Sharp Bounds for Mutual Covering

Abstract

A fundamental tool in network information theory is the covering lemma, which lower bounds the probability that there exists a pair of random variables, among a give number of independently generated candidates, falling within a given set. We use a weighted sum trick and Talagrand's concentration inequality to prove new mutual covering bounds. We identify two interesting applications: 1) When the probability of the set under the given joint distribution is bounded away from 0 and 1, the covering probability converges to 1 doubly exponentially fast in the blocklength, which implies that the covering lemma does not induce penalties on the error exponents in the applications to coding theorems. 2) Using Hall's marriage lemma, we show that the maximum difference between the probability of the set under the joint distribution and the covering probability equals half the minimum total variation distance between the joint distribution and any distribution that can be simulated by selecting a pair from the candidates. Thus we use the mutual covering bound to derive the exact error exponent in the joint distribution simulation problem. In both applications, the determination of the exact exponential (or doubly exponential) behavior relies crucially on the sharp concentration inequality used in the proof of the mutual covering lemma.Parts of the paper (mostly, Part 1) described in the abstract) were presented at ISIT 2017. 1 A single-shot formulation refers to the non-asymptotic setting where the random variables are not necessarily vectors with i.i.d. coordinates.Of course, the main contribution of the paper lies in the new arguments for bounding the covering and the sampling error, which are independent of the type of formulation we adopt.2 In contrast to the standard literature, we add the qualifier "unilateral" to differentiate the standard covering lemma with the mutual covering lemma. DRAFT