Author Identifier

Muhammad Haziq: https://orcid.org/0009-0006-7406-8149

Date of Award

2025

Document Type

Thesis

Publisher

Edith Cowan University

Degree Name

Master of Engineering Science

School

School of Engineering

First Supervisor

Quoc Viet Phung

Second Supervisor

Stefan Lachowicz

Abstract

Underwater acoustic channels are highly time-varying and suffer from Doppler distortions and frequency-selective fades, which degrade the performance of the OFDM receiver. Therefore, this research enhances the transmission capability of OFDM by addressing two core issues: Doppler distortions and frequency-selective fades. The proposed study mitigates the Doppler shift at the subcarrier level using the cross-ambiguity function and employs an AI-based channel predictor that forecasts instantaneous channel gains for dynamic subcarrier power allocation. By integrating subcarrier-level CAF Doppler correction with predictive, adaptive power allocation, this research aims to improve spectral efficiency maximize per-subcarrier SNR, and increase the data rate in UWAC systems.

Simulation results confrm that the proposed CAF Doppler mitigation restores orthogonality and reduces ICI by over 3 dB. Furthermore, the CNN-based predictor delivers channel-state estimates with a normalized MSE below 1 × 10−2 , and the capped water-filling algorithm achieves a roughly 20% reduction in BER as compared to conventional OFDM with equal power allocation. This approach relies on standard pilot subcarriers and a lightweight CNN, making it compatible with existing OFDM frameworks.

Access Note

Access to this thesis is embargoed until 31st October 2026

DOI

10.25958/s1rq-0f83

Available for download on Saturday, October 31, 2026

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