We propose a new iterative detection and decoding (IDD) algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double -EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE) based IDD, as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreases.

中文翻译：

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低复杂度基于双EP的检测器，用于MIMO中的迭代检测和解码

我们提出了一种基于期望传播（EP）的多输入多输出（MIMO）的新的迭代检测和解码（IDD）算法，并将其应用于大规模MIMO场景。提出了两个主要结果。我们首先引入EP来迭代地改进MIMO检测器对后验估计和信道解码器的软输出的高斯近似。用这种新颖的方法，用双倍的 -EP（DEP），收敛性大大提高，而计算复杂度仅为基于线性最小均方误差（LMMSE）的IDD的两倍，如随附的实验所示。此外，如在LMMSE MIMO检测器中一样，当天线数量增加时，DEP所需的矩阵求逆运算的计算成本变得难以承受。在这项工作中，我们还开发了DEP方法，其中后验的均值和协方差矩阵分别通过使用Gauss-Seidel和Neumann级数方法进行近似。作为低复杂度的LMMSE技术，这种低复杂度的DEP检测器在天线数量上具有二次复杂度。