Severe delay and resource (energy, bandwidth) constraints are typical in emerging communications and networking applications within a variety of scientific and engineering disciplines. This is particularly the case in highly constrained sensor networks. Such applications strongly motivate foregoing the convenience of digital communication and revisiting the potential benefits, and even necessity of analog communication, albeit in the modern context of distributed source-channel coding and routing. Major challenges emerge, including the derivation of a theoretical foundation for analog networking from estimation and information theoretic principles, specializing to a variety of subproblems and networking scenarios involving distributed source-channel coding, and developing effective practical algorithmic approaches, as well as a formidable optimization challenge due to the high complexity of the overall objective function which is literally riddled with poor local optima.
The general analog networking problem is formulated in terms of analog mappings, between source and channel variables at source nodes (encoders), between received and transmitted variables at intermediate network nodes, and between received and reconstruction variables at sink nodes (decoders). Research is pursued along several specific lines of investigation, including derivation of delay-constrained performance bounds and optimality conditions, optimal and low complexity algorithm design, global optimization techniques, analog mappings for distributed source-channel coding, analog multiple descriptions coding, extensions to sources and channels with memory, and optimal routing in an analog network. The effort builds on a body of preliminary work and results, including the necessary conditions for optimality of such mappings in simple settings, iterative algorithms to find locally optimal mappings, as well as a powerful non- convex optimization tool (deterministic annealing), which has been successfully employed in related optimization problems.