Low Complexity Joint Estimation of Synchronization Impairments in Sparse Channel for MIMO-OFDM System

TL;DR

This paper introduces a low complexity joint estimation method for synchronization impairments and channel in MIMO-OFDM systems, utilizing compressed sensing to improve performance with fewer samples.

Contribution

It proposes a novel ML algorithm combined with compressed sensing for joint estimation in sparse channels, reducing complexity and improving accuracy.

Findings

CS-based joint estimator outperforms LS-based methods

Reduced sample size still achieves accurate estimation

Enhanced robustness to synchronization impairments

Abstract

Low complexity joint estimation of synchronization impairments and channel in a single-user MIMO-OFDM system is presented in this letter. Based on a system model that takes into account the effects of synchronization impairments such as carrier frequency offset, sampling frequency offset, and symbol timing error, and channel, a Maximum Likelihood (ML) algorithm for the joint estimation is proposed. To reduce the complexity of ML grid search, the number of received signal samples used for estimation need to be reduced. The conventional channel estimation methods using Least-Squares (LS) fail for the reduced sample under-determined system, which results in poor performance of the joint estimator. The proposed ML algorithm uses Compressed Sensing (CS) based channel estimation method in a sparse fading scenario, where the received samples used for estimation are less than that required for an LS based estimation. The performance of the estimation method is studied through numerical simulations, and it is observed that CS based joint estimator performs better than LS based joint estimator