Semi-blind Channel Estimation and Data Detection for Multi-cell Massive MIMO Systems on Time-Varying Channels

TL;DR

This paper introduces an expectation propagation-based algorithm for semi-blind channel estimation and data detection in multi-cell massive MIMO systems with time-varying channels, outperforming traditional methods.

Contribution

It develops a novel EP-based iterative algorithm for joint channel estimation and data detection in dynamic multi-cell MIMO environments.

Findings

EP-based algorithm outperforms KF-M and KS-M in accuracy

Performance improves with more antennas and longer frames

Algorithms surpass block-fading channel methods in time-varying scenarios

Abstract

We study the problem of semi-blind channel estimation and symbol detection in the uplink of multi-cell massive MIMO systems with spatially correlated time-varying channels. An algorithm based on expectation propagation (EP) is developed to iteratively approximate the joint a posteriori distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. This distribution is then used for direct estimation of the channel matrix and detection of data symbols. A modified version of the popular Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize the EP-based algorithm. Performance of the Kalman smoothing algorithm followed by KF-M is also examined. Simulation results demonstrate that channel estimation error and the symbol error rate (SER) of the semi-blind KF-M, KS-M, and EP-based algorithms improve with the increase in the number of base station antennas and the length of the transmitted frame. It is shown that the EP-based algorithm significantly outperforms KF-M and KS-M algorithms in channel estimation and symbol detection. Finally, our results show that when applied to time-varying channels, these algorithms outperform the algorithms that are developed for block-fading channel models.