Inverse Extrapolation for Efficient Precoding in Time-Varying Massive MIMO–OFDM Systems

Abstract

Regularized zero forcing (RZF) precoding is an efficient linear precoding scheme for combating interference in a single-cell massive multiple-input-multiple-output (MIMO) systems. Inaccurate channel state information (CSI) due to channel aging will reduce the performance of the precoder over time. The channel aging determines how often we need to estimate the channels, and thus how frequently we need to send pilots in order to maximize the overall data rate. Channel prediction is one way to improve the CSI accuracy in the downlink, without having to send new pilots but it requires frequent re-computation of the matrix inverse in the RZF precoder, which has high-computational complexity. In this paper, we consider massive MIMO-OFDM systems and propose an algorithm called inverse extrapolation that extrapolates the channel and inverse matrix coefficients separately. The RZF coefficients are then obtained with comparably low complexity with no need for matrix inversion. We compare this algorithm with the traditional way of computing the RZF coefficients through prediction of the channel matrix followed by matrix inversion. The simulation results show that the two predictors have the same performance when the number of antennas is large, and thus the proposed scheme is preferable since it can reduce the complexity. For example, a scenario is shown, where the complexity is reduced by 61.84% without a significant degradation in performance.