Scoring ISAC: Benchmarking Integrated Sensing and Communications via Score-Based Generative Modeling
Lin Chen, Chang Cai, Huiyuan Yang, Xiaojun Yuan, Ying-Jun Angela Zhang

TL;DR
This paper introduces a score-based generative modeling framework for evaluating integrated sensing and communications (ISAC) systems, enabling performance assessment under complex, real-world conditions where traditional metrics are hard to compute.
Contribution
It presents a novel score-based approach to estimate ISAC performance metrics, bridging classical theory and practical data-driven evaluation methods.
Findings
Score-based estimators accurately match analytical metrics in experiments.
Framework extends traditional analysis to complex, realistic scenarios.
Demonstrates potential for system optimization and design in ISAC.
Abstract
Integrated sensing and communications (ISAC) is a key enabler for next-generation wireless systems, aiming to support both high-throughput communication and high-accuracy environmental sensing using shared spectrum and hardware. Theoretical performance metrics, such as mutual information (MI), minimum mean squared error (MMSE), and Bayesian Cram\'{e}r--Rao bound (BCRB), play a key role in evaluating ISAC system performance limits. However, in practice, hardware impairments, multipath propagation, interference, and scene constraints often result in nonlinear, multimodal, and non-Gaussian distributions, making it challenging to derive these metrics analytically. Recently, there has been a growing interest in applying score-based generative models to characterize these metrics from data, although not discussed for ISAC. This paper provides a tutorial-style summary of recent advances in…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Radar Systems and Signal Processing · Direction-of-Arrival Estimation Techniques
