Robust Modulation Classification under Uncertain Noise Condition Using Recurrent Neural Network

Modulation classification using deep neural networks has recently received increasing attention due to its capability in learning rich features of data. In this paper, we propose a low- complexity blind data-driven modulation classifier. Our classifier operates robustly over Rayleigh fading channels under uncertain noise conditions modeled using a mixture of three types of noise, namely, white Gaussian noise, white non- Gaussian noise and correlated non-Gaussian noise. The proposed classifier consists of several layers of recurrent neural networks (RNN) which is well-suited for learning representations from time-correlated data. The classifier is trained using the labeled raw signal samples generated under different noise conditions. Simulation results show that the performance of our proposed classifier approaches that of maximum likelihood classifiers with perfect channel knowledge and outperforms existing expectation maximum (EM) and expectation conditional maximum (ECM) classifiers which iteratively estimate channel and noise parameters.