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A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication

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Publikašuvnnas:Journal of Marine Science and Engineering vol. 13, no. 7 (2025), p. 1284-1320
Váldodahkki: Muhammad, Adil
Eará dahkkit: Liu Songzuo, Suleman, Mazhar, Alharbi Ayman, Honglu, Yan, Muzzammil Muhammad
Almmustuhtton:
MDPI AG
Fáttát:
Underwater communication
Communication
Signal processing
Bit error rate
Machine learning
Orthogonal Frequency Division Multiplexing
Telecommunications
Deep learning
Transfer learning
Efficiency
Propagation
Performance enhancement
Environmental factors
Training
Neural networks
Communications systems
Methods
Performance degradation
Algorithms
Underwater acoustics
Underwater
Signal to noise ratio
Environmental
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Abstrákta:The underwater acoustic (UWA) communication system faces challenges due to environmental factors, extensive multipath spread, and rapidly changing propagation conditions. Deep learning based solutions, especially for orthogonal frequency division multiplexing (OFDM) receivers, have been shown to improve performance. However, the UWA channel characteristics are highly dynamic and depend on the specific underwater conditions. Therefore, these models suffer from model mismatch when deployed in environments different from those used for training, leading to performance degradation and requiring costly, time-consuming retraining. To address these issues, we propose a transfer learning (TL)-based pre-trained model for OFDM based UWA communication. Rather than training separate models for each underwater channel, we aggregate received signals from five distinct WATERMARK channels, across varying signal to noise ratios (SNRs), into a unified dataset. This diverse training set enables the model to generalize across various underwater conditions, ensuring robust performance without extensive retraining. We evaluate the pre-trained model using real-world data from Qingdao Lake in Hangzhou, China, which serves as the target environment. Our experiments show that the model adapts well to these challenging environment, overcoming model mismatch and minimizing computational costs. The proposed TL-based OFDM receiver outperforms traditional methods in terms of bit error rate (BER) and other evaluation metrics. It demonstrates strong adaptability to varying channel conditions. This includes scenarios where training and testing occur on the same channel, under channel mismatch, and with or without fine-tuning on target data. At 10 dB SNR, it achieves an approximately 80% improvement in BER compared to other methods.
ISSN:2077-1312
DOI:10.3390/jmse13071284
Gáldu:Engineering Database