Time-Varying Channel Prediction for Pilot Contamination Mitigation in Hybrid Massive MIMO Communications

For 5G massive MIMO systems, hybrid beamforming (HB) has been investigated thoroughly because it is composed of analog beamforming (AB) and digital beamforming (DB), and can reduce the number of baseband and RF circuits drastically. However, HB still has a major problem of pilot contamination and thus suffers from serious degradation of the channel estimation accuracy, when the same pilot signals are shared among multiple user terminals. To cope with this problem under time-varying channel conditions, we propose a method for adaptively predicting downlink (DL) channels while mitigating the pilot contamination. Considering time division duplex (TDD) multiuser MIMO communications, the proposed scheme firstly estimates an average angle of arrival (AoA) for each user's cluster by applying multiple signal classification (MUSIC) to the received (Rx) pilot signals, which can reconstruct the channel frequency responses of the interfering users and then extracts the desired channel frequency responses from the combined ones. Since the desired channel frequency responses can be considerably extracted, the proposed scheme coarsely compensates for time-variation of the Rx signal by predicting the Doppler effect of each cluster. The residual time-variation can be tracked and removed using detected signals of all the streams in a decision-directed manner. Finally, the estimated total time-variation is used to predict the DL channel frequency response. Computer simulations demonstrate that the proposed scheme can accurately track the time-varying channels while mitigating the pilot contamination.