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Forward-Aware Information Bottleneck-Based Vector Quantization: Multiterminal Extensions for Parallel and Successive Retrieval

Shayan Hassanpour, Dirk Wübben, Armin Dekorsy

IEEE Transactions on Communications  99 (2021)

doi: 10.1109/TCOMM.2021.3097142

Consider the following setup: Through a joint design, multiple observations of a remote data source shall be locally compressed before getting transmitted via several error-prone , rate-limited forward links to a (distant) processing unit. For addressing this specific instance of multiterminal Joint Source-Channel Coding problem, in this article, the foundational principle of the Information Bottleneck method is fully extended to obtain purely statistical design approaches, enjoying the Mutual Information as their fidelity criterion. Specifically, the forms of stationary points for two types of distributed compression schemes are characterized here. Subsequently, those acquired solutions are utilized as the centerpiece of the proposed generic, iterative algorithm, termed the Multiterminal Forward-Aware Vector Information Bottleneck (M-FAVIB) , for addressing the design optimizations. Leveraging an unfolding trick, it will be proven that both distributed compression schemes fall into the category of Successive Upper-Bound Minimization , ensuring their convergence to a stationary point. Eventually, the effectiveness of the proposed compression schemes will be substantiated as well by means of numerical investigations over some typical transmission scenarios.


© The Authors 2021 licensed under CC BY 4.0

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