Adaptive Sparse Channel Estimation for Time-Variant MIMO-OFDM Systems

TL;DR

This paper introduces two adaptive sparse channel estimation methods for time-variant MIMO-OFDM systems that leverage channel sparsity, demonstrating improved performance over traditional NLMS-based approaches.

Contribution

The paper proposes novel adaptive sparse channel estimation techniques that incorporate sparsity-promoting penalties into NLMS algorithms for better accuracy in time-variant MIMO-OFDM systems.

Findings

Proposed methods outperform traditional ACE in simulations.

Sparse penalties improve channel estimation accuracy.

Enhanced stability and low complexity of the new methods.

Abstract

Accurate channel state information (CSI) is required for coherent detection in time-variant multiple-input multipleoutput (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) modulation. One of low-complexity and stable adaptive channel estimation (ACE) approaches is the normalized least mean square (NLMS)-based ACE. However, it cannot exploit the inherent sparsity of MIMO channel which is characterized by a few dominant channel taps. In this paper, we propose two adaptive sparse channel estimation (ASCE) methods to take advantage of such sparse structure information for time-variant MIMO-OFDM systems. Unlike traditional NLMS-based method, two proposed methods are implemented by introducing sparse penalties to the cost function of NLMS algorithm. Computer simulations confirm obvious performance advantages of the proposed ASCEs over the traditional ACE.