Low-Complexity Subspace MMSE Channel Estimation in Massive MU-MIMO System

Yunfeng Deng,Tomoaki Ohtsuki

doi:10.1109/access.2020.3006242

Abstract

Massive multi-user multiple-input multiple-output (massive MU-MIMO) technology is considered as a promising enabler to fulfill the rapid growth of traffic requirement for wireless mobile communications. The massive MU-MIMO system can achieve unlimited capacity when the base station (BS) has accurate channel state information (CSI). In time-division-duplex (TDD) mode, the BS estimates CSI by receiving pilot signals sent from user terminals (UEs). However, because of using non-orthogonal pilots, pilot contamination happens to degrade the quality of the CSI estimation. To deal with pilot contamination problem, a low-complexity subspace minimum mean square error (MMSE) estimation method is proposed in this paper. Specifically, our approach operates the MMSE estimation in a low-dimensional subspace to avoid large matrix manipulation. Meanwhile, subspace projection helps to discriminate the desired signal and interfering signals in the power domain. Interference analysis shows the MMSE estimation can achieve interference-free estimation even in a low-dimensional subspace with a large number of BS antennas, and non-overlapping angles of arrival (AoAs) between desired and interfering UEs. Furthermore, thanks to the low-rank property of the channel covariance matrix in massive MU-MIMO systems, a two-step covariance matrix subspace projection method is proposed for further computational complexity reduction. The complexity analysis and simulation results indicate that our proposed approach has better channel estimation accuracy with lower complexity than the conventional MMSE estimation when the number of BS antennas is large.

Highlights

With rapid increasing numbers of smart phones, tablets, and wearable devices, the wireless data is increasing at an exponential rate in recent years [1]
The undesired interference caused by reusing pilot sequences is called pilot contamination [5], which degrades the accuracy of channel state information (CSI) estimation, resulting in performance degradation of the massive multi-user multiple-input multiple-output (MU-MIMO) system
To deal with the high complexity and the accuracy degradation of channel estimation when overlapping angle of arrival (AoA) happens in the conventional minimum mean square error (MMSE) estimation, we propose a low-complexity subspace MMSE estimation algorithm for the massive MU-MIMO system to mitigate pilot contamination

Summary

INTRODUCTION

With rapid increasing numbers of smart phones, tablets, and wearable devices, the wireless data is increasing at an exponential rate in recent years [1]. With more number of BS antennas, the desired channel vector becomes more orthogonal to the channel vectors of interfering UEs in the massive MU-MIMO system. When the massive MU-MIMO system works in the timedivision-duplex (TDD) mode, usually the BS estimates the channel by using training sequences (pilot sequences) sent from UEs. due to scarcity of time and frequency resources, if each UE takes an orthogonal pilot, the number of orthogonal pilots is insufficient, resulting in reusing pilots. Ohtsuki: Low-Complexity Subspace MMSE Channel Estimation in Massive MU-MIMO System method with low-complexity to mitigate pilot contamination is urgently required. To deal with the high complexity and the accuracy degradation of channel estimation when overlapping AoAs happens in the conventional MMSE estimation, we propose a low-complexity subspace MMSE estimation algorithm for the massive MU-MIMO system to mitigate pilot contamination. The channel matrix for all UEs in cell l and BS in cell j H(lj) ∈ CM×K is denoted by: H(lj) hl(j1) h(lj2) · · · hl(Kj)

PILOT SIGNAL AND UPLINK DATA SIGNAL

MMSE ESTIMATION IN SUBSPACE

COMPLEXITY ANALYSIS

LOW-RANK APPROXIMATION

SIMULATION RESULTS

CONCLUSION