System, method, and apparatus for providing optimized network resources

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 automatically adjust network parameters of the at least one data analysis engine over time based on at least one customer goal, at least one policy, and/or at least one key performance indicator (KPI);

wherein the at least one data analysis engine uses an equalizer to reduce signal interference and/or distortion of at least one signal based on information from at least one other signal for preemptive removal of the at least one other signal before equalization;

wherein the equalizer is operable to use machine learning (ML) and/or AI for the equalization and mitigation of signal noise of the at least one signal in real-time 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 equalizer reduces the signal interference and/or the distortion of the at least one signal using a Kullback Leibler (KL) Divergence value.

4. The system of claim 1 , wherein the AI agent is operable to utilize at least one AI or machine learning (ML) algorithm to make predictions about the electromagnetic environment.

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 provide at least one recommendation for L1, L2, and/or L3 network parameters in real time.

7. The system of claim 1 , wherein the AI agent is operable to provide at least one recommendation to optimize performance over time based on a training model.

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

9. 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.

10. The system of claim 1 , wherein the at least one signal and at least one other signal have an overlapping bandwidth.

11. The system of claim 10 , wherein the equalizer reduces interference gain between the at least one signal and the at least one other signal with the overlapping bandwidth.

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;

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 automatically adjust network parameters of the at least one data analysis engine over time based on at least one customer goal, at least one policy, and/or at least one key performance indicator (KPI);

wherein the at least one data analysis engine uses an equalizer to reduce signal interference and/or distortion of at least one signal based on information from at least one other signal for preemptive removal of the at least one other signal before equalization;

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 MEC layer is operable to use the predictions from the AI agent to create actionable data for optimizing network resources; and

wherein the equalizer is operable to use machine learning (ML) and/or AI for the equalization and mitigation of signal noise of the at least one signal in real-time as the electromagnetic environment changes.

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

14. The system of claim 12 , wherein the AI agent is operable to provide at least one recommendation 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 actionable data is based on the at least one customer goal.

17. The system of claim 12 , wherein the equalizer utilizes information theory.

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; and

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

wherein the AI agent is operable to automatically adjust network parameters of the at least one data analysis engine over time based on at least one customer goal, at least one policy, and/or at least one key performance indicator (KPI);

wherein the at least one data analysis engine uses an equalizer to reduce signal interference and/or distortion of at least one signal based on information from at least one other signal for preemptive removal of the at least one other signal before equalization;

wherein the equalizer is operable to use machine learning (ML) and/or AI for the equalization and mitigation of signal noise of the at least one signal in real-time as the electromagnetic environment changes; and

wherein the AI agent is operable to provide recommendations for the adjustment of the network parameters based on the at least one customer goal.

19. The system of claim 18 , wherein the equalizer reduces the signal interference and/or the distortion of at least one signal using a Kullback Leibler (KL) Divergence value.

20. The system of claim 18 , wherein the AI agent is operable to provide recommendations for optimization of L1, L2, and/or L3 network parameters in real time.