Dynamic Spectrum Management for Signal Detection and Geolocation
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Solution Overview
Problem
Existing spectrum management systems face challenges in efficiently managing the finite electromagnetic spectrum due to varying device frequencies, technological standards, and growing demand, leading to difficulties in optimizing spectrum utilization and interference management.
Innovation Solution
A system for dynamic, prioritized spectrum management that includes monitoring sensors, data analysis engines, and a tip and cue server to automatically detect, learn, and geolocate signals of interest, providing actionable data for optimized spectrum utilization and minimizing interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spectrum management methods are used to regulate radio frequencies, then spectrum usage can be controlled, but effective spectrum management becomes difficult to obtain and can only be reached over a long period of time due to varying device frequencies and technological standards
Solution Approach 1:
The patent implements dynamic spectrum management through machine learning models that continuously learn and adapt to changing electromagnetic environments. The system transitions from static, pre-configured spectrum allocation to dynamic, real-time spectrum utilization decisions based on learned patterns and current conditions, enabling rapid adaptation without lengthy manual reconfiguration processes
Solution Approach 2:
The system employs autonomous machine learning models that automatically detect signals, classify devices, and manage spectrum allocation without human intervention. The learned models self-adjust to new devices and frequency patterns, eliminating the need for manual spectrum management and enabling immediate effectiveness rather than long-term gradual improvement
2Adaptability or versatility
If spectrum resources are allocated to meet growing wireless technology demand, then more services can be supported, but available spectrum becomes a valuable finite resource that requires efficient utilization
Solution Approach 1:
The system dynamically changes spectrum allocation parameters based on learned environmental patterns and current service demands. Machine learning models optimize frequency selection, bandwidth allocation, and power distribution in real-time, allowing the same finite spectrum to serve multiple diverse wireless technologies efficiently through adaptive parameter adjustment rather than requiring additional spectrum resources
Solution Approach 2:
The patent creates a universal spectrum management system that handles multiple wireless technologies and services through a single learned model framework. The system identifies and manages various device types (IoT, 5G, wireless microphones, etc.) and frequency standards uniformly, enabling one system to efficiently allocate spectrum across diverse applications without dedicated management for each service
3Adaptability or versatility
If multiple devices transmit wireless signals at different frequencies and standards, then service diversity is achieved, but interference management and spectrum optimization become difficult
Solution Approach 1:
The patent introduces machine learning models as intermediary layers between diverse wireless devices and the spectrum management function. These learned models act as intelligent mediators that automatically interpret signals from various devices, classify them by type and standard, and coordinate spectrum allocation to prevent interference, simplifying the management of device diversity without requiring complex manual coordination rules
Data Source
AI summary
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.