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On Stochastic Fundamental Limits in a Downlink Integrated Sensing and Communication Network
Journal article   Open access   Peer reviewed

On Stochastic Fundamental Limits in a Downlink Integrated Sensing and Communication Network

Marziyeh Soltani, Mahtab Mirmohseni and Rahim Tafazolli
IEEE transactions on communications, Vol.73(11), pp.10436-10450
01/11/2025

Abstract

Array signal processing CRB Integrated sensing and communication Integrated sensing and communications Lower bound Measurement outage tradeoff performance analysis Probability density function randomness Rayleigh channels Rayleigh fading Signal to noise ratio Stochastic processes Vectors Radar
This paper aims to analyze the stochastic performance of a multiple input multiple output (MIMO) integrated sensing and communication (ISAC) system in a downlink scenario, where a base station (BS) transmits a dual-functional radar-communication (DFRC) signal matrix, serving the purpose of transmitting communication data to the user while simultaneously sensing the angular location of a target. The channel between the BS and the user is modeled as a random channel with Rayleigh fading distribution, and the azimuth angle of the target is assumed to follow a uniform distribution. Due to the randomness inherent in the network, the challenge is to consider suitable performance metrics for this randomness. To address this issue, for users, we employ the user's rate outage probability (OP) and ergodic rate, while for target, we propose using the OP of the Cramér-Rao lower bound (CRB) for the angle of arrival and the ergodic CRB. We have obtained the expressions of these metrics for scenarios where the BS employs two different beamforming methods. Our approach to deriving these metrics involves computing the probability density function (PDF) of the signal-to-noise ratio for users and the CRB for the target. We have demonstrated that the central limit theorem provides a viable approach for deriving these PDFs. In our numerical results, we demonstrate the trade-off between sensing and communication (S & C) by characterizing the region of S & C metrics and by obtaining the Pareto optimal boundary points, confirmed with simulations.
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