Analog Joint Source-Channel Coding for Distributed Functional Compression using Deep Neural Networks

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 increa...

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Vydané v:2021 IEEE International Symposium on Information Theory (ISIT) s. 2429 - 2434
Hlavní autori: Saidutta, Yashas Malur, Abdi, Afshin, Fekri, Faramarz
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 12.07.2021
Predmet:
AWGN channels
Deep learning
Image coding
Minimization
Rate-distortion
Training
Upper bound
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Shrnutí: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.
DOI:10.1109/ISIT45174.2021.9517797