Deterministic Annealing-Based Optimization for Zero-Delay Source-Channel Coding in Networks

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 nonconvex 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.