State Estimation over Broadcast and Multi-Access Channels in an Unreliable Regime

TL;DR

This paper investigates the fundamental limits of state estimation over unreliable broadcast and multi-access channels, introducing novel semantic metrics to optimize encoding and decoding strategies for networked systems.

Contribution

It introduces two new semantic metrics—dissemination and prioritization values—and characterizes optimal strategies for state estimation over unreliable multi-terminal channels.

Findings

Identifies fundamental performance limits in estimation error and communication cost.

Introduces dissemination and prioritization metrics for information valuation.

Shows optimal strategies depend on these semantic metrics.

Abstract

This article examines the problem of state estimation over multi-terminal channels in an unreliable regime. More specifically, we consider two canonical settings. In the first setting, measurements of a common stochastic source need to be transmitted to two distinct remote monitors over a packet-erasure broadcast channel. In the second setting, measurements of two distinct stochastic sources need to be transmitted to a common remote monitor over a packet-erasure multi-access channel. For these networked systems, we uncover the fundamental performance limits in the sense of a causal tradeoff between the estimation error and the communication cost by identifying optimal encoding and decoding strategies. In the course of our analysis, we introduce two novel semantic metrics that play essential roles in state estimation over broadcast and multi-access channels. The first metric arising in the context of broadcast channels is the dissemination value of information, which quantifies the valuation of provisioning a piece of information to multiple receivers simultaneously. The second metric arising in the context of multi-access channels is the prioritization value of information, which quantifies the valuation of provisioning a piece of information chosen from one out of multiple transmitters. Our findings certify that the optimal encoding and decoding strategies hinge on these semantic metrics.