Symbol Detection for Massive MIMO AF Relays Using Approximate Bayesian Inference

Abstract

For massive MIMO AF relays, symbol detection becomes a practical issue when the number of antennas is not large enough, since linear methods are non-optimal and optimal methods are exponentially complex. This paper proposes a new detection algorithm that offers Bayesian-optimal MSE at the cost of $O(n^3)$ complexity per iteration. The algorithm is in essence a hybrid of two methods recently developed for deep learning, with particular optimization for relay. As a hybrid, it inherits from the two a state evolution formulism, where the asymptotic MSE can be precisely predicted through a scalar equivalent model. The algorithm also degenerates easily to many results well-known when single-hop considered.

References in corpus (7)

• Massive MIMO for Next Generation Wireless Systems
• Message Passing Algorithms for Compressed Sensing
• Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs
• Bilinear Generalized Approximate Message Passing
• Bayesian Optimal Data Detector for Hybrid mmWave MIMO-OFDM Systems with Low-Resolution ADCs
• Generalized Approximate Message Passing for Estimation with Random Linear Mixing
• Inference in Deep Networks in High Dimensions