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.

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• An Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models
• Multilevel MIMO Detection with Deep Learning
• A Neural Network Aided Approach for LDPC Coded DCO-OFDM with Clipping Distortion
• Deep Learning for Wireless Communications
• Learning to Search for MIMO Detection