In this paper, we introduce a framework for Joint Source-Channel Coding of distributed Gaussian sources over a multiple access AWGN channel. Although there are prior works that have studied this, they either strongly rely on intuition to design encoders and decoder or require the knowledge of the complete joint distribution of all the distributed sources. Our system overcomes this. We model our system as a Variational Autoencoder and leverage insight provided by this connection to propose a crucial regularization mechanism for learning. This allows us to beat the state of the art by improving the signal reconstruction quality by almost 1dB for certain configurations. The end-to-end learned system is also found to be robust to channel condition variations of ±5dB and shows a drop in signal reconstruction quality by at most 1dB. Finally, we propose a novel lower bound on the optimal distortion in signal reconstruction and empirically showcase the tightness of the bound in comparison with the existing bound.

/pdf/vae-for-joint-source-channel-coding-of-distributed-gaussian-2mq2zqw1yu.pdf

VAE for Joint Source-Channel Coding of Distributed Gaussian Sources over AWGN MAC

Adaptive controllers and signal processors play a key role in dealing with parameter uncertainties. This paper proposes an adaptive and new information theoretic algorithm for secure and optimal source-coding. We optimise the volume of the achievable rate-distortion-equivocation region by the private Helen’s rate (HR), defining a stochastic mean-field game (MFG). The aforementioned stochasticity deals with the additional uncoded side-information (SI) at the encoder-decoder, or even possibly-decoded SI at the eavesdropper (Eve). The stochastic partial derivative equations (SPDEs), namely the Hamilton-Jacobi-Bellman (HJB) and the Fokker-Planck-Kolmogorov (FPK) are presented, being solved by a discretised Lagrangian. We explore the Information-flow over the resultant Riemann-Sphere and our proposed SMFG’s stability from a many-body-theoretic perspective. We also show that while the equivocation (uncertainty) rate is $\Delta \le min \{\mathcal {H}(\mathcal {X}),\mathcal {R}_{h} \}$ , $\mathcal {I}(\mathcal {Y};\mathcal {Z}|\mathcal {W})$ which is upper-bounded to $min\{ \mathcal {I}(\mathcal {X};\mathcal {Y}),\mathcal {I}(\mathcal {Y};\mathcal {Z}|\mathcal {W}) \}$ , versus $\mathcal {I}(\mathcal {X};\mathcal {Z}|\mathcal {W})$ theoretically converges to the information-Bottleneck-bound $\mathcal {H}(\mathcal {X})$ . Simulation results also show an out-performance of our scheme over the existing work, proving the SMFG’s stability and an adequate distance to Pareto-Optimal sets . Our generic solution covers a comprehensive field of studies determining smooth non-linearity .

/pdf/a-novel-stable-stochastic-mean-field-game-for-lossy-source-3irrmk3bbc.pdf

A Novel & Stable Stochastic-Mean-Field-Game for Lossy Source-Coding Paradigms: A Many-Body-Theoretic Perspective

A conjectural expression of the asymptotic gap between the rate-distortion function of an arbitrary generalized Gaussian multiterminal source coding system and that of its centralized counterpart in the high-resolution regime is proposed. The validity of this expression is verified when the number of sources is no more than 3.

/pdf/generalized-gaussian-multiterminal-source-coding-in-the-high-4g16ont8cq.pdf

Generalized Gaussian Multiterminal Source Coding in the High-Resolution Regime

This book, composed of the collection of papers that have appeared in the Special Issue of theEntropy journal dedicated to “Information Theory for Data Communications and Processing”,reflects, in its eleven chapters, novel contributions based on the firm basic grounds of informationtheory. The book chapters [1–11] address timely theoretical and practical aspects that carry bothinteresting and relevant theoretical contributions, as well as direct implications for modern currentand future communications systems. [...]

https://www.mdpi.com/1099-4300/22/11/1250/pdf

Information Theory for Data Communications and Processing.

Consider a symmetric multivariate Gaussian source with l components, which are corrupted by independent and identically distributed Gaussian noises; these noisy components are compressed at a certain rate, and the compressed version is leveraged to reconstruct the source subject to a mean squared error distortion constraint. The rate-distortion analysis is performed for two scenarios: centralized encoding (where the noisy source components are jointly compressed) and distributed encoding (where the noisy source components are separately compressed). It is shown, among other things, that the gap between the rate-distortion functions associated with these two scenarios admits a simple characterization in the large l limit.

https://www.mdpi.com/1099-4300/21/2/213/pdf

Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding.

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Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding.