Support Size of $\varepsilon$-Capacity-Achieving Inputs for the Amplitude-Constrained AWGN Channel

TL;DR

This paper investigates the minimal support size of near-capacity achieving input distributions for the amplitude-constrained AWGN channel, providing sharp bounds and a unified framework across different approximation regimes.

Contribution

It introduces the quantity $K_ ext{ε}(A)$ to quantify support size for ε-close capacity and characterizes its scaling laws in various regimes, connecting approximation theory with information theory.

Findings

For polynomially decaying ε, support size scales as Θ(A√log A).

For exponentially small ε, bounds range between A√log A and A^{3/2}.

Framework explains prior numerical observations as different ε-regimes.

Abstract

We study the amplitude-constrained additive white Gaussian noise (AWGN) channel from the perspective of near-optimal input distributions. While it is known that the capacity-achieving input is discrete with finitely many mass points, the precise scaling of its support size as a function of the amplitude constraint remains an open problem. In this work, we instead consider the minimal support size required to achieve capacity up to an $\varepsilon$-gap. We introduce the quantity $K_\varepsilon(A)$, defined as the smallest support size among discrete inputs supported on $[-A,A]$ that achieves mutual information within $\varepsilon$ of capacity. We show that this relaxed formulation is significantly more tractable and admits sharp characterizations across different regimes of $\varepsilon$. In particular, when $\varepsilon$ decays polynomially with $A$, i.e., $\varepsilon = A^{-\beta}$ for $\beta \geq 1$, we establish that $K_\varepsilon(A) = \Theta(A\sqrt{\log A})$. For exponentially small gaps, we obtain bounds of order between $A\sqrt{\log A}$ and $A^{3/2}$. Our approach combines approximation-theoretic bounds for Gaussian mixtures with information-theoretic control of entropy via $\chi^2$-divergence, together with a wrapping argument that relates the problem to approximating the uniform distribution on the circle. Beyond the technical results, our framework provides a conceptual explanation for the variety of scaling laws observed in prior numerical studies, showing that these correspond to different regimes of $\varepsilon$-optimality rather than intrinsic properties of the exact optimizer.