  This paper studies the problem of global optimization of zero-delay
source-channel codes that map between the source space and the channel space,
under a given transmission power constraint and for the mean square error
distortion. Particularly, we focus on two well known network settings: the
Wyner-Ziv setting where only a decoder has access to side information and the
distributed setting where independent encoders transmit over independent
channels to a central decoder. Prior work derived the necessary conditions for
optimality of the encoder and decoder mappings, along with a greedy
optimization algorithm that imposes these conditions iteratively, in
conjunction with the heuristic noisy channel relaxation method to mitigate poor
local minima. While noisy channel relaxation is arguably effective in simple
settings, it fails to provide accurate global optimization in more complicated
settings considered in this paper. We propose a powerful non-convex
optimization method based on the concept of deterministic annealing -- which is
derived from information theoretic principles and was successfully employed in
several problems including vector quantization, classification and regression.
We present comparative numerical results that show strict superiority of the
proposed method over greedy optimization methods as well as prior approaches in
literature.
