Data-Driven Signal Detection for Underwater Acoustic Filter Bank Multicarrier Communications

Author:

Zhu Yunan1ORCID,Wang Biao1ORCID,Xie Fangtong1ORCID,Wu Chengxi1ORCID,Chao Peng1ORCID

Affiliation:

1. Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212100, China

Abstract

By contraposing the signal detection for filter bank multicarrier (FBMC) communications with the underwater acoustic (UWA) channel, this paper analyzes the traditional imaginary interference problem and proposes a deep learning-based method. The neural network with feature extraction and automatic learning ability is employed to replace the demodulation modules to recover transmitted signals without explicit channel estimation and equalization. Sufficient data sets are generated according to the measured channel conditions in Qingjiang river, the optimization of network parameters is finished by constraining cost function in offline training, and the signal detection is carried out directly with the well-trained network in online testing. The system performance of various supervised learning models such as multilayer perceptron (MLP), convolutional neural network (CNN), and bidirectional long short-term memory (BLSTM) network is compared under different data sizes, network parameters, and prototype filters. The simulation results show that the bit error rate (BER) performance of the proposed signal detection is better than that of the classic one, which indicates that deep learning is a promising tool in UWA communication systems.

Funder

Postgraduate Research & Practice Innovation Program of Jiangsu Province

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

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