Identification for Colored Gaussian Channels

TL;DR

This paper investigates the identification capacity of discrete-time Gaussian channels with correlated noise and ISI, revealing super-exponential growth of codebook size and providing bounds on capacity based on noise spectrum and memory growth.

Contribution

It establishes that codebook size grows super-exponentially even with sub-linear ISI memory and characterizes bounds on identification capacity considering noise spectrum and memory parameters.

Findings

Codebook size grows as 2^{(n log n) R} with n.

Super-exponential growth persists despite sub-linear ISI memory.

Bounds on identification capacity depend on noise spectrum and ISI memory growth.

Abstract

We study the identification capacity of discrete-time Gaussian channels impaired by correlated noise and inter-symbol interference (ISI). Our analysis is formulated for deterministic encoding functions subject to a peak power constraint and colored noise whose covariance matrix features a polynomially bounded singular value spectrum, i.e., $\sim [n^{-\mu} , n^{\mu/2}]$ where $n$ is the codeword length and $\mu \in [0,1/2)$ is the spectrum rate. A central result establishes that, even when the ISI memory length grows sub-linearly with $n,$ i.e., $\sim n^{\kappa}$ where $\kappa \in [0,1/2)$ and $\kappa + \mu \in [0,1/2),$ the codebook size continues to exhibit super-exponential growth in $n$, i.e., $\sim 2^{(n \log n)R},$ with $R$ representing the associated coding rate. Moreover, by employing the well-known Mahalanobis-distance decoder induced by colored Gaussian noise statistics, we characterize bounds on the identification capacity, with the resulting bounds parameterized by $\kappa$ and $\mu.$