System, method, and apparatus for providing optimized network resources

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

• a Multi-Access Edge Computing (MEC) layer including a wireless network resource optimization application;
• a network exposure function (NEF) configured to manage MEC layer operations;
• at least one data analysis engine configured to analyze measured data from the electromagnetic environment to create analyzed data;
• and
• a machine learning (ML) engine programmed according to customer goals for a customer application in communication with the MEC layer configured to provide predictions about the electromagnetic environment based on the customer goals, the measured data, and historical data and/or external data sources;
• wherein the analyzed data is represented in a vector ensemble class including a center frequency, bandwidth, an upper frequency component, a lower frequency component, signal-to-noise ratio, and time duration for each signal in the electromagnetic environment;
• wherein the wireless network resource optimization application is configured to combine the vector ensemble class for each signal 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 analyzed data is relevant to satisfying the customer goals;
• wherein the wireless network resource optimization application is configured to use the actionable data and the predictions about the electromagnetic environment from the ML engine to optimize network resources and identify physical layer resources required by the customer application;
• and
• wherein the wireless network resource optimization application provides for coexistence of multiple services based on service needs and/or coexistence policies.

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

• at least one data analysis engine configured to receive and analyze measured data from a sensor in the electromagnetic environment to create analyzed data;
• a Multi-Access Edge Computing (MEC) layer in communication with the at least one data analysis engine and including a wireless network resource optimization application;
• and
• a machine learning (ML) engine programmed according to customer goals for a customer application in communication with the MEC layer configured to provide predictions about the electromagnetic environment based on the customer goals, the measured data, and historical data and/or external data sources;
• wherein the analyzed data is represented in a vector ensemble class including a center frequency, bandwidth, an upper frequency component, a lower frequency component, signal-to-noise ratio, and time duration for each signal in the electromagnetic environment;
• wherein the MEC layer is configured to combine the vector ensemble class for each signal in the electromagnetic environment with a customer goals index vector of binary values to create actionable data;
• and
• wherein each binary value of the customer goals index vector represents whether or not a specific piece of the analyzed data is relevant to satisfying the customer goals;
• wherein the wireless network resource optimization application is configured to use the actionable data and the predictions about the electromagnetic environment from the ML engine to optimize network resources and identify physical layer resources required by the customer application;
• and
• wherein the wireless network resource optimization application provides for coexistence of multiple services based on service needs and/or coexistence policies.