RF Spectrum Signal Detection Using Learned Power-Frequency Maps
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Solution Overview
Problem
Existing 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
An apparatus that learns the RF environment using statistical learning techniques, forms a knowledge map, and performs real-time spectral sweeps to detect low-power or buried signals, providing near-real-time identification and classification of signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If narrowly tailored spectral analyzers are used for specific communications standards, then measurement precision for that specific standard is improved, but adaptability to other technologies deteriorates
Solution Approach 1:
The patent implements a universal spectral analyzer that can detect and analyze multiple communications standards (cellular, Wi-Fi, radar, TV, etc.) within a single device. The system uses a wideband receiver capable of scanning across diverse frequency bands and employs adaptive signal processing algorithms that automatically identify and classify different signal types, eliminating the need for multiple specialized devices while maintaining measurement precision for each standard.
Solution Approach 2:
The system dynamically adjusts its operating parameters including frequency range, bandwidth, resolution, and detection thresholds based on the detected signal type and environmental conditions. The receiver can reconfigure its sampling rate, filtering characteristics, and analysis parameters in real-time to optimize performance across different communications standards without physical reconfiguration.
2Productivity
If comprehensive spectrum management is implemented, then spectrum utilization efficiency is improved, but device complexity increases
Solution Approach 1:
The system automatically performs spectrum sensing, signal classification, and availability assessment without requiring manual intervention. The processor continuously monitors the RF environment, identifies signal characteristics, determines spectrum holes, and generates recommendations for optimal channel selection and power levels, enabling comprehensive spectrum management through automated self-service operations.
Solution Approach 2:
The system implements continuous feedback loops where detected signal information is processed to update the spectrum availability map, which then feeds back into channel selection decisions and power level adjustments. This closed-loop feedback mechanism enables dynamic adaptation to changing spectral conditions while maintaining efficient spectrum utilization.
3Speed
If real-time spectrum analysis is performed, then response time is improved, but processing energy consumption increases
Solution Approach 1:
The system performs partial spectrum analysis by focusing computational resources on identifying and classifying only the most relevant signal types and characteristics rather than analyzing every possible parameter for every detected signal. The processor prioritizes analysis based on the detected signal strength, frequency band, and potential interference level, reducing overall processing energy consumption while maintaining adequate response time for spectrum management decisions.
Data Source
AI summary
Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.