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- W4312309767 abstract "The underwater acoustic (UWA) channel in the delay-Doppler domain is more sparse and slower time-varying than in the time-delay domain. Hence, the channel dynamics can be tracked more efficiently in the delay-Doppler spreading function (DDSF) representation. In this paper, we propose a data detection scheme with sparse channel prediction in the DDSF representation. Moreover, the sparse channel prediction is further improved by the reinforcement learning in the DDSF representation. Experimental result shows that proposed schemes can obtain a 3. 6-7.5dB bit error rate (BER) performance improvement compared with the traditional channel prediction." @default.
- W4312309767 created "2023-01-04" @default.
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- W4312309767 date "2022-10-17" @default.
- W4312309767 modified "2023-09-27" @default.
- W4312309767 title "Underwater Acoustic Channel Prediction in delay-Doppler Domain based on Reinforcement Learning" @default.
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- W4312309767 doi "https://doi.org/10.1109/oceans47191.2022.9977194" @default.
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