Channel Prediction in Time-Varying Massive MIMO Environments

TLDR

Massive MIMO channels vary rapidly and non‑stationarily, making conventional channel state information obsolete and degrading performance. The paper proposes a channel prediction algorithm for massive MIMO environments. The algorithm models the channel with a first‑order Taylor expansion, then estimates and predicts the state, and derives an interval of effective prediction. Simulations show that within the effective prediction interval the algorithm achieves reliable channel prediction with low computational complexity.

Abstract

The massive MIMO channel is characterized by non-stationarity and fast variation, thereby the channel state information obtained by traditional methods will be outdated and the system performance will be degraded. In this paper, we propose a channel prediction algorithm in massive MIMO environments. First, considering the channel characteristics, we propose a first-order Taylor expansion-based predictive channel modeling method. Then, a channel prediction algorithm consisting of the estimation stage and prediction stage is proposed and the interval of effective prediction (IEP) is derived. The performance of the proposed algorithm is testified by numerical simulations. It is shown that, within the IEP, a reliable channel prediction can be obtained with low computational complexity.