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(n3) 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.
@article{arxiv.2003.11760,
title = {Symbol Detection for Massive MIMO AF Relays Using Approximate Bayesian Inference},
author = {Haochuan Zhang and Qiuyun Zou},
journal= {arXiv preprint arXiv:2003.11760},
year = {2020}
}