Channel Reconstruction for SVD-ZF Precoding in Massive 3D-MIMO Systems Low-Complexity Algorithm

TL;DR

This paper introduces low-complexity channel reconstruction techniques for massive MIMO systems using randomized algorithms to approximate SVD, significantly reducing computational load while maintaining high data rates.

Contribution

It proposes novel randomized low-rank SVD approximation methods for downlink precoding in massive MIMO, reducing complexity without sacrificing performance.

Findings

Reduced computational cost by less than 30% compared to traditional methods

Achieved near 1Gbps data rate with 128 antennas

Effective adaptation to varying stream demands

Abstract

In this paper, we study the low-complexity channel reconstruction methods for downlink precoding in massive MIMO systems. When the user is allocated less streams than the number of its antennas, the BS or user usually utilizes the singular value decomposition (SVD) factorizations to get the effective channels, whose dimension is equal to the num of streams. This process is called channel reconstruction in BS for TDD mode. However, with the increasing of antennas in BS, the computation burden of SVD is becoming incredibly high. As a countermeasure, we propose a series of novel low-complexity channel reconstruction methods for downlink zero-forcing precoding (ZF). We adopt randomized algorithms to construct an approximate SVD, which could reduce the dimensions of the matrix, especially when approximating an input matrix with a low-rank element. Besides, this method could automatically modify the parameters to adapt arbitrary number demand of streams from users. The simulation results show that the proposed methods only cost less than 30% float computation than the traditional SVD-ZF method, while keeping nearly the same performance of 1Gbps with 128 BS antennas.