Adaptive MIMO Channel Estimation using Sparse Variable Step-Size NLMS Algorithms

TL;DR

This paper introduces two stable sparse variable step-size NLMS algorithms for adaptive MIMO channel estimation, exploiting channel sparsity to improve accuracy and stability over traditional fixed step-size methods.

Contribution

The paper proposes novel sparse VSS-NLMS algorithms that adapt step-size dynamically, enhancing MIMO channel estimation accuracy and stability compared to existing fixed step-size approaches.

Findings

The proposed algorithms outperform conventional methods in mean square error.

They achieve lower bit error rates in simulations.

The algorithms adapt effectively to channel sparsity.

Abstract

To estimate multiple-input multiple-output (MIMO) channels, invariable step-size normalized least mean square (ISSNLMS) algorithm was applied to adaptive channel estimation (ACE). Since the MIMO channel is often described by sparse channel model due to broadband signal transmission, such sparsity can be exploited by adaptive sparse channel estimation (ASCE) methods using sparse ISS-NLMS algorithms. It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance and computational cost. The previous approaches can exploit channel sparsity but their step-sizes are keeping invariant which unable balances well the three aspects and easily cause either estimation performance loss or instability. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE for estimating MIMO channels is formulated in MIMO systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISSNLMS algorithms and sparse VSS-NLMS ones are explained. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.