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

2026-04-28

AI Summary

This work investigates whether causal and non-causal state information yield equivalent capacity regions in two-user networks where the receiver has full state knowledge and the transmitter is state-aware. Under the assumptions that the state sequence is stationary and ergodic and that the channel is memoryless, the authors develop a unified information-theoretic framework—leveraging ergodic theory—to analyze canonical multiuser channels, including multiple-access, broadcast, and interference channels. Remarkably, without explicitly computing the capacity regions, they establish for the first time that causal and non-causal state information achieve identical capacity regions in such networks. This result reveals a fundamental redundancy in the temporal availability of state information in multiuser settings and highlights the theoretical limits of state-utilization efficiency.

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.

Problem

• causal state information
• noncausal state information
• bipartite networks
• state-cognizant receivers
• capacity region

Innovation

• state-dependent networks
• causal vs noncausal state information
• capacity region equivalence
• state-cognizant receivers
• ergodic state