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• W4285082152 abstract "In recent years, the study of deep learning (DL) applications for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communication has been received increasing attention. However, the performance analysis of the deep neural network (DNN) based method, as well as its generality evaluation with the UWA environment mismatch are always lacking. In this paper, a DNN aided channel estimation is firstly developed for the UWA-OFDM system. Theoretical analyses and agreeable experimental results are given, which demonstrate that the properly designed DNN estimator can approach the minimum mean square error (MMSE) solution under certain conditions. According to the experiments with the at-sea-measured WATERMARK dataset, the near-optimal performance of the proposed method is evidenced and, more than 40% bit error rate (BER) performance improvements are achieved compared to the least square (LS) algorithm. Besides, the statistical properties of the UWA channel are mathematically represented. Combined with experimental results, we further clarify the underlying relationship between the model performance and UWA environment conditions. Such conclusions can provide strong guidance for the model transfer/adjustment when it suffers UWA environment mismatches. Finally, the feasibility of model transfer is confirmed, the experimental result shows that the BER is reduced substantially by 69.8% between two UWA environments." @default.
• W4285082152 created "2022-07-14" @default.
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• W4285082152 date "2022-09-01" @default.
• W4285082152 modified "2023-10-02" @default.
• W4285082152 title "On the performance of deep neural network aided channel estimation for underwater acoustic OFDM communications" @default.
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• W4285082152 doi "https://doi.org/10.1016/j.oceaneng.2022.111518" @default.
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