Third-order Analysis of Channel Coding in the Small-to-Moderate Deviations Regime
Recep Can Yavas, Victoria Kostina, and Michelle Effros

TL;DR
This paper introduces the concept of channel skewness to improve third-order approximations of channel coding limits in small-to-moderate deviations regimes, providing more accurate bounds for practical blocklengths.
Contribution
It generalizes existing asymptotic bounds to include the moderate deviations regime and introduces the channel skewness as a key factor in third-order analysis.
Findings
The channel skewness significantly improves approximation accuracy in the MD regime.
Exact computation of channel skewness for Gaussian channels using Shannon's bounds.
Enhanced bounds for binary symmetric channels in practical blocklength and error probability ranges.
Abstract
This paper studies the third-order characteristic of nonsingular discrete memoryless channels and the Gaussian channel with a maximal-power constraint. The third-order term in our expansions employs a new quantity here called the channel skewness, which affects the approximation accuracy more significantly as the error probability decreases. For the Gaussian channel, evaluating Shannon's 1959 random coding bound and Vazquez-Vilar's 2021 meta-converse bound in the central limit theorem (CLT) regime enables exact computation of the channel skewness. For discrete memoryless channels, this work generalizes Moulin's 2017 bounds on the asymptotic expansion of the maximum achievable message set size for nonsingular channels from the CLT regime to include the moderate deviations (MD) regime, thereby refining Altu\u{g} and Wagner's 2014 MD result. For an example binary symmetric channel and most…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWireless Communication Security Techniques · Machine Learning and Algorithms · Cryptography and Data Security
