Error-Correcting Functional Index Codes, Generalized Exclusive Laws and Graph Coloring

TL;DR

This paper introduces a new framework for functional index coding that accounts for error correction, utilizing confusion graphs and generalized exclusive laws to optimize transmission efficiency in broadcast networks.

Contribution

It develops a novel approach to functional index coding with error correction, including bounds and conditions for optimal and error-resilient codes.

Findings

Bounds on optimal code size based on confusion graph parameters

Necessary and sufficient conditions for error-correcting functional index codes

Lower bounds on the length of error-correcting codes

Abstract

We consider the \emph{functional index coding problem} over an error-free broadcast network in which a source generates a set of messages and there are multiple receivers, each holding a set of functions of source messages in its cache, called the \emph{Has-set}, and demands to know another set of functions of messages, called the \emph{Want-set}. Cognizant of the receivers' \emph{Has-sets}, the source aims to satisfy the demands of each receiver by making coded transmissions, called a \emph{functional index code}. The objective is to minimize the number of such transmissions required. The restriction a receiver's demands pose on the code is represented via a constraint called the \emph{generalized exclusive law} and obtain a code using the \emph{confusion graph} constructed using these constraints. Bounds on the size of an optimal code based on the parameters of the confusion graph are presented. Next, we consider the case of erroneous transmissions and provide a necessary and sufficient condition that an FIC must satisfy for correct decoding of desired functions at each receiver and obtain a lower bound on the length of an error-correcting FIC.