Based on the N2N-SAMP for sparse underwater acoustic channel estimation
IntroductionOrthogonal Frequency Division Multiplexing (OFDM) is widely recognized for its high efficiency in modulation techniques and has been extensively applied in underwater acoustic communication. However, the unique sparsity and noise interference characteristics of the underwater channel pos...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1460388/full |
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| author | Zhen Wang Zhen Wang Maofa Wang Yangzhen Wang Yangzhen Wang Zhenjing Zhu Zhenjing Zhu Guangtao Shang Guangtao Shang Jiabao Zhao Jiabao Zhao Ning Hu Ning Hu |
| author_facet | Zhen Wang Zhen Wang Maofa Wang Yangzhen Wang Yangzhen Wang Zhenjing Zhu Zhenjing Zhu Guangtao Shang Guangtao Shang Jiabao Zhao Jiabao Zhao Ning Hu Ning Hu |
| author_sort | Zhen Wang |
| collection | DOAJ |
| description | IntroductionOrthogonal Frequency Division Multiplexing (OFDM) is widely recognized for its high efficiency in modulation techniques and has been extensively applied in underwater acoustic communication. However, the unique sparsity and noise interference characteristics of the underwater channel pose significant challenges to the performance of traditional channel estimation methods.MethodsTo address these challenges, we propose a sparse underwater channel estimation method that combines the Noise2Noise (N2N) algorithm with the Sparsity Adaptive Matching Pursuit (SAMP) algorithm. This novel approach integrates the N2N technique from image denoising theory with the SAMP algorithm, utilizing a constant iteration termination threshold that does not require prior information. The method leverages the U-net neural network structure to denoise noisy pilot signals, thereby restoring channel sparsity and enhancing the accuracy of channel estimation.ResultsSimulation results indicate that our proposed method demonstrates commendable channel estimation performance across various signal-to-noise ratio (SNR) conditions. Notably, in low SNR environments, the N2N-SAMP algorithm significantly outperforms the traditional SAMP algorithm in terms of Mean Squared Error (MSE) and Bit Error Rate (BER). Specifically, at SNR levels of 0 dB, 10 dB, and 20 dB, the MSE of channel estimation is reduced by 58.95%, 76.08%, and 19.42%, respectively, compared to the SAMP algorithm that selects the optimal threshold based on noise power. Furthermore, the system’s BER is decreased by 12.35%, 26.41%, and 29.62%, respectively.DiscussionThe findings suggest that the integration of N2N and SAMP algorithms offers a promising solution for improving channel estimation in underwater communication channels, especially under low SNR conditions. The significant reduction in MSE and BER highlights the effectiveness of our proposed method in enhancing the reliability and accuracy of underwater communication systems. |
| format | Article |
| id | doaj-art-c20dab08f2a64df0baba56e7429568b1 |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-c20dab08f2a64df0baba56e7429568b12024-12-17T05:10:18ZengFrontiers Media S.A.Frontiers in Physics2296-424X2024-12-011210.3389/fphy.2024.14603881460388Based on the N2N-SAMP for sparse underwater acoustic channel estimationZhen Wang0Zhen Wang1Maofa Wang2Yangzhen Wang3Yangzhen Wang4Zhenjing Zhu5Zhenjing Zhu6Guangtao Shang7Guangtao Shang8Jiabao Zhao9Jiabao Zhao10Ning Hu11Ning Hu12Ocean Engineering Research Center, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Mecharical Engineering, Hangzhou Dianzi University, Hangzhou, ChinaOcean Engineering Research Center, Hangzhou Dianzi University, Hangzhou, ChinaOcean Engineering Research Center, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Mecharical Engineering, Hangzhou Dianzi University, Hangzhou, ChinaOcean Engineering Research Center, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Mecharical Engineering, Hangzhou Dianzi University, Hangzhou, ChinaOcean Engineering Research Center, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Mecharical Engineering, Hangzhou Dianzi University, Hangzhou, ChinaOcean Engineering Research Center, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Mecharical Engineering, Hangzhou Dianzi University, Hangzhou, ChinaOcean Engineering Research Center, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Mecharical Engineering, Hangzhou Dianzi University, Hangzhou, ChinaIntroductionOrthogonal Frequency Division Multiplexing (OFDM) is widely recognized for its high efficiency in modulation techniques and has been extensively applied in underwater acoustic communication. However, the unique sparsity and noise interference characteristics of the underwater channel pose significant challenges to the performance of traditional channel estimation methods.MethodsTo address these challenges, we propose a sparse underwater channel estimation method that combines the Noise2Noise (N2N) algorithm with the Sparsity Adaptive Matching Pursuit (SAMP) algorithm. This novel approach integrates the N2N technique from image denoising theory with the SAMP algorithm, utilizing a constant iteration termination threshold that does not require prior information. The method leverages the U-net neural network structure to denoise noisy pilot signals, thereby restoring channel sparsity and enhancing the accuracy of channel estimation.ResultsSimulation results indicate that our proposed method demonstrates commendable channel estimation performance across various signal-to-noise ratio (SNR) conditions. Notably, in low SNR environments, the N2N-SAMP algorithm significantly outperforms the traditional SAMP algorithm in terms of Mean Squared Error (MSE) and Bit Error Rate (BER). Specifically, at SNR levels of 0 dB, 10 dB, and 20 dB, the MSE of channel estimation is reduced by 58.95%, 76.08%, and 19.42%, respectively, compared to the SAMP algorithm that selects the optimal threshold based on noise power. Furthermore, the system’s BER is decreased by 12.35%, 26.41%, and 29.62%, respectively.DiscussionThe findings suggest that the integration of N2N and SAMP algorithms offers a promising solution for improving channel estimation in underwater communication channels, especially under low SNR conditions. The significant reduction in MSE and BER highlights the effectiveness of our proposed method in enhancing the reliability and accuracy of underwater communication systems.https://www.frontiersin.org/articles/10.3389/fphy.2024.1460388/fullOFDMunderwater acoustic communicationSAMPNoise2Noise (N2N)U-net |
| spellingShingle | Zhen Wang Zhen Wang Maofa Wang Yangzhen Wang Yangzhen Wang Zhenjing Zhu Zhenjing Zhu Guangtao Shang Guangtao Shang Jiabao Zhao Jiabao Zhao Ning Hu Ning Hu Based on the N2N-SAMP for sparse underwater acoustic channel estimation Frontiers in Physics OFDM underwater acoustic communication SAMP Noise2Noise (N2N) U-net |
| title | Based on the N2N-SAMP for sparse underwater acoustic channel estimation |
| title_full | Based on the N2N-SAMP for sparse underwater acoustic channel estimation |
| title_fullStr | Based on the N2N-SAMP for sparse underwater acoustic channel estimation |
| title_full_unstemmed | Based on the N2N-SAMP for sparse underwater acoustic channel estimation |
| title_short | Based on the N2N-SAMP for sparse underwater acoustic channel estimation |
| title_sort | based on the n2n samp for sparse underwater acoustic channel estimation |
| topic | OFDM underwater acoustic communication SAMP Noise2Noise (N2N) U-net |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2024.1460388/full |
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