New Results on Joint Channel and Impulsive Noise Estimation and Tracking in Underwater Acoustic OFDM Systems
Impulsive noise can greatly affect the performance of underwater acoustic (UA) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, by utilizing the sparsity of the UA channel impulse response and impulsive noise, we first propose a novel sparse Bayesian learning (SBL) based exp...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2020
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP140102131 http://hdl.handle.net/20.500.11937/88932 |
| _version_ | 1848765117225762816 |
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| author | Wang, S. He, Z. Niu, K. Chen, Jaden Rong, Yue |
| author_facet | Wang, S. He, Z. Niu, K. Chen, Jaden Rong, Yue |
| author_sort | Wang, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Impulsive noise can greatly affect the performance of underwater acoustic (UA) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, by utilizing the sparsity of the UA channel impulse response and impulsive noise, we first propose a novel sparse Bayesian learning (SBL) based expectation maximization (EM) algorithm for joint channel estimation and impulsive noise mitigation in UA OFDM systems. Secondly, considering that the UA channel and impulsive noise are fast time-varying, we develop a new approach which combines the SBL with the forward-backward Kalman filtering to track the UA channel and impulsive noise. To further improve the system performance, we utilize the information available on data subcarriers for joint time-varying channel estimation and data detection, based on the SBL algorithm and the Kalman filter. The performance of our proposed algorithms is verified through both numerical simulations and by data collected during a UA communication experiment conducted in the estuary of the Swan River, Perth, Australia. The results demonstrate that compared with existing approaches, the proposed algorithms achieve a better system bit-error-rate and frame-error-rate performance. |
| first_indexed | 2025-11-14T11:30:09Z |
| format | Journal Article |
| id | curtin-20.500.11937-88932 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:30:09Z |
| publishDate | 2020 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-889322025-04-28T03:21:04Z New Results on Joint Channel and Impulsive Noise Estimation and Tracking in Underwater Acoustic OFDM Systems Wang, S. He, Z. Niu, K. Chen, Jaden Rong, Yue Science & Technology Technology Engineering, Electrical & Electronic Telecommunications Engineering Kalman filter impulsive noise OFDM sparse Bayesian learning underwater acoustic communication COMMUNICATION MITIGATION ALGORITHMS Impulsive noise can greatly affect the performance of underwater acoustic (UA) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, by utilizing the sparsity of the UA channel impulse response and impulsive noise, we first propose a novel sparse Bayesian learning (SBL) based expectation maximization (EM) algorithm for joint channel estimation and impulsive noise mitigation in UA OFDM systems. Secondly, considering that the UA channel and impulsive noise are fast time-varying, we develop a new approach which combines the SBL with the forward-backward Kalman filtering to track the UA channel and impulsive noise. To further improve the system performance, we utilize the information available on data subcarriers for joint time-varying channel estimation and data detection, based on the SBL algorithm and the Kalman filter. The performance of our proposed algorithms is verified through both numerical simulations and by data collected during a UA communication experiment conducted in the estuary of the Swan River, Perth, Australia. The results demonstrate that compared with existing approaches, the proposed algorithms achieve a better system bit-error-rate and frame-error-rate performance. 2020 Journal Article http://hdl.handle.net/20.500.11937/88932 10.1109/TWC.2020.2966622 English http://purl.org/au-research/grants/arc/DP140102131 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC fulltext |
| spellingShingle | Science & Technology Technology Engineering, Electrical & Electronic Telecommunications Engineering Kalman filter impulsive noise OFDM sparse Bayesian learning underwater acoustic communication COMMUNICATION MITIGATION ALGORITHMS Wang, S. He, Z. Niu, K. Chen, Jaden Rong, Yue New Results on Joint Channel and Impulsive Noise Estimation and Tracking in Underwater Acoustic OFDM Systems |
| title | New Results on Joint Channel and Impulsive Noise Estimation and Tracking in Underwater Acoustic OFDM Systems |
| title_full | New Results on Joint Channel and Impulsive Noise Estimation and Tracking in Underwater Acoustic OFDM Systems |
| title_fullStr | New Results on Joint Channel and Impulsive Noise Estimation and Tracking in Underwater Acoustic OFDM Systems |
| title_full_unstemmed | New Results on Joint Channel and Impulsive Noise Estimation and Tracking in Underwater Acoustic OFDM Systems |
| title_short | New Results on Joint Channel and Impulsive Noise Estimation and Tracking in Underwater Acoustic OFDM Systems |
| title_sort | new results on joint channel and impulsive noise estimation and tracking in underwater acoustic ofdm systems |
| topic | Science & Technology Technology Engineering, Electrical & Electronic Telecommunications Engineering Kalman filter impulsive noise OFDM sparse Bayesian learning underwater acoustic communication COMMUNICATION MITIGATION ALGORITHMS |
| url | http://purl.org/au-research/grants/arc/DP140102131 http://hdl.handle.net/20.500.11937/88932 |