A unified approach for network information theory

In this paper, we take a unified approach for network information theory and prove a coding theorem, which can recover most of the achievability results in network information theory that are based on random coding. The final single-letter expression has a very simple form, which was made possible by treating sources, channels, states and side information in a unified way and by combining various constraints such as cost and distortion constraints as a single joint-typicality constraint. To demonstrate usefulness of our unified coding theorem, we show that a generalized decode-compress-amplify-and-forward bound can be obtained as a simple corollary of our theorem and show it strictly outperforms previously known coding schemes. Using our unified framework, we formally define and characterize three types of network duality based on channel input-output reversal and network flow reversal combined with packing-covering duality.