论文标题
使用近似贝叶斯推理的大规模MIMO AF继电器检测符号检测
Symbol Detection for Massive MIMO AF Relays Using Approximate Bayesian Inference
论文作者
论文摘要
对于大量的MIMO AF继电器，当天线数量不够大时，符号检测成为一个实际问题，因为线性方法是非最佳的，最佳方法是指数化的。本文提出了一种新的检测算法，该算法以$ O（n^3）$复杂性为$ O（n^3）$复杂性提供贝叶斯最佳的MSE。该算法本质上是最近开发的两种用于深度学习的方法的混合体，并特别优化了继电器。作为一种混合动力，它从两个状态进化的配方中继承，在该配方中，可以通过标量等效模型精确预测渐近MSE。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。
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.
