Tight Exponential Strong Converses for Lossy Source Coding with Side-Information and Distributed Function Computation

The exponential strong converse for a coding problem states that, if a coding rate is beyond the theoretical limit, the correct probability converges to zero exponentially. For the lossy source coding with side-information, also known as the Wyner-Ziv (WZ) problem, a lower bound on the strong converse exponent was derived by Oohama. In this paper, we derive the tight strong converse exponent for the WZ problem; as a special case, we also derive the tight strong converse exponent for the distributed function computation problem. For the converse part, we use the change-of-measure argument developed in the literature and the soft Markov constraint introduced by Oohama; the matching achievability is proved via the Poisson matching approach recently introduced by Li and Anantharam. Our result is build upon the recently derived tight strong converse exponent for the Wyner-Ahlswede-Korner (WAK) problem; however, compared to the WAK problem, more sophisticated argument is needed. As an illustration of the necessity of the soft Markov constraint, we present an example such that the soft Markov constraint is strictly positive.