Variational Learning Algorithms For Channel Estimation in RIS-assisted mmWave Systems

TL;DR

This paper introduces two variational expectation maximization algorithms for efficient channel estimation in RIS-assisted mmWave systems, leveraging structured sparsity to improve accuracy and reduce computational complexity.

Contribution

The paper proposes two novel variational algorithms, SM-SBL and FM-SBL, that exploit channel sparsity and reduce complexity in RIS-aided mmWave systems.

Findings

Both algorithms outperform existing methods in accuracy.

FM-SBL has lower computational complexity than SM-SBL.

Numerical results demonstrate improved channel estimation performance.

Abstract

We consider the problem of estimating the channel in reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) systems. We propose two variational expectation maximization (VEM) based algorithms for channel estimation in RIS-aided wireless systems. The first algorithm is a structured mean field-based sparse Bayesian learning (SM-SBL) algorithm that exploits the doubly-structured sparsity and the individual sparsity of the elements of the channel. To exploit the sparsities, we propose a column-wise coupled Gaussian prior. We next design the factorized mean field-based algorithm based on the prior we propose. This algorithm called the factorized mean field SBL (FM-SBL) algorithm, addresses the time complexities of the SM-SBL algorithm without sacrificing channel estimation accuracy. We show using extensive numerical investigations that the i) proposed SM-SBL and FM-SBL algorithms outperform several existing algorithms and ii) FM-SBL has lower time complexity than the SM-SBL algorithm.