Title: Improving Massive MIMO Belief Propagation Detector with Deep Neural Network,
Abstract: In this paper, deep neural network (DNN) is utilized to improve the belief
propagation (BP) detection for massive multiple-input multiple-output (MIMO)
systems. A neural network architecture suitable for detection task is firstly
introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed
based on two modified BP detectors, damped BP and max-sum BP. The correction
factors in these algorithms are optimized through deep learning techniques,
aiming at improved detection performance. Numerical results are presented to
demonstrate the performance of the DNN detectors in comparison with various BP
modifications. The neural network is trained once and can be used for multiple
online detections. The results show that, compared to other state-of-the-art
detectors, the DNN detectors can achieve lower bit error rate (BER) with
improved robustness against various antenna configurations and channel
conditions at the same level of complexity.