Deep Learning Based Nonlinear Signal Detection in Millimeter-Wave Communications

Abstract

For millimeter-wave (mm-Wave) communications, signal detection in the presence of the power amplifier (PA) nonlinearity and unknown multipath channel has remained one challenging task in single-input single-output (SISO) communication system. Besides, the PA nonlinearity in multiple-input multiple-output (MIMO) communication system also has severe effects upon the signal detection in receiver-end.In this paper, firstly, we suggest a deep-learning (DL) framework, i.e. integrating feedforward neural network (FNN) and recurrent neural network (RNN), to combat both the nonlinear distortion and linear inter-symbol-interference (ISI) from a global point of view, thereby accomplishing nonlinear equalization and signal detection at the receiver-end in SISO communication system.Utilizing the powerful mapping and learning capability of DL, our new method is able to detect symbols via the received signals corrupted by both nonlinear distortion and linear ISI, avoiding both the explicit nonlinear pre-distorter in transmitter and the channel state information (CSI) estimator.Secondly, our DL-based framework can also successfully cope with the joint nonlinear distortion and space-time decoding problem in MIMO communication system, without explicitly pre-calibrating nonlinear distortion and estimating CSI.Numerical experiments demonstrate our DL-based detector is more effective in alleviating the performance degradation both from the coupled nonlinear distortion and linear ISI in SISO communication system, and the coupled nonlinear distortion and linear space-time decoding in MIMO communication system.Compared with the state-of-the-art methods, e.g. pre-distorter and post-equalizer, our DL-based scheme effectively improves the detection performance.