Low-Complexity Channel Reconstruction Methods Based on SVD-ZF Precoding in Massive 3D-MIMO Systems

TL;DR

This paper introduces low-complexity channel reconstruction methods for massive 3D-MIMO systems that significantly reduce computational load while maintaining high data rates, by dividing large SVD computations into smaller ones based on antenna correlations.

Contribution

The paper proposes novel low-complexity channel reconstruction techniques that split large SVDs into smaller ones considering antenna correlations, reducing computation by over 90% without performance loss.

Findings

Less than 10% floating-point computation compared to traditional methods.

Maintains nearly the same data rate of 1Gbps.

Effective for massive 3D-MIMO systems with high antenna counts.

Abstract

In this paper, we study the low-complexity channel reconstruction methods for downlink precoding in massive multiple-Input multiple-Output (MIMO) systems. When the user is allocated less streams than the number of its antennas, the base station (BS) or user usually utilizes the singular value decomposition (SVD) to get the effective channels, whose dimension is equal to the number of streams. This process is called channel reconstruction and done in BS for time division duplex (TDD) mode. However, with the increasing of antennas in BS, the computation burden of SVD is getting incredible. Here, we propose a series of novel low-complexity channel reconstruction methods for downlink precoding in 3D spatial channel model. We consider different correlations between elevation and azimuth antennas, and divide the large dimensional matrix SVD into two kinds of small-size matrix SVD. The simulation results show that the proposed methods only produce less than 10% float computation than the traditional SVD zero-forcing (SVD-ZF) precoding method, while keeping nearly the same performance of 1Gbps.