Randomized Iterative Methods for Low-Complexity Large-Scale MIMO Detection
Zheng Wang, Robert M. Gower, Yili Xia, Lanxin He, Yongming Huang
Abstract
In this paper, we introduce a randomized iterative method for signal detection in uplink large-scale multiple-input multiple-output (MIMO) systems, which not only achieves a low computational complexity but also enjoys a global and exponentially fast convergence. First of all, by adopting the random sampling into the iterations, the randomized iterative detection algorithm (RIDA) is proposed for large-scale MIMO systems. We show that RIDA converges exponentially fast in terms of mean squared error (MSE). Furthermore, this global convergence <i>always</i> holds, and does not depend on the standard requirements such as <inline-formula><tex-math notation="LaTeX"><?TeX $N\gg K$?></tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX"><?TeX $N$?></tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX"><?TeX $K$?></tex-math></inline-formula> denote the numbers of antennas at the sides of base station and users. This broadly extends the applications of low-complexity detection in uplink large-scale MIMO systems. Then, based on a new conditional sampling, optimization and enhancements are given to further improve both the convergence and efficiency of RIDA, resulting in the modified randomized iterative detection algorithm (MRIDA). Meanwhile, with respect to MRIDA, further complexity reduction by exploiting the matrix structure is given while its implementation by deep neural networks (DNN) is also presented for a better detection performance.