Quantization for Robust Distributed Coding

Abstract

A distributed source coding approach is proposed for robust data communications in sensor networks. When sensor measurements are quantized, possible correlations between the measurements can be exploited to reduce the overall rate of communication required to report these measurements. Robust distributed source coding (RDSC) approaches differentiate themselves from other works in that the reconstruction error of all sources will not exceed a given upper bound, even if only a subset of the multiple descriptions of the distributed source code are received. We deal with practical aspects of RDSC in the context of scalar quantization of two correlated sources. As a benchmark to evaluate the performance of the proposed scheme, we derive theoretically achievable distortion-rate performances of an RDSC for two jointly Gaussian sources by applying known results on the classical multiple description source coding.

Lattice-Based Robust Distributed Source Coding

“…[14] and Chen and Berger [15]. The design of practical schemes was addressed in [16]- [18], where iterative algorithms were employed for locally optimal designs. On the other hand, the work of Heegard and Berger [19] considers The the robust version of the Wyner-Ziv problem and provides a characterization of the rate-distortion region.…”

“…[14] and Chen and Berger [15]. The design of practical schemes was addressed in [16]- [18], where iterative algorithms were employed for locally optimal designs. On the other hand, the work of Heegard and Berger [19] considers…”