Exploiting Sparse Matrix Solution to Mitigate Channel Aging in Sub-6 GHz 5G MU-MIMO Using Beamspace-Delay-Doppler Domain

This paper proposes a novel uplink (UL) channel prediction technique to combat the effects of channel aging in multiuser multiple-input multiple-output (MU-MIMO) systems by exploiting the sparsity of channel matrix in three-stage-transformed domain, more specifically in the beamspace-delay-Doppler domain. Channel aging is a typical problem that impairs wireless systems due to inaccurate beamforming (BF) weights calculated from obsolete channel state information. This results in throughput degradation due to residual interference among spatially-multiplexed users. In the proposed method, the channel estimates calculated at fixed intervals from UL sounding reference signals (SRS) are subjected to three-stage domain transformations, namely, beamspace, delay, and Doppler domains to attain sparsity. Channel prediction is performed in the transformed domain to obtain the channel estimates at time-slots where no SRS transmission is scheduled. Upon inverse transformation to the initial domain, downlink BF weights are calculated based on the predicted channel estimates. We evaluate the proposed method using system-level simulation of 5G new radio (NR) sub-6 GHz DL MU-MIMO employing the 3GPP urban micro channel model and demonstrate the throughput improvement.