Distributed Quantization Networks

TL;DR

This paper demonstrates that low-rate intersensor communication, or chatting, significantly enhances distributed source coding performance, especially for nonlinear computations, by providing an asymptotic analysis and optimal design framework.

Contribution

It introduces the DFSQ framework for analyzing and designing distributed quantization with chatting, revealing substantial performance gains in high-resolution regimes.

Findings

Chatting dramatically improves distortion performance.

Optimal rate allocation enhances efficiency in heterogeneous networks.

Theoretical analysis aligns with practical examples demonstrating gains.

Abstract

Several key results in distributed source coding offer the intuition that little improvement in compression can be gained from intersensor communication when the information is coded in long blocks. However, when sensors are restricted to code their observations in small blocks (e.g., 1), intelligent collaboration between sensors can greatly reduce distortion. For networks where sensors are allowed to "chat" using a side channel that is unobservable at the fusion center, we provide asymptotically-exact characterization of distortion performance and optimal quantizer design in the high-resolution (low-distortion) regime using a framework called distributed functional scalar quantization (DFSQ). The key result is that chatting can dramatically improve performance even when intersensor communication is at very low rate, especially if the fusion center desires fidelity of a nonlinear computation applied to source realizations rather than fidelity in representing the sources themselves. We also solve the rate allocation problem when communication links have heterogeneous costs and provide a detailed example to demonstrate the theoretical and practical gains from chatting. This example for maximum computation gives insight on the gap between chatting and distributed networks, and how to optimize the intersensor communication.