Unified Evaluation Methodology for AI-Native Integrated Sensing and Communication

Filip Lemic, Andra Blaga, Francesco Devoti, Guillermo Encinas Lago, Jan Adler, Amitha Mayya, Padmanava Sen, Giorgos Stratidakis, Sotiris Droulias, Angeliki Alexiou, Alexander Artemenko, Aya Mostafa Ahmed, Visa Koivunen, Robin Rajamäki, Simon Schütze, Robert Elschner, Amélie Hennequart, Ahmad Shoukair, Youssef Nasser, Nahuel Soprano-Loto, François Baccelli, Visa Tapio, Paul Almasan, Andra Lutu, Vincenzo Sciancalepore, Carmen Delgado, Xavier Costa-Pérez

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.