| Autor: |
Chuanxi Xing, Yanling Ran, Guangzhi Tan, Qiang Meng, Mao Lu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 123376-123387 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3454316 |
| Popis: |
To address the problem of reduced channel estimation performance in underwater acoustic Orthogonal Frequency Division Multiplexing (OFDM) systems due to impulse noise, a method for impulse noise suppression and underwater acoustic channel estimation based on Temporally Multiple Sparse Bayesian Learning (TMSBL) is proposed. The method exploits the sparse nature of the underwater acoustic channel and impulse noise, which transforms the channel and impulse noise estimation problem into a compressed multi-measurement perception problem. Firstly, the joint estimation of channel and impulse noise is implemented using TMSBL(JCI-TMSBL), which effectively mitigates the impact of impulse noise on the performance of the algorithm. The noise-reduced received pilot matrix is then combined with the estimated time-domain channel impulse response to obtain the a priori information for TMSBL channel estimation, which improves the performance of channel estimation. Finally, the method applies the TMSBL channel estimation technique to achieve the joint estimation of underwater acoustic channels associated with different symbols. Simulation results demonstrate that the proposed method reduces the normalized mean square error of channel estimation by approximately 93.87% and the runtime by approximately 10.67% compared to the TMSBL method. This indicates that the proposed method improves the accuracy of the algorithm and reduces its computational complexity. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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