Granted Patent B2

US 12,684,360 · App. 19/371,510 · Granted Jul 14, 2026

Inventor: Armando Montalvo (Winter Garden, FL)

Assignee: Digital Global Systems, Inc.

H04W16/10H04W24/02H04W24/08H04W24/10H04W28/24H04W16/14H04W24/04H04W28/0268

Abstract

Claims (56)

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

a Mult-Access Edge Computing (MEC) layer in a wireless network;

at least one data analysis engine in the MEC layer configured to analyze detected signal information from the electromagnetic environment to create measured data;

wherein the measured data is represented in a vector ensemble class for each signal of the detected signal information from the electromagnetic environment;

wherein the data analysis engine is configured to identify information in the measured data relevant to at least one customer goal for a customer application to create analyzed data;

a wireless network resource optimization application in the MEC layer; and

a machine learning (ML) engine in the MEC layer programmed according to the at least one customer goal;

wherein the ML engine is configured to use the analyzed data to make predictions about the electromagnetic environment based on the at least one customer goal and the measured data;

wherein the wireless network resource optimization application is configured to combine the vector ensemble class for each signal of the detected signal information from the electromagnetic environment with a customer goals index vector of binary values to create actionable data;

wherein each binary value of the customer goals index vector represents whether or not a specific piece of the detected signal information from the electromagnetic environment is relevant to satisfying the at least one customer goal;

wherein the wireless network resource optimization application is configured to analyze each signal in the electromagnetic environment for possible interactions based on center frequency and a combination of bandwidth and an upper and lower frequency component, and utilize the actionable data, the possible interactions, and the predictions about the electromagnetic environment to optimize network resources of a wireless network for the customer application based on environmental conditions;

wherein the MEC layer is operable to generate at least one event and conditions to trigger the at least one event based on the at least one customer goal; and

wherein the MEC layer is operable to emit an alert when the conditions for the at least one event are met.

2 . The system of claim 1 , wherein the wireless network resource optimization application is configured to optimize network resources by reconfiguring the MEC layer associated with a network slice or a subnetwork.

3 . The system of claim 1 , wherein the MEC layer is configured to provide a recommendation for optimization of network resources for the customer application based on environmental conditions.

4 . The system of claim 3 , wherein the network resources include physical layer resources.

5 . The system of claim 3 , wherein the network resources include resource blocks, modulation parameters, and/or bandwidth.

6 . The system of claim 1 , wherein the system is configured to perform network slicing to create the network slice.

7 . The system of claim 1 , wherein the wireless network resource optimization application utilizes a constraint vector.

8 . The system of claim 7 , wherein the MEC layer is configured to generate the constraint vector from the at least one customer goal.

9 . The system of claim 1 , wherein the system includes a MEC host deployed at an edge of a radio access network (RAN).

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

a Multi-Access Edge Computing (MEC) layer in a wireless network; and

a wireless network resource optimization application in the MEC layer including at least one data analysis engine for analyzing detected signal information from a radiofrequency (RF) environment to create measured data;

wherein in the measured data is represented in a vector ensemble class for each signal of the detected signal information from the RF environment;

wherein the data analysis engine is configured to identify information in the measured data relevant to at least one customer goal for a customer application to create analyzed data;

a machine learning (ML) engine in the MEC layer programmed according to the at least one customer goal;

wherein the detected signal information includes a center frequency, bandwidth, upper frequency component, and lower frequency component of at least one signal from the RF environment;

wherein the at least one data analysis engine is configured to analyze each signal of the detected signal information from the RF environment to obtain statistical information and analyze possible interactions based on the center frequency, bandwidth, upper frequency component, and lower frequency component to create analyzed data;

wherein the ML engine is configured to use the analyzed data to make predictions about the RF environment based on the at least one customer goal and the measured data;

wherein the wireless network resource optimization application is configured to combine the vector ensemble class for each signal in the RF environment with a customer goals index vector of binary values to create actionable data;

wherein each binary value of the customer goals index vector represents whether or not a specific piece of the detected signal information is relevant to satisfying the customer goals;

wherein the wireless network resource application is configured to utilize the actionable data, the possible interactions, and the predictions about the RF environment to optimize network resources of a wireless network for the customer application based on environmental conditions;

wherein the MEC layer is operable to generate at least one event and conditions to trigger the at least one event based on the at least one customer goal;

wherein the MEC layer is operable to create a prediction about the at least one event using the ML engine, wherein the prediction describes whether the at least one event will occur; and

wherein the MEC layer is operable to emit an alert when the prediction indicates that the at least one event will occur.

11 . The system of claim 10 , wherein the wireless network resource optimization application is configured to optimize network resources of a wireless network by reconfiguring the MEC layer associated with a network slice or a subnetwork.

12 . The system of claim 10 , wherein the MEC layer is configured to provide a recommendation for optimization of physical layer resources for the customer application based on environmental conditions.

13 . The system of claim 10 , wherein the wireless network resource optimization application is configured to analyze the prediction about the at least one event to create actionable data to optimize network resources of a wireless network.

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

at least one data analysis engine analyzing detected signal information from the electromagnetic environment to create measured data;

wherein in the measured data is represented in a vector ensemble class for each signal of the detected signal information in the electromagnetic environment;

the data analysis engine identifying information in the measured data relevant to at least one customer goal for a customer application to create analyzed data;

at least one machine learning (ML) engine programmed according to the at least one customer goal making predictions about the electromagnetic environment based on the at least one customer goal and the measured data;

a wireless network resource optimization application combining the vector ensemble class for each signal of the detected signal information in the electromagnetic environment with a customer goals index vector of binary values to create actionable data;

wherein each binary value of the customer goals index vector represents whether or not a specific piece of the detected signal information is relevant to satisfying the customer goals;

the wireless network resource optimization application utilizing the actionable data, the possible interactions, and the predictions about the electromagnetic environment to optimize network resources of a wireless network for the at least one customer application based on environmental conditions;

a Multi-Access Edge Computing (MEC) layer in a wireless network generating at least one event and conditions to trigger the at least one event based on the at least one customer goal; and

the MEC layer emitting an alert when the conditions for the at least one event are met;

wherein the at least one data analysis engine, the ML engine, and the wireless network resource optimization application are in the MEC layer.

15 . The method of claim 14 , wherein the wireless network resource optimization application is configured to optimize network resources by reconfiguring the MEC layer associated with a network slice or a subnetwork.

16 . The method of claim 14 , further comprising the MEC layer providing a recommendation for optimizing physical layer resources for the customer application based on environmental conditions.

17 . The method of claim 14 , wherein the wireless network resource optimization application utilizes a constraint vector.

18 . The method of claim 14 , further comprising a MEC host deployed at an edge of a radio access network (RAN).

19 . The method of claim 14 , wherein the MEC layer is in a network slice.

20 . The method of claim 19 , further comprising performing network slicing to create the network slice.

Assignments (1)

Continuity (13)

References Cited (305)