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Symbol Detection for Massive MIMO AF Relays Using Approximate Bayesian Inference
arXiv - CS - Information Theory Pub Date : 2020-03-26 , DOI: arxiv-2003.11760
Haochuan Zhang and Qiuyun Zou

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.

中文翻译:

使用近似贝叶斯推理的大规模 MIMO AF 中继的符号检测

对于大规模 MIMO AF 中继,当天线数量不够大时,符号检测成为一个实际问题,因为线性方法不是最优的,而最优方法是指数复杂的。本文提出了一种新的检测算法,该算法以每次迭代的复杂度为 $O(n^3)$ 为代价提供贝叶斯最优 MSE。该算法本质上是最近为深度学习开发的两种方法的混合,特别是对中继进行了优化。作为混合体,它继承了两者的状态演化公式,其中渐近 MSE 可以通过标量等效模型精确预测。当考虑单跳时,该算法也很容易退化为许多众所周知的结果。
更新日期:2020-03-27
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