System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.
1. A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one sensor operable to monitor the electromagnetic environment, thereby creating measured data; at least one data analysis engine for analyzing the measured data to create analyzed data; and at least one survey occupancy application; wherein the at least one sensor includes an antenna array formed of multiple antennas resonating at different frequency bands and configured in a 1D (linear), 2D (planar), or 3D antenna configuration; wherein the system is operable to use machine learning (ML) to create prediction models to autonomously create forecasts for future spectrum usage based on historical data or the analyzed data; and wherein the at least one survey occupancy application is operable to use a priority assigned to at least one customer application to dynamically allocate at least one frequency band in the electromagnetic spectrum; wherein the prediction models are used for the allocation of the at least one frequency band in the electromagnetic spectrum.
2. The system of claim 1 , wherein the at least one sensor includes at least one radio server and/or at least one software defined radio.
3. The system of claim 1 , wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
4. The system of claim 1 , further comprising a certification and compliance application, wherein the certification and compliance application is operable to determine if the at least one customer application and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
5. The system of claim 1 , wherein the at least one survey occupancy application is operable to schedule occupancy in a frequency band.
6. The system of claim 1 , further comprising a learning engine configured to learn the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
7. The system of claim 1 , wherein the at least one survey occupancy application is operable to determine occupancy in the at least one frequency band.
8. A system for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: at least one sensor operable to monitor the electromagnetic environment, thereby creating measured data; at least one data analysis engine for analyzing the measured data to create analyzed data; at least one server; and at least one survey occupancy application; wherein the at least one data analysis engine is operable to automatically detect at least one signal and learn the electromagnetic environment based on the analyzed data; wherein the at least one survey occupancy application is operable to determine occupancy in at least one frequency band; wherein the at least one survey occupancy application is operable to schedule occupancy in at least one frequency band; and wherein the system is operable to use machine learning (ML) to create prediction models to autonomously create forecasts for future spectrum usage based on historical data and the analyzed data; wherein the at least one survey occupancy application is operable to use a priority assigned to at least one customer application to dynamically allocate the at least one frequency band in the electromagnetic spectrum.
9. The system of claim 8 , further comprising a learning engine configured to learn the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
10. The system of claim 8 , wherein the system further includes a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
11. The system of claim 8 , wherein the at least one survey occupancy application is operable to preprocess at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
12. A method for dynamic, prioritized spectrum utilization management in an electromagnetic environment comprising: monitoring the electromagnetic environment using at least one sensor, thereby creating measured data; determining occupancy in at least one frequency band using a survey occupancy application; scheduling occupancy in the at least one frequency band using the at least one survey occupancy application; creating prediction models to autonomously create forecasts for future spectrum usage based on historical data or the measured data; assigning a priority to at least one customer application; and dynamically allocating the at least one frequency band in the electromagnetic spectrum based on the priority; wherein the prediction models are used for the allocation of the at least one frequency band in the electromagnetic spectrum.
13. The method of claim 12 , wherein the at least one survey occupancy application preprocesses at least two signals that exist in the at least one frequency band based on interference between the at least two signals.
14. The method of claim 12 , further comprising a certification and compliance application determining if at least one customer application and/or at least one customer device is behaving according to at least one rule and/or at least one policy.
15. The method of claim 12 , wherein the at least one sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
16. The method of claim 12 , further comprising learning the electromagnetic environment using machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.
17. The method of claim 12 , further comprising determining an impact of interference on customer goals and/or customer operations and allocating the at least one frequency band in the electromagnetic spectrum based on the determined impact of the interference.