Joint Estimation and Detection for MIMOSTBC System Based on Deep Neural Network Motivated by the recent advances in deep neural network (DNN), we propose a general DNN scheme to joint channel estimation and signal detection for multiinput multioutput (MIMO) system with spacetime block coding (STBC). Different from the maximum likelihood (ML) detection with imperfect channel state information (CSI) which consists of channel estimator, combiner and detector, the proposed DNN scheme is designed to replace the three modules jointly, estimate CSI implicitly and recover the transmitted symbols directly. Specifically, a fourlayer DNN is constructed for MIMOAlamouti system and is simulated under the condition of quasistatic rayleigh channel and QPSK modulation. The simulation results demonstrate that, at the same bit error ratio (BER), the signaltonoise ratio (SNR) of the DNNbased detection has a loss of 1dB compared with ML detection with perfect CSI, and has a gain of 3dB compared with ML detection with imperfect CSI in MIMOAlamouti system.

Deep Learning Aided Signal Detection in OFDM Systems with TimeVarying Channels * In this paper, we propose a deep learning aided approach for signal detection in orthogonal frequencydivision multiplexing (OFDM) systems with timevarying channels. The method simplifies the architecture of OFDM systems by treating OFDM receivers as a black box. We utilize fullyconnected deep neural network (FCDNN) properly and successfully simulate an endtoend timevarying OFDM system. Compared with two conventional algorithms welldesigned to deal with OFDM systems in timevarying environment, the proposed method does not need to estimate channel parameters to detect signals. Simulation results also demonstrate that the trained DNN model has the ability to remember the characteristics of wireless timevarying channels and provide more accurate and robust signal recovery performance.