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- W3007471567 abstract "The paper presents a brief survey on the underwater acoustic communication channel estimation of orthogonal frequency division multiplexing using deep learning techniques. In recent times, deep learning has shown great potential in the field of communication. The underwater acoustic channel is a highly challenging system as it has a restricted bandwidth, enlarged multi-path, medium refractive characteristics, acute fading, rapid time variation, large Doppler shifts, etc. For the UWA-OFDM communication model, DL-based receivers can be elucidated as a deep neural network for a better estimation than the conventional techniques. Sufficient training is required for the recovery of the transmitted symbols. It does not require any explicit CE or equalization and balancing. The estimation is accomplished in two stages. The first one is training stage. For training, this deep neural network-transmitted data, the signal received in the unknown channel, etc., are used as labeled data. The second one is test stage, where the deep neural network receiver recovers transmitted symbols, given the received signal. The accuracy and the computational complexity, these two have been a great challenge for the underwater acoustic channel estimation with the existing traditional channel estimation methods. The deep learning method is useful to solve these problems. This survey is to find out a few optimum methods of deep learning to estimate the UWA channel in an efficient way." @default.
- W3007471567 created "2020-03-06" @default.
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- W3007471567 date "2020-01-01" @default.
- W3007471567 modified "2023-10-16" @default.
- W3007471567 title "A Survey Report on Deep Learning Techniques in Underwater Acoustic Channels" @default.
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- W3007471567 doi "https://doi.org/10.1007/978-981-15-2449-3_35" @default.
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