Super-Linear Growth of the Capacity-Achieving Input Support for the Amplitude-Constrained AWGN Channel

Fuente: arXiv
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Main Author: Wang, Haiyang
Format: Preprint
Published: 2025
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author Wang, Haiyang
author_facet Wang, Haiyang
contents We study the growth of the support size of the capacity-achieving input distribution for the amplitude-constrained additive white Gaussian noise (AWGN) channel. While it is known since Smith (1971) that the optimal input is discrete with finitely many mass points, tight bounds on the number of support points $K_A$ as the amplitude constraint $A$ increases remain open. Not much is known until recently, when Dytso et al. (2019) proved that $K_A$ grows at least linearly and at most quadratically in $A$. Here, we provide a novel method, building on Ma et al. (2024); Zhang (1994), to derive the first non-trivial lower bound showing that KA grows super-linearly in A.
format Preprint
id arxiv_https___arxiv_org_abs_2510_20723
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Super-Linear Growth of the Capacity-Achieving Input Support for the Amplitude-Constrained AWGN Channel
Wang, Haiyang
Information Theory
We study the growth of the support size of the capacity-achieving input distribution for the amplitude-constrained additive white Gaussian noise (AWGN) channel. While it is known since Smith (1971) that the optimal input is discrete with finitely many mass points, tight bounds on the number of support points $K_A$ as the amplitude constraint $A$ increases remain open. Not much is known until recently, when Dytso et al. (2019) proved that $K_A$ grows at least linearly and at most quadratically in $A$. Here, we provide a novel method, building on Ma et al. (2024); Zhang (1994), to derive the first non-trivial lower bound showing that KA grows super-linearly in A.
title Super-Linear Growth of the Capacity-Achieving Input Support for the Amplitude-Constrained AWGN Channel
topic Information Theory
url https://arxiv.org/abs/2510.20723