim 1 , wherein the ML engine is configured to use the analyzed data to make predictions about the electromagnetic environment and/or identify physical layer resources required for the customer application.

3 . The system of claim 2 , wherein the physical layer resources include an antenna, resource blocks, modulation parameters, bandwidth, spectrum sharing, and/or spectrum aggregation.

4 . The system of claim 1 , wherein the ORAN resources are optimized by changing at least one parameter of one or more user equipment (UE).

5 . The system of claim 1 , wherein the wireless network resource optimization application is configured to optimize the ORAN resources based on at least one policy.

6 . The system of claim 1 , wherein the wireless network resource optimization application generates at least one ORAN command to change at least one ORAN parameter.

7 . A system for dynamic spectrum utilization management in an electromagnetic environment comprising:

a machine learning (ML) engine programmed according to customer goals regarding a customer application; and

at least one data analysis engine configured to use the ML engine to analyze measured data from the electromagnetic environment to identify relevant information required by the customer application to create analyzed data;

wherein the measured data includes detected signal information for at least one signal in the electromagnetic environment;

wherein the detected signal information includes a center frequency and bandwidth of at least one signal in the electromagnetic environment;

a Multi-Access Edge Computing (MEC) layer in a network slice or a subnetwork, wherein the MEC layer is in communication with an open radio access network (ORAN);

a wireless network resource optimization application in the MEC layer, wherein the wireless network resource optimization application includes a programmable rules and policy editor;

wherein the programmable rules and policy editor includes at least one rule and/or at least one policy;

wherein the wireless network resource optimization application is configured to obtain statistical information of the detected signal information and analyze possible interactions based on the center frequency and the bandwidth of the at least one signal in the electromagnetic environment;

wherein the wireless network resource optimization application is configured to use the analyzed data from the data analysis engine, the statistical information, and the possible interactions to create actionable data in accordance with the at least one rule and/or at least one policy; and

wherein the wireless network resource optimization application is configured to optimize ORAN resources based on the actionable data.

8 . The system of claim 7 , wherein the ML engine is configured to use the analyzed data to make predictions about the electromagnetic environment and/or identify physical layer resources required for the customer application.

9 . The system of claim 8 , wherein the ORAN resources are optimized by reconfiguring at least one parameter of the ORAN and/or the MEC associated with the network slice or the subnetwork.

10 . The system of claim 8 , wherein the ORAN resources are optimized by changing at least one parameter of one or more user equipment (UE).

11 . The system of claim 7 , further comprising at least one monitoring sensor configured to receive the measured data, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.

12 . The system of claim 7 , wherein one or more of the at least one rule and/or the at least one policy is defined by requirements of at least one customer and/or at least one government entity.

13 . A method for dynamic spectrum utilization management in an electromagnetic environment comprising:

providing a Multi-Access Edge Computing (MEC) layer in a network slice or a subnetwork and a wireless network resource optimization application in the MEC layer, wherein the MEC layer is in communication with an open radio access network (ORAN);

a data analysis engine receiving measured data from the electromagnetic environment;

wherein the measured data includes detected signal information for at least one signal in the electromagnetic environment;

wherein the detected signal information includes a center frequency and bandwidth of at least one signal in the electromagnetic environment;

the wireless network resource optimization application obtaining statistical information of the detected signal information and analyzing possible interactions based on the center frequency and the bandwidth of the at least one signal in the electromagnetic environment;

a machine learning (ML) engine programmed according to customer goals regarding a customer application;

the data analysis engine analyzing the measured data from the electromagnetic environment using the ML engine to identify relevant information required by the customer application to create analyzed data;

the wireless network resource optimization application using the analyzed data from the data analysis engine, the statistical information, and the possible interactions to create actionable data; and

the wireless network resource optimization application optimizing ORAN resources using the actionable data.

14 . The method of claim 13 , further comprising at least one monitoring sensor receiving the measured data from the electromagnetic environment located with at least one radio unit (RU).

15 . The method of claim 13 , wherein the ORAN resources are optimized by reconfiguring at least one parameter of the ORAN and/or the MEC associated with the network slice or the subnetwork.

16 . The method of claim 13 , wherein the ORAN resources are optimized by changing at least one parameter of one or more user equipment (UE).

17 . The method of claim 13 , further comprising a machine learning (ML) engine using the analyzed data to make predictions about the electromagnetic environment and/or identify physical layer resources required for the customer application.

Assignments (1)

Continuity (12)

References Cited (271)