Sparse Multipath Channel Estimation Using Compressive Sampling Matching Pursuit Algorithm

TL;DR

This paper proposes a novel sparse multipath channel estimation method using the CoSaMP algorithm, combining the advantages of greedy and convex optimization techniques for improved stability and practicality.

Contribution

Introduction of a CoSaMP-based channel estimation strategy that merges greedy and convex methods for better stability and implementation feasibility.

Findings

CoSaMP algorithm effectively estimates sparse multipath channels.

The proposed method outperforms existing greedy and convex approaches.

Enhanced stability and practicality in channel estimation.

Abstract

Wideband wireless channel is a time dispersive channel and becomes strongly frequency-selective. However, in most cases, the channel is composed of a few dominant taps and a large part of taps is approximately zero or zero. To exploit the sparsity of multi-path channel (MPC), two methods have been proposed. They are, namely, greedy algorithm and convex program. Greedy algorithm is easy to be implemented but not stable; on the other hand, the convex program method is stable but difficult to be implemented as practical channel estimation problems. In this paper, we introduce a novel channel estimation strategy using compressive sampling matching pursuit (CoSaMP) algorithm which was proposed in [1]. This algorithm will combine the greedy algorithm with the convex program method. The effectiveness of the proposed algorithm will be confirmed through comparisons with the existing methods.