Information Rates Over Multi-View Channels

TL;DR

This paper explores the fundamental limits of reliable communication over multi-view channels, demonstrating exponential convergence of capacity and dispersion to input entropy and varentropy, and introducing a new Poisson approximation channel model.

Contribution

It provides the first analysis of capacity and dispersion convergence in multi-view channels and introduces the Poisson approximation channel model for such systems.

Findings

Channel capacity converges exponentially fast to input entropy.

Dispersion converges exponentially fast to input varentropy.

Introduces the Poisson approximation channel model.

Abstract

We investigate the fundamental limits of reliable communication over multi-view channels, in which the channel output is comprised of a large number of independent noisy views of a transmitted symbol. We consider first the setting of multi-view discrete memoryless channels and then extend our results to general multi-view channels (using multi-letter formulas). We argue that the channel capacity and dispersion of such multi-view channels converge exponentially fast in the number of views to the entropy and varentropy of the input distribution, respectively. We identify the exact rate of convergence as the smallest Chernoff information between two conditional distributions of the output, conditioned on unequal inputs. For the special case of the deletion channel, we compute upper bounds on this Chernoff information. Finally, we present a new channel model we term the Poisson approximation channel -- of possible independent interest -- whose capacity closely approximates the capacity of the multi-view binary symmetric channel for any fixed number of views.