Time Varying Channel Tracking With Spatial and Temporal BEM for Massive MIMO Systems

In this paper, we design a channel tracking method for massive multiple-input multiple-output systems under both time-varying and spatial-varying circumstances. By exploiting the characteristics of massive antenna array, a spatial-temporal basis expansion model is proposed to reduce the effective dimension of uplink/downlink channel, which decomposes channel state information into time-varying spatial information and gain information. We first model the user's movement as the one-order unknown Markov process, whose parameters are blindly obtained by expectation and maximization learning. Then, the uplink time-varying spatial information can also be blindly tracked by unscented Kalman filter and Taylor series expansion of the steering vector, while the rest of uplink channel gain information can be trained by only a few pilot symbols. Due to physical angle reciprocity, the spatial information of the downlink channel can be immediately computed from the uplink counterpart, which greatly reduces the complexity of downlink channel tracking. Various numerical results are provided to demonstrate the effectiveness of the proposed method.