Integrated Communication and Bayesian Estimation of Fixed Channel States

TL;DR

This paper analyzes the fundamental tradeoff between information rate and estimation accuracy in integrated sensing and communication systems using the asymptotically tight Bayesian Cramér-Rao lower bound, applicable to general channels.

Contribution

It introduces a novel characterization of the rate-MSE tradeoff using ATBCRB analysis, applicable to general channels under regularity conditions.

Findings

Derived a full tradeoff characterization between information rate and MSE.

Validated the theoretical results with a numerical example in spectrum sensing.

Demonstrated the applicability of the approach to general channels.

Abstract

This work studies an information-theoretic performance limit of an integrated sensing and communication (ISAC) system where the goal of sensing is to estimate a random continuous state. Considering the mean-squared error (MSE) for estimation performance metric, the Bayesian Cram\'{e}r-Rao lower bound (BCRB) is widely used in literature as a proxy of the MSE; however, the BCRB is not generally tight even asymptotically except for restrictive distributions. Instead, we characterize the full tradeoff between information rate and the exact MSE using the asymptotically tight BCRB (ATBCRB) analysis, a recent variant of the BCRB. Our characterization is applicable for general channels as long as the regularity conditions are met, and the proof relies on constant composition codes and ATBCRB analysis with the codes. We also perform a numerical evaluation of the tradeoff in a variance estimation example, which commonly arises in spectrum sensing scenarios.