A Deep Learning Framework for Blind Time-Frequency Localization in Wideband Systems.
VTC Spring(2020)
Abstract
In this paper, we propose a blind timefrequency localization method for wireless signals present in a wideband radio frequency (RF) spectrum. The signal detection problem is transformed into an object detection problem by converting the RF time-series captures into spectrogram images. A deep learning system based on the Faster RCNN [2] is then configured to suit the signal detection task. Guidelines are provided to make design choices in terms of both data pre-processing and the FRCNN modeling, for example, on the Short Time Fourier Transform (STFT) parameters, the spectrogram sizes, and the anchor sizes. Experiments with artificially generated WiFi high throughput data [3] reveal that (i) the proposed framework can achieve up to a mean average precision (mAP) of 0.9 for captures with positive signalto-noise ratio (SNR), (ii) the proposed framework is fairly robust to the number and size of the anchors, and (iii) the proposed framework is sensitive to the disparity in the signal sizes, giving us few insights into possible future extensions.
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Key words
positive signal-to-noise ratio,wireless signals,blind timefrequency localization method,wideband systems,blind Time-frequency localization,mean average precision,artificially generated WiFi high,Short Time Fourier Transform parameters,FRCNN modeling,data pre-processing,signal detection task,Faster RCNN,deep learning system,spectrogram images,RF time-series captures,object detection problem,signal detection problem,wideband radio frequency spectrum
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