On Large Scale Distributed Compression and Dispersive Information Routing for Networks

TL;DR

This paper introduces a scalable decoding approach for large-scale distributed source coding that controls complexity through a novel bit-subset selector, optimizing the trade-off between complexity and distortion, with applications to dispersive information routing.

Contribution

It proposes a new decoding paradigm with a bit-subset selector and a DA-based design strategy, enabling scalable distributed compression and efficient dispersive routing in large networks.

Findings

Significant reduction in decoder complexity while maintaining low distortion.

Effective joint design of system components via deterministic annealing.

Substantial performance gains over traditional distributed coding methods.

Abstract

This paper considers the problem of distributed source coding for a large network. A major obstacle that poses an existential threat to practical deployment of conventional approaches to distributed coding is the exponential growth of the decoder complexity with the number of sources and the encoding rates. This growth in complexity renders many traditional approaches impractical even for moderately sized networks. In this paper, we propose a new decoding paradigm for large scale distributed compression wherein the decoder complexity is explicitly controlled during the design. Central to our approach is a module called the "bit-subset selector" whose role is to judiciously extract an appropriate subset of the received bits for decoding per individual source. We propose a practical design strategy, based on deterministic annealing (DA) for the joint design of the system components, that enables direct optimization of the decoder complexity-distortion trade-off, and thereby the desired scalability. We also point out the direct connections between the problem of large scale distributed compression and a related problem in sensor networks, namely, dispersive information routing of correlated sources. This allows us to extend the design principles proposed in the context of large scale distributed compression to design efficient routers for minimum cost communication of correlated sources across a network. Experiments on both real and synthetic data-sets provide evidence for substantial gains over conventional approaches.