Pilot-Assisted Underwater Acoustic Channel Estimation for MIMO OFDM Systems Using Sparse Bayesian Learning Algorithm

Asmaa Yahia, Eman Hamdan

Abstract


The field of underwater acoustic communication (UWA) has many industrial and maritime applications. This study focuses on cutting-edge channel estimation algorithms for UWA communications based on compressed sensing (CS). Since underwater channels involve sparse multipath, this investigation scrutinizes the process of channel estimation in systems employing multiple-input multiple-output (MIMO) technology with orthogonal frequency division multiplexing (OFDM). It interprets the utilization of pilot tones within the framework of a compressive sensing challenge. The performance of Compressive Sampling Matching Pursuit (CoSaMP) and Sparse Bayesian Learning (SBL) algorithms is compared with the conventional least square (LS) estimation algorithm by simulation.

The research infers that, methodologies rooted in compressed sensing yield superior channel estimation compared to the conventional LS algorithm for underwater communication systems utilizing MIMO-OFDM. For CS algorithms the simulation shows that SBL algorithm outperforms CoSaMP algorithm. Mean square error (MSE) and bit error rate (BER) are used to quantify this superiority when signal-to-noise ratio (SNR) conditions vary, employing both uniform and dispersed pilot configurations.

  

Keywords


channel estimation, sparse signal, Sparse Bayesian Learning, compressed sensing.

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References


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DOI: https://dx.doi.org/10.21622/MARLOG.2024.13.1.102

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Copyright (c) 2025 Asmaa Yahia, Eman Hamdan

The International Maritime Transport and Logistics Journal (MARLOG)

E-ISSN: 2974-3141
P-ISSN: 2974-3133

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt