Year: 2018
Type: conference-paper
Abstract: In this paper, we present a deep learning based underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication system.Unlike the traditional receiver for UWA OFDM communication system, the deep learning based receiver interpreted as a deep neural network (DNN) can recover the transmitted symbols directly without explicit channel estimation and equalization after sufficient training.The estimation of transmitted symbols in the DNN based receiver is achieved in two stages: 1) training stage, where labeled data such as known transmitted data and signal received in the unknown channel are used to train the DNN, and 2) test stage, where the DNN receiver recovers transmitted symbols given the received signal.To demonstrate the performance of the deep learning based UWA OFDM communications, we generate a large number of labeled and unlabeled data by using an acoustic propagation model with a measured sound speed profile to train and test the DNN receiver.The performance of the deep learning based UWA OFDM communications is evaluated under various system parameters, such as the cyclic prefix (CP) length, number of pilot symbols, and others.Simulation results demonstrate that the deep leaning based receiver offers consistent improvement in performance compared to the traditional UWA OFDM receiver.
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3215326671