Analog Joint Source-Channel Coding for Distributed Functional Compression using Deep Neural Networks
Analog Joint Source-Channel Coding for Distributed Functional Compression using Deep Neural Networks
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DOI：
10.1109/isit45174.2021.9517797
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Yashas Malur Saidutta;A. Abdi;F. Fekri
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文献类型：
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作者：
Yashas Malur Saidutta;A. Abdi;F. Fekri
本文研究了在高斯多址(MAC)和AWGN信道上用于分布式模拟功能压缩的联合信源信道编码(JSCC)。值得注意的是，我们提出了一种基于深度神经网络的解决方案来学习编码器和解码器。我们提出了三种提高性能的方法。第一种方法将问题描述为一个自动编码器；第二种方法利用拉格朗日乘子在目标中加入功率约束；第三种方法从信息瓶颈原理推导出目标。我们证明了所有提出的方法都是对间接率失真问题最小化目标上界的变分逼近。进一步地，我们证明了第三种方法是比另外两种方法更紧的上界的变分近似。最后，我们给出了图像分类的实验结果。我们与现有的工作进行了比较，并展示了所提出的方法所产生的性能改进。
In this paper, we study Joint Source-Channel Coding (JSCC) for distributed analog functional compression over both Gaussian Multiple Access Channel (MAC) and AWGN channels. Notably, we propose a deep neural network based solution for learning encoders and decoders. We propose three methods of increasing performance. The first one frames the problem as an autoencoder; the second one incorporates the power constraint in the objective by using a Lagrange multiplier; the third method derives the objective from the information bottleneck principle. We show that all proposed methods are variational approximations to upper bounds on the indirect rate-distortion problem's minimization objective. Further, we show that the third method is the variational approximation of a tighter upper bound compared to the other two. Finally, we show empirical performance results for image classification. We compare with existing work and showcase the performance improvement yielded by the proposed methods.