A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
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 imp...
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/7/1284 |
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| author | Muhammad Adil Songzuo Liu Suleman Mazhar Ayman Alharbi Honglu Yan Muhammad Muzzammil |
| author_facet | Muhammad Adil Songzuo Liu Suleman Mazhar Ayman Alharbi Honglu Yan Muhammad Muzzammil |
| author_sort | Muhammad Adil |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-88967a4559fd443f874ac3676e5ccd31 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-88967a4559fd443f874ac3676e5ccd312025-08-20T03:08:01ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01137128410.3390/jmse13071284A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic CommunicationMuhammad Adil0Songzuo Liu1Suleman Mazhar2Ayman Alharbi3Honglu Yan4Muhammad Muzzammil5National Key Laboratory of Underwater Acoustic Technology, Harbin 150001, ChinaNational Key Laboratory of Underwater Acoustic Technology, Harbin 150001, ChinaNational Key Laboratory of Underwater Acoustic Technology, Harbin 150001, ChinaComputer and Network Engineering Department, College of Computing, Umm Al-Qura University, Mecca 24231, Saudi ArabiaNational Key Laboratory of Underwater Acoustic Technology, Harbin 150001, ChinaNational Key Laboratory of Underwater Acoustic Technology, Harbin 150001, ChinaThe 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.https://www.mdpi.com/2077-1312/13/7/1284OFDMchannel estimationtransfer learningunderwater acousticsWATERMARK |
| spellingShingle | Muhammad Adil Songzuo Liu Suleman Mazhar Ayman Alharbi Honglu Yan Muhammad Muzzammil A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication Journal of Marine Science and Engineering OFDM channel estimation transfer learning underwater acoustics WATERMARK |
| title | A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication |
| title_full | A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication |
| title_fullStr | A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication |
| title_full_unstemmed | A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication |
| title_short | A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication |
| title_sort | novel transfer learning based ofdm receiver design for enhanced underwater acoustic communication |
| topic | OFDM channel estimation transfer learning underwater acoustics WATERMARK |
| url | https://www.mdpi.com/2077-1312/13/7/1284 |
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