Capacity approximation of memoryless channels with countable output alphabets

We present a new algorithm, based on duality of convex programming and the specific structure of the channel capacity problem, to iteratively construct upper and lower bounds for the capacity of memoryless channels having continuous input and countable output alphabets. Under a mild assumption on the decay rate of the channel's tail, explicit bounds for the approximation error are provided. We demonstrate the applicability of our result on the discrete-time Poisson channel having a peak-power input constraint.