Open AccessDeep neural network‐based underwater OFDM receiver
Author(s) -
Zhang Jing,
Cao Yu,
Han Guangyao,
Fu Xiaomei
Publication year - 2019
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.0243
Subject(s) - computer science , channel (broadcasting) , orthogonal frequency division multiplexing , underwater acoustic communication , radio receiver design , bit error rate , artificial neural network , process (computing) , signal (programming language) , underwater , electronic engineering , real time computing , telecommunications , artificial intelligence , transmitter , engineering , programming language , operating system , oceanography , geology
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.