Deep neural network‐based underwater OFDM receiver. Issue 13 (1st August 2019)

• Record Type: Journal Article
• Authors: Zhang, Jing; Cao, Yu; Han, Guangyao; Fu, Xiaomei
• Is Part Of: IET communications. Volume 13:Issue 13(2019)
• Page Start: 1998
• Page End: 2002
• Publication Date: 2019-08-01
• Subjects: radio receivers -- error statistics -- OFDM modulation -- underwater acoustic communication -- signal processing -- neural nets -- telecommunication computing; receiver design -- UWA channel -- deep neural network‐based orthogonal frequency division multiplexing receiver -- UWA communication -- single neural network -- signal processing -- general receiver -- underwater acoustic channel -- underwater OFDM receiver -- underwater OFDM receiver; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.0243
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : Due to the characteristics of the underwater acoustic (UWA) channel, the process at the receiver is complicated to match the channel. To simplify receiver design and match UWA channel better, this study proposes a deep neural network‐based orthogonal frequency division multiplexing receiver for UWA communication. Different from existing receivers needing a neural network and several other processing parts, the proposed receiver only uses a single neural network to implement the whole signal processing. Moreover, it is a general receiver which is suitable for other modulation schemes. Simulation results show that the proposed receiver offers better bit error rate performance over traditional ones.