The maximum mutual information between the output of a binary symmetric channel and a Boolean function of its input

TL;DR

This paper proves the Courtade-Kumar conjecture, establishing an upper bound on the mutual information between any Boolean function of a BSC input and its output, for all input sizes and error probabilities, using fundamental information theory concepts.

Contribution

The paper provides a complete proof of the Courtade-Kumar conjecture for any Boolean function and error probability, employing basic information theory techniques.

Findings

Mutual information is bounded by 1-H(p) for all Boolean functions.

The proof applies to any input size n and error probability p.

The result confirms the maximum mutual information achievable.

Abstract

We prove the Courtade-Kumar conjecture, which states that the mutual information between any Boolean function of an $n$-dimensional vector of independent and identically distributed inputs to a memoryless binary symmetric channel and the corresponding vector of outputs is upper-bounded by $1-\operatorname{H}(p)$, where $\operatorname{H}(p)$ represents the binary entropy function. That is, let $\mathbf{X}=[X_1...X_n]$ be a vector of independent and identically distributed Bernoulli($1/2$) random variables, which are the input to a memoryless binary symmetric channel, with the error probability equal to $0 \leq p \leq 1/2$, and $\mathbf{Y}=[Y_1...Y_n]$ the corresponding output. Let $f:\{0,1\}^n \rightarrow \{0,1\}$ be an $n$-dimensional Boolean function. Then, $\operatorname{MI}(f(\mathbf{X}),\mathbf{Y}) \leq 1-\operatorname{H}(p)$. We provide the proof for the most general case of the conjecture, that is for any $n$-dimensional Boolean function $f$ and for any value of the error probability of the binary symmetric channel, $0 \leq p \leq 1/2$. Our proof employs only basic concepts from information theory, probability theory and transformations of random variables and vectors.