Massive MIMO Belief Propagation Detection Using DIP with DNN-Trained Scaling Factor

• Published in: IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC pp. 1 - 6
• Main Authors: Tachibana, Junta, Bouazizi, Mondher, Ohtsuki, Tomoaki
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.04.2024
• Subjects: Degradation; Detection algorithms; Interference; Massive MIMO; Noise; Reliability; Training
• ISSN: 1558-2612
• DOI: 10.1109/WCNC57260.2024.10570942

Belief propagation (BP) detection is a technique for separating and detecting incoming signals with minimal complexity in massive multiple-input multiple-output (MIMO) systems. However, because of the interference and noise that remain in the received signals even after attempting to remove them through conventional techniques, errors manifest in the transmitted messages. Due to the MIMO channel's numerous loops, a message containing mistakes spreads across the factor graph leading to a degradation in the BP's convergence properties and detection performance. In this paper, we propose a BP detection using deep image prior (DIP) with deep neural network (DNN)-trained scaling factor. By applying DIP to the BP detection algorithm, we achieve a reduction in residual interference and noise. Post DIP application, there is a modification in the variance of both interference and noise components. To align it more accurately with its true value and enhance message reliability, we adjust the variance using scaling factors trained through DNN-based damped BP (DNN-dBP). Using computer simulations, we demonstrate that applying DIP helps decrease the power of the residual interference and noise after removing the interference at each iteration in the BP detection. It is also shown that the proposed method improves the detection performance compared to the normal BP detection, BP detection without DIP when training the scaling factors of the variance, and BP detection to which DIP is applied without training the scaling factors of the variance.