Interference Detection and Recognition Based on Signal Reconstruction Using Recurrent Neural Network

Interference detection using deep neural network has recently received increasing attention due to its capability in learning rich features of data. In this paper, we proposed a low-complexity blind interference detection method. Our method operates on Time-frequency overlapped interference signal and can separate which from received signal. The proposed method uses AutoEncoder network to reconstruct the transmitted signal and separate interference signal. The AutoEncoder network consists of several layers of recurrent neural network (RNN) which is well-suited for learning representations from time-correlated data. Simulation results show that the separated interference signal has the same features of original interference, so it not only achieves good interference detection performance, but also can realize interference recognition.

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