Deep Joint Source-Channel Coding and Modulation for Underwater Acoustic Communication
Deep Joint Source-Channel Coding and Modulation for Underwater Acoustic Communication
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DOI：
10.1109/globecom46510.2021.9685931
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Yoshiaki Inoue;D. Hisano;K. Maruta;Yuko Hara-Azumi;Yu Nakayama
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文献类型：
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作者：
Yoshiaki Inoue;D. Hisano;K. Maruta;Yuko Hara-Azumi;Yu Nakayama
水下通信是一种很有前途的技术，它以声波为主要载波进行远距离通信，提供泛在网络连接。水声通信因其固有的带宽窄、信号衰减大、多径传播时变、传播速度慢等特点，如何有效地传输图像一直是一个具有挑战性的研究课题。在本文中，我们提出了一种新的方法来解决UAC中的这些限制，即基于深度神经网络(DNN)的联合信源-信道编码和调制(JSCCM)。我们提出了一种基于DNN的编解码器训练方法，不同于传统的基于分离的信源和信道编码和调制，该方法直接将图像像素值编码/解码为调制符号。通过数值仿真，证实了深JSCCM方案比传统方案具有更高的数据传输速率。
Underwater communication is a promising technology to provide ubiquitous network connectivity, where acoustic waves are used as the primary carrier for long-range communication. It has been a challenging research topic to efficiently transmit images with under-water acoustic communication (UAC), due to its inherently narrow bandwidth, strong signal attenuation, time-varying multipath propagation, and low propagation speed. In this paper, we present a new approach to addressing these limitations in UAC, namely the joint source-channel coding and modulation (JSCCM) based on a deep neural network (DNN). We develop a training method of DNN-based encoder and decoder, which directly encode/decode image-pixel values to modulated symbols, unlike conventional separation-based source and channel coding and modulation. Through numerical simulations, the deep JSCCM is confirmed to achieve significantly higher data-rate than conventional schemes.