Sparse Bayesian Multi-Task Learning of Time-Varying Massive MIMO Channels with Dynamic Filtering

TL;DR

This paper introduces a dynamic filtering approach for sparse Bayesian multi-task learning to efficiently estimate time-varying massive MIMO channels, reducing complexity and improving accuracy in wireless communication systems.

Contribution

It proposes a novel dynamic initialization method for MT-SBL that leverages previous time step information, enhancing convergence speed and estimation accuracy.

Findings

Reduced computational complexity and convergence time.

Negligible loss in estimation accuracy.

Lower power leakage through angular refinement.

Abstract

Sparsity of channel in the next generation of wireless communication for massive multiple-input-multiple-output (MIMO) systems can be exploited to reduce the overhead in the training. The multitask (MT)-sparse Bayesian learning (SBL) is applied for learning time-varying sparse channels in the uplink for multi-user massive MIMO orthogonal frequency division multiplexing systems. In particular, the dynamic information of the sparse channel is used to initialize the hyperparameters in the MT-SBL procedure for the next time step. Then, the expectation maximization based updates are applied to estimate the underlying parameters for different subcarriers. Through the simulation studies, it is observed that using the dynamic information from the previous time step considerably reduces the complexity and the required time for the convergence of MT-SBL algorithm with negligible sacrificing of the estimation accuracy. Finally, the power leakage is reduced due to considering angular refinement in the proposed algorithm.