Optimization of SVD Precoding Complexity Based on Pseudo Skeleton Sampling

• 1、School of Information and Communication Engineering, Beijing University of Posts and Telecommnications, Beijing 100876

Abstract：Massive-MIMO technology has been a research hotspot in the communication field. Theoretically, the more antennas the receiver and transmitter are equipped with, the greater the degree of freedom the propagation channel can provide, and the better the performance in terms of data rate or link reliability. Communication stability and reliability. SVD (Singular Value Decomposition, SVD) decomposition plays an important role in the precoding process. When the channel matrix dimension is low, the traditional SVD decomposition algorithm has better results. However, when the dimension of the channel matrix of a Massive-MIMO system is high, problems such as high computational complexity and inability to meet real-time processing requirements may occur. Aiming at the above problems, this paper proposes a technique for optimizing the complexity of precoding SVD decomposition based on pseudo-skeleton sampling. The low-rank characteristics of the channel matrix brought by the spatial correlation of large-scale antenna arrays are used to reduce the complexity of SVD decomposition. Using pseudo-skeletal decomposition theory, an SVD decomposition method with approximately linear complexity is proposed, which greatly reduces the complexity of SVD decomposition in the precoding process.

Keywords： multivariate time series analysis complexity optimization pseudo skeleton sampling precoding