Unified Evaluation Methodology for AI-Native Integrated Sensing and Communication

2026-07-16

AI Summary

This work addresses the absence of a unified evaluation framework in existing AI-native integrated sensing and communication (ISAC) systems, which hinders end-to-end trade-offs among sensing accuracy, communication reliability, and resource overhead. The authors propose a unified architecture and assessment methodology for AI-native ISAC, establishing—for the first time—a minimal reporting checklist that links technical key performance indicators (KPIs) to application-level key value indicators (KVIs). They further develop comparable baselines tailored to heterogeneous scenarios, including outdoor UAV and indoor reconfigurable intelligent surface (RIS) deployments. Through a three-stage closed-loop pipeline encompassing theoretical bound analysis, high-fidelity digital twin simulation, and over-the-air preliminary validation—augmented by online learning under uncertainty and multidimensional performance correlation analysis—the study demonstrates reproducible evaluations in two representative setups, effectively bridging the gap between theoretical gains and real-world performance.

Abstract

Integrated Sensing and Communication (ISAC) couples radio sensing, data transmission, and control actions within a single closed-loop system. When Artificial Intelligence (AI)-driven policies adapt sensing and communication online across a variety of sensing tasks and objectives, end-to-end performance is shaped not only by waveform and channel conditions but also by inference latency, uncertainty, environmental dynamics, and hardware non-idealities, leading to fundamental trade-offs between sensing accuracy, communication reliability, and resource overhead. This manuscript presents a unified system architecture and evaluation methodology for AI-native ISAC, defined as ISAC in which learning-based agents adapt sensing, communication, and actuation policies online under uncertainty. We formalize the design space of closed-loop ISAC, propose a three-stage validation pipeline from bounds and feasibility analysis, through high-fidelity digital-twin simulation, to preliminary over-the-air validation, and provide a minimal reporting checklist that links technical Key Performance Indicators (KPIs) (e.g., data rate, SINR, target detection, parameter estimation, track quality, localization error, outage, latency, overhead, and energy per decision) to application-level Key Value Indicators (KVIs) (e.g., availability and mission effectiveness). Two representative instantiations, specifically Unmanned Aerial Vehicle (UAV)-based outdoor and Reconfigurable Intelligent Surface (RIS)-enabled indoor coverage extensions, are used to illustrate how to structure reproducible baselines and comparable evidence across heterogeneous deployments, helping bridge the gap between theoretical ISAC gains and deployment-ready performance claims.

Problem

• Integrated Sensing and Communication
• AI-native
• evaluation methodology
• closed-loop system
• performance trade-offs

Innovation

• AI-Native ISAC
• closed-loop sensing and communication
• unified evaluation methodology
• digital-twin simulation
• KPI-to-KVI mapping