On the relationship between edge removal and strong converses

This paper explores the relationship between two ideas in network information theory: edge removal and strong converses. Edge removal properties state that if an edge of small capacity is removed from a network, the capacity region does not change too much. Strong converses state that, for rates outside the capacity region, the probability of error converges to 1. Various notions of edge removal and strong converse are defined, depending on how edge capacity and residual error probability scale with blocklength, and relations between them are proved. In particular, each class of strong converse implies a specific class of edge removal. The opposite direction is proved for deterministic networks, and some discussion is given for the noisy case.

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