Scoring ISAC: Benchmarking Integrated Sensing and Communications via Score-Based Generative Modeling

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 score-based performance evaluation, with a focus on ISAC systems. We refer to the summarized framework as scoring ISAC, which not only reflects the core methodology based on score functions but also emphasizes the goal of scoring (i.e., evaluating) ISAC systems under realistic conditions. We present the connections between classical performance metrics and the score functions and provide the practical training techniques for learning score functions to estimate performance metrics. Proof-of-concept experiments on target detection and localization validate the accuracy of score-based performance estimators against ground-truth analytical expressions, illustrating their ability to replicate and extend traditional analyses in more complex, realistic settings. This framework demonstrates the great potential of score-based generative models in ISAC performance analysis, algorithm design, and system optimization.