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

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.