System, method, and apparatus for providing optimized network resources
Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and/or user defined policies and rules to extract actionable information to help the customer optimize the network resources.
1. A method for optimization of spectrum utilization in an electromagnetic environment comprising:
monitoring the electromagnetic environment to create measured data with at least one monitoring sensor;
analyzing the measured data to create analyzed data including at least two wireless signals with at least one data analysis engine;
predicting the electromagnetic environment with an embedded artificial intelligence (AI) agent based on the analyzed data;
generating and selecting at least one parameter to store in a parameter block based on the measured data with the at least one data analysis engine;
dynamically optimizing network parameters based on the at least one parameter and/or at least one customer goal;
dynamically optimizing Layer 1 (L1), Layer 2 (L2), and Layer 3 (L3) functions and/or parameters based on statistical learning techniques and/or control theory;
identifying and mitigating interference between the at least two wireless signals transmitted on overlapping frequency bands with at least one interference mitigation engine based on a communication from the embedded AI agent; and
managing simultaneous operation of at least two wireless networks containing the at least two wireless signals with at least one multi-network orchestration module.
2. The method of claim 1 , wherein the embedded AI agent utilizes at least one AI algorithm and/or machine learning (ML) algorithm to refine a training model and adjust the at least one parameter.
3. The method of claim 2 , wherein the embedded AI agent learns from previous spectrum allocation decisions.
4. The method of claim 1 , further comprising at least one smart contract engine managing dynamic spectrum leasing between at least two telecommunication operators.
5. The method of claim 4 , wherein the at least two telecommunication operators share spectrum resources.