Multiple Packing: Lower Bounds via Error Exponents

TL;DR

This paper establishes lower bounds on the maximal rates of multiple packings in high-dimensional Euclidean spaces, linking error exponents from coding theory to geometric packing problems, and extends understanding of list-decodable codes.

Contribution

It introduces new lower bounds on multiple packing densities by connecting list-decoding error exponents with geometric packing bounds, a novel approach in high-dimensional geometry.

Findings

Derived the best known lower bounds on multiple packing density.

Established a new inequality relating error exponents to list-decoding radius.

Provided bounds on list-decoding error exponents in various settings.

Abstract

We derive lower bounds on the maximal rates for multiple packings in high-dimensional Euclidean spaces. Multiple packing is a natural generalization of the sphere packing problem. For any $ N>0 $ and $ L\in\mathbb{Z}_{\ge2} $, a multiple packing is a set $\mathcal{C}$ of points in $ \mathbb{R}^n $ such that any point in $ \mathbb{R}^n $ lies in the intersection of at most $ L-1 $ balls of radius $ \sqrt{nN} $ around points in $ \mathcal{C} $. We study this problem for both bounded point sets whose points have norm at most $\sqrt{nP}$ for some constant $P>0$ and unbounded point sets whose points are allowed to be anywhere in $ \mathbb{R}^n $. Given a well-known connection with coding theory, multiple packings can be viewed as the Euclidean analog of list-decodable codes, which are well-studied for finite fields. We derive the best known lower bounds on the optimal multiple packing density. This is accomplished by establishing a curious inequality which relates the list-decoding error exponent for additive white Gaussian noise channels, a quantity of average-case nature, to the list-decoding radius, a quantity of worst-case nature. We also derive various bounds on the list-decoding error exponent in both bounded and unbounded settings which are of independent interest beyond multiple packing.