RF Signal Detection for Dynamic Spectrum Sharing Settlements
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Solution Overview
Problem
Current spectrum management devices are limited by their specificity to certain technologies, bulkiness, high cost, difficulty in use, and lack of real-time data analysis, making them inefficient for managing diverse wireless communications spectrum needs.
Innovation Solution
A system utilizing a receiver, processor, and blockchain platform for real-time dynamic spectrum allocation and management, employing statistical learning and machine learning to identify and classify RF signals, including low-power and buried signals, and enabling dynamic spectrum sharing through smart contracts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If narrowly tailored spectral analyzer devices are used for specific communication standards, then measurement precision for that specific standard is improved, but adaptability to other technologies and spectrum uses deteriorates
Solution Approach 1:
The patent implements a universal spectrum management device that can handle multiple communication standards and spectrum uses through a single integrated platform. The system uses a database of spectrum usage patterns and machine learning algorithms to adapt to different technologies without requiring separate specialized devices for each standard.
2Adaptability or versatility
If comprehensive spectrum management devices are used to manage diverse spectrum needs, then adaptability is improved, but device complexity and bulkiness increase
Solution Approach 1:
The patent creates a virtualized spectrum management system that uses software-based simulation and modeling to replicate spectrum behavior. Instead of using physically complex hardware for each function, the system uses computational models to represent and manage different spectrum scenarios, reducing physical complexity while maintaining comprehensive functionality.
3Ease of manufacture
If traditional spectrum management devices are used, then initial cost may be lower, but loss of time in achieving effective spectrum management increases due to manual processes
Solution Approach 1:
The patent implements automated spectrum management where the system performs self-learning and self-adjustment. The machine learning algorithms automatically analyze spectrum usage patterns, identify optimization opportunities, and execute management decisions without requiring manual intervention, thereby reducing the time needed to achieve effective spectrum management while maintaining ease of deployment.
Data Source
AI summary
Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.