RF Spectrum Sensing With Knowledge Maps for Dynamic Sharing
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Solution Overview
Problem
Current spectrum management devices are limited by their specificity to certain technologies, are bulky and difficult to use, require external connectivity, and lack real-time data analysis, making them inefficient for managing diverse wireless communications spectrum.
Innovation Solution
A system utilizing a blockchain platform with a receiver, processor, and memory for real-time RF spectrum allocation, capable of learning the RF environment, forming a knowledge map, and executing smart contracts for dynamic spectrum sharing based on statistical learning and machine learning algorithms.
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 deteriorates
Solution Approach 1:
The patent applies universality by creating a single spectral analyzer device that can measure multiple communication standards (cellular, Wi-Fi, radar, TV, etc.) through a unified signal processing architecture. The device uses a general-purpose receiver combined with adaptive classification algorithms that automatically identify and process different signal types, eliminating the need for multiple specialized devices while maintaining measurement precision across diverse standards.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting processing parameters based on detected signal characteristics. The system changes its operational parameters (such as frequency resolution, detection thresholds, and processing algorithms) according to the identified communication standard, allowing a single device to adapt its measurement precision to match the requirements of different technologies without physical reconfiguration.
2Adaptability or versatility
If comprehensive spectrum management devices are used to cover all technologies, then adaptability is improved, but device complexity and bulk increase
Solution Approach 1:
The patent resolves device complexity by implementing a universal receiver architecture that handles multiple technologies through software-based classification rather than hardware complexity. The single receiver is paired with an intelligent classification system that automatically identifies signal types and routes processing accordingly, achieving comprehensive coverage without proportionally increasing hardware complexity.
Solution Approach 2:
The patent uses copying by creating a simplified virtual representation of signal characteristics through classification algorithms. Instead of requiring complex hardware for each technology type, the system creates digital models and classifications of different signal types, allowing the same physical hardware to handle multiple technologies through software-based virtualization.
3Productivity
If traditional spectrum management devices are used, then basic spectral analysis is possible, but real-time data analysis and automation capability are insufficient
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors spectrum usage patterns, classifies signals in real-time, and provides automated recommendations or actions based on detected conditions. The feedback loop enables the system to adapt its behavior dynamically, providing real-time spectrum management capabilities that are both monitored and adjusted based on actual spectrum conditions and management objectives.
Solution Approach 2:
The patent applies self-service by enabling the spectrum management system to automatically detect, classify, and manage spectrum resources without requiring constant human intervention. The automated classification and decision-making capabilities allow the system to self-regulate spectrum usage patterns, providing real-time management with minimal human involvement while maintaining productivity.
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.