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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 as the blocklength goes to infinity. Various notions of edge removal and strong converse are defined, depending on how edge capacity and error probability scale with blocklength, and relations between them are proved. Each class of strong converse implies a specific class of edge removal. The opposite directions are proved for deterministic networks. Furthermore, a technique based on a novel, causal version of the blowing-up lemma is used to prove that for discrete memoryless networks, the weak edge removal property--that the capacity region changes continuously as the capacity of an edge vanishes--is equivalent to the exponentially strong converse--that outside the capacity region, the probability of error goes to 1 exponentially fast. This result is used to prove exponentially strong converses for several examples, including the discrete 2-user interference channel with strong interference, with only a small variation from traditional weak converse proofs.
The edge-removal problem asks whether the removal of a $lambda$-capacity edge from a given network can decrease the communication rate between source-terminal pairs by more than $lambda$. In this short manuscript, we prove that for undirected network
We study the infimum of the best constant in a functional inequality, the Brascamp-Lieb-like inequality, over auxiliary measures within a neighborhood of a product distribution. In the finite alphabet and the Gaussian cases, such an infimum converges
The edge removal problem studies the loss in network coding rates that results when a network communication edge is removed from a given network. It is known, for example, that in networks restricted to linear coding schemes and networks restricted t
Improved lower bounds on the average and the worst-case rate-memory tradeoffs for the Maddah-Ali&Niesen coded caching scenario are presented. For any number of users and files and for arbitrary cache sizes, the multiplicative gap between the exact ra
The sparsity and compressibility of finite-dimensional signals are of great interest in fields such as compressed sensing. The notion of compressibility is also extended to infinite sequences of i.i.d. or ergodic random variables based on the observe