人工智能原生集成感知与通信的统一评估方法
Unified Evaluation Methodology for AI-Native Integrated Sensing and Communication
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中文总结 AI 辅助
研究人工智能原生集成感知与通信，提出统一系统架构和评估方法，含形式化设计空间、三阶段验证管道及报告清单，通过实例说明如何构建基线和证据，弥合理论与实际性能差距。
中文摘要 AI 辅助
集成感知与通信（ISAC）在单个闭环系统中结合了无线电感知、数据传输和控制操作。当人工智能驱动的策略在各种感知任务和目标上在线适应感知和通信时，端到端性能不仅受波形和信道条件影响，还受推理延迟、不确定性、环境动态和硬件非理想性影响，导致感知精度、通信可靠性和资源开销之间的基本权衡。本文提出了一种用于人工智能原生ISAC的统一系统架构和评估方法，其中基于学习的智能体在不确定性下在线适应感知、通信和驱动策略。我们形式化了闭环ISAC的设计空间，提出了一个三阶段验证管道，从边界和可行性分析，到高保真数字孪生模拟，再到初步空中验证，并提供了一个最小报告清单，将技术关键性能指标与应用级关键价值指标联系起来。通过两个代表性实例说明了如何构建可重现的基线和跨异构部署的可比证据，有助于弥合理论ISAC增益与可部署性能声明之间的差距。
英文摘要
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.
