The Equivalence of Causal and Noncausal State Information on Bipartite Networks With State-Cognizant Receivers

TL;DR

This paper proves that for certain bipartite networks with state-aware components, the capacity region remains unchanged whether the state information is provided causally or noncausally, under specific conditions.

Contribution

It establishes the equivalence of causal and noncausal state information in the capacity analysis of bipartite networks with state-cognizant receivers.

Findings

Capacity region is unaffected by causal or noncausal state information under ergodic and memoryless conditions.

Networks studied include multi-access, broadcast, and interference channels.

The result simplifies capacity analysis by removing the need to distinguish between causal and noncausal information.

Abstract

State-dependent bipartite networks with state-cognizant receivers and state-informed transmitters are studied. Such networks have no nodes that both transmit and receive. Examples are the multi-access channel, the broadcast channel, and the interference channel. Without computing the capacity region of the network, it is shown that if the state sequence is ergodic and autonomous, and if, conditionally on the state sequence, the network law is memoryless, then the network capacity region does not depend on whether the state information is provided to the encoders causally or noncausally.