Adaptive Deep Joint Source-Channel Coding for One-to-Many Wireless Image Transmission

IF 4.8 1区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Lei Luo;Ziyang He;Junjie Wu;Hongwei Guo;Ce Zhu
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引用次数: 0

Abstract

Deep learning based joint source-channel coding (DJSCC) has recently made significant progress and emerged as a potential solution for future wireless communication. However, there are still several crucial issues that necessitate further in-depth exploration to enhance the efficiency of DJSCC, such as channel quality adaptability, bandwidth adaptability, and the delicate balance between efficiency and complexity. This work proposes an adaptive deep joint source-channel coding scheme tailored for one-to-many wireless image transmission scenarios (ADMIT). First, to effectively improve transmission performance, neighboring attention is introduced as the backbone for the proposed ADMIT method. Second, a channel quality adaptive module (CQAM) is designed based on multi-scale feature fusion, which seamlessly adapts to fluctuating channel conditions across a wide range of channel signal-to-noise ratios (CSNRs). Third, to be precisely tailored to different bandwidth resources, the channel gained adaptive module (CGAM) dynamically adjusts the significance of individual channels within the latent space, which ensures seamless varying bandwidth accommodation with a single model through bandwidth adaptation and symbol completion. Additionally, to mitigate the imbalance of loss across multiple bandwidth ratios during the training process, the gradient normalization (GradNorm) based training strategy is leveraged to ensure adaptive balancing of loss decreasing. The extensive experimental results demonstrate that the proposed method significantly enhances transmission performance while maintaining relatively low computational complexity. The source codes are available at: https://github.com/llsurreal919/ADMIT.
一对多无线图像传输的自适应深度联合源信道编码
基于深度学习的联合源信道编码(DJSCC)最近取得了重大进展,并成为未来无线通信的潜在解决方案。然而,要提高DJSCC的效率,仍有几个关键问题需要进一步深入探讨,如信道质量适应性、带宽适应性以及效率与复杂性之间的微妙平衡。本文提出了一种适合一对多无线图像传输场景(ADMIT)的自适应深度联合源信道编码方案。首先,为了有效提高传输性能,在提出的ADMIT方法中引入了相邻注意作为主干。其次,设计了基于多尺度特征融合的信道质量自适应模块(CQAM),该模块能够无缝适应大范围信道信噪比(CSNRs)波动的信道条件。第三,为了精确定制不同的带宽资源,信道增益自适应模块(CGAM)在潜在空间内动态调整单个信道的显著性,通过带宽自适应和符号补全,保证了单一模型对不同带宽的无缝适应。此外,为了减轻训练过程中多个带宽比损失的不平衡,利用基于梯度归一化(GradNorm)的训练策略来确保损失减少的自适应平衡。大量的实验结果表明,该方法在保持较低的计算复杂度的同时显著提高了传输性能。源代码可从https://github.com/llsurreal919/ADMIT获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Broadcasting
IEEE Transactions on Broadcasting 工程技术-电信学
CiteScore
9.40
自引率
31.10%
发文量
79
审稿时长
6-12 weeks
期刊介绍: The Society’s Field of Interest is “Devices, equipment, techniques and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.” In addition to this formal FOI statement, which is used to provide guidance to the Publications Committee in the selection of content, the AdCom has further resolved that “broadcast systems includes all aspects of transmission, propagation, and reception.”
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