Wideband Spectrum Signal Classification for Automated RF Management
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Solution Overview
Problem
Existing spectrum management devices are limited to specific technologies, are bulky and expensive, or require complex processes, making them difficult to use and manage, and lack automated identification and reporting capabilities for efficient spectrum utilization.
Innovation Solution
A spectrum management device that identifies, classifies, and catalogs signals based on radio frequency measurements, providing real-time data analysis and reporting, with features like signal identification, geo-location, and database integration for efficient spectrum management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If narrowly tailored spectral analyzers are used for specific communication standards, then measurement precision for that specific standard is improved, but device complexity increases and adaptability decreases when standards change
Solution Approach 1:
The patent implements a universal spectral analyzer that can detect and analyze multiple communication standards (cellular, Wi-Fi, radar, TV, etc.) within a single device. The system uses a wideband receiver capable of tuning across diverse frequency bands and employs a signal processor with multiple algorithms that can identify and analyze different modulation types and communication protocols, eliminating the need for separate specialized analyzers for each standard.
Solution Approach 2:
The system dynamically adapts its analysis parameters and processing algorithms based on the detected signal type. When a different communication standard or signal pattern is detected, the processor automatically adjusts its detection parameters, threshold values, and analysis methods to optimize performance for that specific standard, allowing the device to maintain high measurement precision across varying conditions without manual reconfiguration.
2Productivity
If comprehensive spectral management is implemented across all frequency bands, then spectrum utilization efficiency is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the spectral management function into modular components: a wideband receiver module that can be configured for different frequency bands, a signal processor module with specialized algorithms for different signal types, and a database module for storing reference signals. This modular architecture allows the system to manage comprehensive spectrum efficiently while keeping individual components relatively simple and reusable across different applications.
Solution Approach 2:
The system incorporates automated signal identification and classification capabilities where the processor automatically detects the type of signal present, retrieves appropriate reference data from the database, and performs analysis without requiring manual intervention. The database stores reference signals and characteristics for various communication standards, enabling the system to self-configure and self-optimize its analysis parameters based on the detected signal type, reducing the need for complex manual setup and operation.
3Productivity
If automated signal identification and classification is implemented, then productivity is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-storing reference signals and their characteristics in a database before actual analysis occurs. When a signal is detected, the processor compares the detected signal features against the pre-stored references to rapidly identify signal type and classify it appropriately. This preliminary preparation enables fast automated identification while maintaining high accuracy, as the system leverages pre-validated reference data rather than performing complex real-time analysis from scratch.
Data Source
AI summary
Systems, methods, and devices enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements. Signal data is compared with stored data to identify the signal of interest. Signal degradation data is calculated based on noise figure parameters, hardware parameters and environment parameters.