Strong Converse, Feedback Channel Capacity and Hypothesis Testing
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In light of recent results by Verdu ad Han on channel capacity, we examine three problems: the strong converse condition to the channel coding theorem, the capacity of arbitrary channels with feedback and the Neyman-Pearson hypothesis testing type-II error exponent. It is first remarked that the strong converse condition holds if and only is the sequence of normalized channel information densities converges in probability to a constant. Examples illustrating this condition are also provided. A general formula for the capacity of arbitrary channels with output feedback is then obtained. Finally, a general expression for the Neyman-Pearson type-II exponent based on arbitrary observations subject to a constant bound on the type-I error probability is derived.