Sparsity-Aware Joint Frame Synchronization and Channel Estimation: Algorithm and USRP Implementation

TL;DR

This paper introduces a sparsity-aware joint frame synchronization and channel estimation method using OMP, improving performance over multi-path channels with practical USRP implementation.

Contribution

It proposes a novel joint estimation algorithm exploiting channel sparsity, validated through simulations and USRP experiments, enhancing system robustness.

Findings

Improved mean square error (MSE) in channel estimation.

Enhanced system performance over multi-path channels.

Successful USRP implementation demonstrating practical viability.

Abstract

Conventional correlation-based frame synchronization techniques can suffer significant performance degradation over multi-path frequency-selective channels. As a remedy, in this paper we consider joint frame synchronization and channel estimation. This, however, increases the length of the resulting combined channel and its estimation becomes more challenging. On the other hand, since the combined channel is a sparse vector, sparse channel estimation methods can be applied. We propose a joint frame synchronization and channel estimation method using the orthogonal matching pursuit (OMP) algorithm which exploits the sparsity of the combined channel vector. Subsequently, the channel estimate is used to design the equalizer. Our simulation results and experimental outcomes using software defined radios show that the proposed approach improves the overall system performance in terms of the mean square error (MSE) between the transmitted and the equalized symbols compared to the conventional method.