Volume-Refined Achievability and Converse Approximations for Noisy Permutation Channels

Abstract

We study volume-refined achievability and converse bounds for noisy permutation channels generated by strictly positive DMCs, allowing the reachable output polytope to have arbitrary affine dimension $d\ge 1$. The reachable output polytope may be lower-dimensional than the output simplex, whereas existing refined achievability analyses and fixed-error converses are not adapted to this intrinsic affine geometry. On the achievability side, we develop an affine-coordinate simplex-lattice construction adapted to the reachable output polytope, together with a nearest-neighbor decoder and a geometric error-reduction argument in the same coordinate space. This yields a Gaussian achievability approximation with an $o(1)$ remainder. On the converse side, we first use a meta-converse combined with a KL covering and a local testing estimate to obtain a fixed-error converse with a bounded remainder, which implies the logarithmic $\epsilon$-capacity $d/2$. We then apply the meta-converse with a stratified Jeffreys-mixture auxiliary output distribution. Using a local Laplace approximation and a local likelihood-ratio approximation, this choice identifies the Fisher-volume term and an explicit Gaussian testing constant, yielding a constant-order converse approximation with an $o(1)$ remainder. The achievability and converse constants arise from different constructions and are not claimed to match in general.