Affine Combination of Two Adaptive Sparse Filters for Estimating Large Scale MIMO Channels

TL;DR

This paper introduces an affine combination of two adaptive sparse filters to improve large scale MIMO channel estimation, reducing complexity and exploiting sparsity for better performance in next-generation wireless systems.

Contribution

It proposes a novel affine combination method with two sparse LMS filters and an approximate optimal combiner, enhancing channel estimation accuracy in large scale MIMO systems.

Findings

The proposed method outperforms traditional channel estimation techniques.

Simulation results confirm improved estimation accuracy.

The approach effectively exploits channel sparsity to reduce complexity.

Abstract

Large scale multiple-input multiple-output (MIMO) system is considered one of promising technologies for realizing next-generation wireless communication system (5G) to increasing the degrees of freedom in space and enhancing the link reliability while considerably reducing the transmit power. However, large scale MIMO system design also poses a big challenge to traditional one-dimensional channel estimation techniques due to high complexity and curse of dimensionality problems which are caused by long delay spread as well as large number antenna. Since large scale MIMO channels often exhibit sparse or/and cluster-sparse structure, in this paper, we propose a simple affine combination of adaptive sparse channel estimation method for reducing complexity and exploiting channel sparsity in the large scale MIMO system. First, problem formulation and standard affine combination of adaptive least mean square (LMS) algorithm are introduced. Then we proposed an effective affine combination method with two sparse LMS filters and designed an approximate optimum affine combiner according to stochastic gradient search method as well. Later, to validate the proposed algorithm for estimating large scale MIMO channel, computer simulations are provided to confirm effectiveness of the proposed algorithm which can achieve better estimation performance than the conventional one as well as traditional method.