Broadcast Function Computation with Complementary Side Information

TL;DR

This paper investigates the optimal encoding rates for function computation in a three-node network with correlated sources and side information, establishing conditions for zero-error and epsilon-error scenarios.

Contribution

It introduces the first characterization of minimum encoding rates for function computation with complementary side information, including zero-error and epsilon-error cases, extending to index coding.

Findings

Zero-error rate equals epsilon-error rate for specific functions.

Cut-set bound is achievable under epsilon-error for binary sources and compatible functions.

Results generalize to a broader class of index coding problems.

Abstract

We consider the function computation problem in a three node network with one encoder and two decoders. The encoder has access to two correlated sources $X$ and $Y$. The encoder encodes $X^n$ and $Y^n$ into a message which is given to two decoders. Decoder 1 and decoder 2 have access to $X$ and $Y$ respectively, and they want to compute two functions $f(X,Y)$ and $g(X,Y)$ respectively using the encoded message and their respective side information. We want to find the optimum (minimum) encoding rate under the zero error and $\epsilon$-error (i.e. vanishing error) criteria. For the special case of this problem with $f(X,Y) = Y$ and $g(X,Y) = X$, we show that the $\epsilon$-error optimum rate is also achievable with zero error. This result extends to a more general `complementary delivery index coding' problem with arbitrary number of messages and decoders. For other functions, we show that the cut-set bound is achievable under $\epsilon$-error if $X$ and $Y$ are binary, or if the functions are from a special class of `compatible' functions which includes the case $f=g$.