Recurrent Neural Detection of Time–Frequency Overlapped Interference Signals

Abstract

For interfering signals overlap with normal signals in both time and frequency domain, it is difficult to detect them. Therefore, this paper proposes a novel bidirectional recurrent neural network-based interference detection method. By utilizing the ability of recurrent neural network of extracting the nonlinear features of the time series context, the model can get a prediction of following signal samples and calculate the difference between prediction signal and original signal to do interference detection. The proposed method can achieve a better sensitivity and determine the exact location of the complete interfering signal. In the experiment part, we demonstrate the efficacy of this method in multiple typical scenarios of time–frequency overlapped wireless signals.