On the Sum Rate of Gaussian Multiterminal Source Coding: New Proofs and Results

Abstract

We show that the lower bound on the sum rate of the direct and indirect Gaussian multiterminal source coding problems can be derived in a unified manner by exploiting the semidefinite partial order of the distortion covariance matrices associated with the minimum mean squared error (MMSE) estimation and the so-called reduced optimal linear estimation, through which an intimate connection between the lower bound and the Berger-Tung upper bound is revealed. We give a new proof of the minimum sum rate of the indirect Gaussian multiterminal source coding problem (i.e., the Gaussian CEO problem). For the direct Gaussian multiterminal source coding problem, we derive a general lower bound on the sum rate and establish a set of sufficient conditions under which the lower bound coincides with the Berger-Tung upper bound. We show that the sufficient conditions are satisfied for a class of sources and distortion constraints; in particular, they hold for arbitrary positive definite source covariance matrices in the high-resolution regime. In contrast with the existing proofs, the new method does not rely on Shannon's entropy power inequality.