论文标题
关于大量MIMO检测的差异贝叶斯观点
A Variational Bayesian Perspective on Massive MIMO Detection
论文作者
论文摘要
大量多输入多输出（MIMO）系统中的最佳数据检测需要过度的计算复杂性。文献中已经提出了多种检测算法，在复杂性和检测性能之间提供了不同的权衡。在本文中，我们建立在差异贝叶斯（VB）推理的基础上，以设计大型MIMO系统的低复杂性多源检测算法。我们首先在接收器（CSIR）上使用完美的通道状态信息检查了大规模的MIMO检测问题，并表明具有已知噪声方差的常规VB方法会产生较差的检测性能。为了解决这一限制，我们设计了两种新的VB算法，这些算法使用算法本身假定的噪声方差和协方差矩阵。我们进一步开发了使用不完美CSIR进行大规模MIMO检测的VB框架。仿真结果表明，与广泛的通道模型相比，所提出的VB方法的检测误差明显较低。
Optimal data detection in massive multiple-input multiple-output (MIMO) systems requires prohibitive computational complexity. A variety of detection algorithms have been proposed in the literature, offering different trade-offs between complexity and detection performance. In this paper, we build upon variational Bayes (VB) inference to design low-complexity multiuser detection algorithms for massive MIMO systems. We first examine the massive MIMO detection problem with perfect channel state information at the receiver (CSIR) and show that a conventional VB method with known noise variance yields poor detection performance. To address this limitation, we devise two new VB algorithms that use the noise variance and covariance matrix postulated by the algorithms themselves. We further develop the VB framework for massive MIMO detection with imperfect CSIR. Simulation results show that the proposed VB methods achieve significantly lower detection errors compared with existing schemes for a wide range of channel models.
