Wi-Fi Signal Classification via FFT Block Analysis
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Solution Overview
Problem
Existing Wi-Fi detection methods face challenges in accurately classifying Wi-Fi signals from Fourier Transform samples, leading to false detections due to interference from other devices, which affects channel utilization and duty cycle calculations.
Innovation Solution
The proposed solution involves classifying Wi-Fi signals by collecting and dividing Fourier Transform samples into frequency blocks, determining bandwidth, and using criteria such as slope values, sub-peak bins, adjacent channel analysis, and characteristic matching to differentiate between Wi-Fi and non-Wi-Fi signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple energy detection is used to identify Wi-Fi signals, then detection sensitivity is improved, but false detection rate increases due to inability to distinguish Wi-Fi frames from other narrowband interferers
Solution Approach 1:
The patent segments the detection process into multiple stages: initial energy detection followed by classification of detected signals as either Wi-Fi or non-Wi-Fi interferers. This segmentation allows the system to maintain high detection sensitivity while reducing false positives through subsequent classification steps.
Solution Approach 2:
The patent introduces an intermediary classification mechanism that acts as a mediator between energy detection and final signal identification. This classification step analyzes characteristics of detected signals to determine whether they are Wi-Fi frames or other narrowband interferers, thereby reducing false detections while maintaining detection sensitivity.
2Measurement precision
If sophisticated spectrum analyzers are used to accurately identify interference sources, then measurement precision is improved, but device cost and complexity increase
Solution Approach 1:
The patent enables Wi-Fi devices to perform their own interference classification using built-in spectrum data collection functionality. Instead of requiring external sophisticated spectrum analyzers, the system uses the radio's own capabilities to collect spectrum data and classify signals, thereby reducing device cost and complexity while maintaining measurement precision.
Solution Approach 2:
The patent creates a simplified copy of spectrum analyzer functionality within the Wi-Fi radio itself. By implementing signal classification algorithms in software, the system replicates the interference identification capabilities of sophisticated spectrum analyzers without requiring the expensive hardware, thus reducing device cost while maintaining measurement precision.
3Measurement precision
If radio decodes all received frames to identify Wi-Fi signals, then classification accuracy is improved, but processing time and energy consumption increase
Solution Approach 1:
The patent applies partial action by performing classification on a subset of signal characteristics rather than fully decoding all received frames. The system analyzes key features such as signal energy, spectral characteristics, and temporal patterns to classify signals as Wi-Fi or non-Wi-Fi, achieving sufficient classification accuracy without the time and energy cost of complete frame decoding.
Data Source
AI summary
The present disclosure discloses a system and method for. classifying Wi-Fi signals from Fourier transform samples. Generally, classifying Wi-Fi signals from Fourier transform samples includes: collecting and dividing Fourier transform samples into frequency blocks; determining the bandwidth for the Fourier transform sample; and determining whether the Fourier transform sample corresponds to a narrowband signal. Further, if a determination is made that the Fourier transform sample does not correspond to a narrowband signal, channel utilization is calculated based on a determination that the FFT sample corresponds to a Wi-Fi signal. If it is determined that the Fourier transform sample corresponds to a narrowband signal, then a determination is made that the FFT sample corresponds to a Wi-Fi signal based on certain criteria. The certain criteria may include one or more of a slope value, a number of sub-peak bins, an analysis of adjacent channels, characteristic matching, or other criteria.