Sparse Channel Estimation in Wideband Systems with Geometric Sequence Decomposition

TL;DR

This paper introduces a novel sparse channel estimation method for wideband systems that leverages geometric sequence decomposition, outperforming existing algorithms especially at high SNR and large bandwidths.

Contribution

It proposes a new approach converting the channel estimation problem into geometric sequence parameter extraction, differing from traditional compressed sensing methods.

Findings

Superior performance at high SNR

Effective in large bandwidth conditions

Outperforms existing algorithms

Abstract

The sparsity of multipaths in the wideband channel has motivated the use of compressed sensing for channel estimation. In this letter, we propose a different approach to sparse channel estimation. We exploit the fact that $L$ taps of channel impulse response in time domain constitute a non-orthogonal superposition of $L$ geometric sequences in frequency domain. This converts the channel estimation problem into the extraction of the parameters of geometric sequences. Numerical results show that the proposed scheme is superior to existing algorithms in high signal-to-noise ratio (SNR) and large bandwidth conditions.