Electromagnetic Environment Learning for Dynamic Spectrum Allocation
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Solution Overview
Problem
Effective spectrum management is hindered by the diverse nature of wireless signal propagations across different frequencies and technological standards, and the growing demand for spectrum exceeds the finite available resources, necessitating efficient utilization.
Innovation Solution
A system for dynamic spectrum utilization management in private wireless networks, incorporating monitoring sensors, data analysis engines, and learning engines to detect signals of interest, learn the electromagnetic environment, and create actionable data for optimizing network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spectrum management is implemented across diverse wireless signal propagations and technological standards, then spectrum utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments spectrum management by creating separate functional modules: monitoring sensors detect signals independently, data analysis engines process different signal types separately, and learning engines handle specific technological standards individually. This modular segmentation allows efficient management of diverse wireless signals while reducing overall system complexity through divided responsibilities.
Solution Approach 2:
The patent introduces intermediary components including data analysis engines that act as mediators between monitoring sensors and learning engines, and orchestration mechanisms that coordinate between different technological standards. These intermediaries simplify the interaction complexity while maintaining efficient spectrum utilization across diverse standards.
2Productivity
If monitoring and analysis of electromagnetic environment is performed to optimize network resources, then resource optimization is improved, but measurement and detection difficulty increases
Solution Approach 1:
The system performs preliminary actions by deploying monitoring sensors to continuously detect and record electromagnetic signals before optimization is needed. Data analysis engines pre-process signals to extract relevant features, and learning engines pre-learn patterns from historical data. This preliminary detection and analysis reduces the complexity of real-time measurement and enables faster resource optimization decisions.
Solution Approach 2:
The patent implements feedback loops where monitoring sensors continuously measure the electromagnetic environment, data analysis engines analyze the measurements, and learning engines use the analyzed data to generate optimization recommendations that are fed back to reconfigure network resources. This closed-loop feedback system improves resource optimization while managing detection difficulty through iterative learning.
3Productivity
If dynamic spectrum allocation is implemented to meet growing demand, then spectrum resource efficiency is improved, but device complexity increases
Solution Approach 1:
The system implements dynamic spectrum allocation where monitoring sensors continuously track spectrum usage, data analysis engines dynamically adjust analysis parameters based on current conditions, and learning engines adapt their models in real-time to changing demand patterns. This dynamic approach maximizes spectrum resource efficiency while the modular architecture manages the inherent complexity through adaptive, rather than static, management mechanisms.
Data Source
AI summary
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.