Networked Multiple Description Estimation and Compression with Resource Scalability

TL;DR

This paper introduces a resource-scalable joint source-channel multiple description framework for sensor networks, enabling efficient distributed estimation and decoding with minimal encoder complexity and improved error correction.

Contribution

It proposes a novel JSC-MD framework that allows heterogeneous network nodes to perform MAP or MMSE decoding using the same MDQ, enhancing error correction and efficiency.

Findings

JSC-MD MAP estimator is a longest path algorithm in a weighted DAG.

JSC-MD MMSE decoder extends the forward-backward algorithm for multiple descriptions.

Outperforms existing MDQ decoders by up to 8dB in error correction.

Abstract

We present a joint source-channel multiple description (JSC-MD) framework for resource-constrained network communications (e.g., sensor networks), in which one or many deprived encoders communicate a Markov source against bit errors and erasure errors to many heterogeneous decoders, some powerful and some deprived. To keep the encoder complexity at minimum, the source is coded into K descriptions by a simple multiple description quantizer (MDQ) with neither entropy nor channel coding. The code diversity of MDQ and the path diversity of the network are exploited by decoders to correct transmission errors and improve coding efficiency. A key design objective is resource scalability: powerful nodes in the network can perform JSC-MD distributed estimation/decoding under the criteria of maximum a posteriori probability (MAP) or minimum mean-square error (MMSE), while primitive nodes resort to simpler MD decoding, all working with the same MDQ code. The application of JSC-MD to distributed estimation of hidden Markov models in a sensor network is demonstrated. The proposed JSC-MD MAP estimator is an algorithm of the longest path in a weighted directed acyclic graph, while the JSC-MD MMSE decoder is an extension of the well-known forward-backward algorithm to multiple descriptions. Both algorithms simultaneously exploit the source memory, the redundancy of the fixed-rate MDQ, and the inter-description correlations. They outperform the existing hard-decision MDQ decoders by large margins (up to 8dB). For Gaussian Markov sources, the complexity of JSC-MD distributed MAP sequence estimation can be made as low as that of typical single description Viterbi-type algorithms.