Vector Autoregression Model Utilization for Massive-MIMO Channel Denoising

Abstract

In modern wireless communication systems Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, serving area, and the overall cell throughput with the antenna array beamforming. MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by User Equipments (UEs). UEs have limited power capabilities and thus cannot achieve high uplink (UL) signal-to-noise ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple methods that can be applied to improve the accuracy of the channel estimation (CE) in noisy conditions. In this paper the beam-delay Vector Autoregression (VAR) denoising method is proposed. The pre- and post-processing steps are described. The performance of VAR denoising is compared with the baseline approaches. The obtained results and possible improvements are discussed.