2009•IEEE Transactions on Information TheoryRequires access

On the Capacity of the Discrete-Time Poisson Channel

Amos Lapidoth, Stefan M. Moser

Open publisher page 156 citations

Abstract

The large-inputs asymptotic capacity of a peak-power and average-power limited discrete-time Poisson channel is derived using a new firm (nonasymptotic) lower bound and an asymptotic upper bound. The upper bound is based on the dual expression for channel capacity and the notion of capacity-achieving input distributions that escape to infinity. The lower bound is based on a lower bound on the entropy of a conditionally Poisson random variable in terms of the differential entropy of its conditional mean.

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What this paper is about

The large-inputs asymptotic capacity of a peak-power and average-power limited discrete-time Poisson channel is derived using a new firm (nonasymptotic) lower bound and an asymptotic upper bound. The upper bound is based on the dual expression for channel capacity and the notion of capacity-achieving input distributions that escape to infinity. The lower bound is based on a lower bound on the entropy of a conditionally Poisson random variable in terms of the differential entropy of its conditional mean.

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Available abstract

The large-inputs asymptotic capacity of a peak-power and average-power limited discrete-time Poisson channel is derived using a new firm (nonasymptotic) lower bound and an asymptotic upper bound. The upper bound is based on the dual expression for channel capacity and the notion of capacity-achieving input distributions that escape to infinity. The lower bound is based on a lower bound on the entropy of a conditionally Poisson random variable in terms of the differential entropy of its conditional mean.

Key concepts: Upper and lower bounds, Mathematics, Channel capacity, Poisson distribution, Differential entropy, Entropy (arrow of time), Random variable, Conditional entropy

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