Spectral Efficient Neural Network-Based M-ary Chirp Spread Spectrum Receivers for Underwater Acoustic Communication

IF 2.6 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Arabian Journal for Science and Engineering Pub Date : 2024-05-17 DOI:10.1007/s13369-024-08918-6
Songzuo Liu, Habib Hussain Zuberi, Zuhair Arfeen, Xuanye Zhang, Muhammad Bilal, Zongxin Sun
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Abstract

This article addresses the challenges encountered in underwater acoustic communication (UWAC) and presents a novel approach for chirp spread spectrum (CSS) communication. CSS is recognized for its ability to adjust to multipath and Doppler dispersion in underwater conditions, despite it usually demands a large bandwidth time product to achieve optimal performance. To address this constraint and improve data rate, the paper proposes a neural network-based receiver for spectral efficient M-ary CSS communication. M-ary communication is accomplished by transmitting chirps with different start and stop frequencies. At the receiver, a multilayer perceptron (MLP) artificial neural network and a one-dimensional convolutional neural network (1D CNN) are used for supervised classification. The neural network is trained offline using a comprehensive dataset developed by the BELLHOP ray tracing algorithm, which simulates various underwater acoustic channels. The application of VTRM pre-processing equalization aims to enhance performance. The simulation results illustrate the superior performance of the proposed receiver when compared to a conventional receiver based on a matched filter. The 16-ary chirp spread spectrum 1D CNN and MLP receivers show a gain of 6 and 4 dB, respectively, in a simulated channel after undergoing VTRM pre-processing. Furthermore, the utilization of a 16-ary 1D CNN receiver results in a noticeable 6 dB enhancement in two recorded channels. However, the MLP receiver outperforms the traditional receiver in terms of bit error rate. The article emphasizes the possibility of higher data rates and enhanced performance in underwater communication systems by employing the proposed M-ary CSS neural network-based method.

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用于水下声学通信的基于神经网络的高频谱效率 M-ary Chirp 扩频接收器
本文探讨了水下声学通信(UWAC)中遇到的挑战,并提出了一种啁啾扩频(CSS)通信的新方法。CSS 因其在水下条件下适应多径和多普勒频散的能力而得到认可,尽管它通常需要较大的带宽时间积才能达到最佳性能。为了解决这一限制并提高数据传输速率,本文提出了一种基于神经网络的接收器,用于频谱高效的 M-ary CSS 通信。M-ary 通信是通过发送起始和终止频率不同的啁啾来实现的。接收器采用多层感知器(MLP)人工神经网络和一维卷积神经网络(1D CNN)进行监督分类。神经网络使用 BELLHOP 射线跟踪算法开发的综合数据集进行离线训练,该算法模拟了各种水下声道。VTRM 预处理均衡的应用旨在提高性能。仿真结果表明,与基于匹配滤波器的传统接收器相比,所提出的接收器性能更优越。经过 VTRM 预处理后,16 级啁啾扩频 1D CNN 和 MLP 接收器在模拟信道中的增益分别为 6 和 4 dB。此外,利用 16 层 1D CNN 接收器在两个记录信道中明显增强了 6 分贝。不过,就误码率而言,MLP 接收器优于传统接收器。文章强调,通过采用所提出的基于 M-ary CSS 神经网络的方法,可以提高水下通信系统的数据传输速率和性能。
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来源期刊
Arabian Journal for Science and Engineering
Arabian Journal for Science and Engineering MULTIDISCIPLINARY SCIENCES-
CiteScore
5.70
自引率
3.40%
发文量
993
期刊介绍: King Fahd University of Petroleum & Minerals (KFUPM) partnered with Springer to publish the Arabian Journal for Science and Engineering (AJSE). AJSE, which has been published by KFUPM since 1975, is a recognized national, regional and international journal that provides a great opportunity for the dissemination of research advances from the Kingdom of Saudi Arabia, MENA and the world.
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