GMD-Based Hybrid Precoding For Millimeter-Wave Massive MIMO Systems

TL;DR

This paper introduces a GMD-based hybrid precoding method for mmWave massive MIMO systems that simplifies design and improves performance over traditional SVD-based schemes, reducing complexity and enhancing BER.

Contribution

It proposes a novel GMD-based hybrid precoding scheme that avoids complex bit allocation and improves BER performance in mmWave massive MIMO systems.

Findings

Outperforms SVD-based hybrid precoding in simulations

Achieves better BER with lower complexity

Effectively avoids complicated bit allocation

Abstract

Hybrid precoding can significantly reduce the number of required radio frequency (RF) chains and relieve the huge energy consumption in mmWave massive MIMO systems, thus attracting much interests from academic and industry. However, most existing hybrid precoding schemes are based on singular value decomposition (SVD). Due to the very different sub-channel signal-to-noise ratios (SNRs) after SVD, complicated bit allocations is usually required to match the sub-channel SNRs. To solve this problem, we propose a geometric mean decomposition (GMD)-based hybrid precoding scheme to avoid the complicated bit allocation. Its basic idea is to seek a pair of analog and digital precoding matrices that are sufficiently close to the optimal unconstrained GMD precoding matrix. Specifically, we design the analog (digital) precoding matrix while keeping the digital (analog) precoding matrix fixed. Further, the principle of basis pursuit is utilized in the design of analog precoding matrix, while we obtain the digital precoding matrix by projecting the GMD operation on the digital precoding matrix. Simulation results verify that the proposed GMD-based hybird precoding scheme outperforms conventional SVD-based hybrid precoding schemes and achieves much better bit error rate (BER) performance with low complexity.