Distributed Inference in Tree Networks using Coding Theory

TL;DR

This paper introduces coding theory techniques for distributed inference in tree networks, enabling efficient data compression and accurate global decision-making at the fusion center, with proven asymptotic optimality.

Contribution

It develops novel coding-based schemes for distributed classification and estimation in tree networks, optimizing data fusion and demonstrating asymptotic optimality.

Findings

Schemes are simple and practically implementable.

Proposed methods achieve asymptotic optimality under certain conditions.

Performance analyzed for fixed height and fixed degree tree networks.

Abstract

In this paper, we consider the problem of distributed inference in tree based networks. In the framework considered in this paper, distributed nodes make a 1-bit local decision regarding a phenomenon before sending it to the fusion center (FC) via intermediate nodes. We propose the use of coding theory based techniques to solve this distributed inference problem in such structures. Data is progressively compressed as it moves towards the FC. The FC makes the global inference after receiving data from intermediate nodes. Data fusion at nodes as well as at the FC is implemented via error correcting codes. In this context, we analyze the performance for a given code matrix and also design the optimal code matrices at every level of the tree. We address the problems of distributed classification and distributed estimation separately and develop schemes to perform these tasks in tree networks. The proposed schemes are of practical significance due to their simple structure. We study the asymptotic inference performance of our schemes for two different classes of tree networks: fixed height tree networks, and fixed degree tree networks. We show that the proposed schemes are asymptotically optimal under certain conditions.