用无人机和智能算法提升零能耗物联网定位与感知能力
AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey
- 用无人机作移动基站，增强反向散射信号覆盖与定位精度
- 融合感知与通信，实现无线资源的高效共享与多任务协同
- 适合研究智能物联网、低功耗通信及6G系统的学者与工程师
零能耗物联网使被动或近被动设备通过能量采集运行而非依赖电池。反向散射通信（BackCom）通过反射和调制入射射频信号支持这一愿景，但面临反射弱、双路损耗、覆盖有限、直连干扰和对外部射频源依赖等问题。无人机（UAV）可作为移动载波发射器、数据收集者、中继、空中接收器、移动锚点、传感平台和边缘智能节点，缓解上述限制。集成感知与通信（ISAC）进一步实现数据传输、定位、目标探测与环境感知共用无线资源。本文综述基于射频的AI赋能无人机辅助反向散射定位与ISAC在零能耗物联网中的应用，涵盖使能技术，采用结构化PRISMA方法论，构建统一分类体系，涵盖网络架构、无人机角色、反向散射模式、射频源、定位与感知功能、人工智能技术及性能指标。通过对比表格、定量趋势分析、覆盖评估与教学式数值示例，探讨无人机辅助背向散射、无源定位、基于ISAC的无人机-反向散射系统及人工智能驱动优化。最后，指出未来方向：真实信道建模、能量中性运行、基准测试、可复现性、可扩展可信人工智能、安全隐私、硬件验证，以及与RIS、MEC、数字孪生和6G技术的集成。
原文摘要 · Abstract (English)
Zero-energy Internet of Things (IoT) enables passive or near-passive devices to operate on harvested energy rather than batteries. Backscatter communication (BackCom) supports this vision by enabling tags to transmit data via reflection and modulation of incident RF signals, but it suffers from weak reflections, double-path loss, limited coverage, direct-link interference, and dependence on external RF sources. Unmanned aerial vehicles (UAVs) can mitigate these limitations by acting as mobile carrier emitters, data collectors, relays, aerial receivers, mobile anchors, sensing platforms, and edge-intelligence nodes. Integrated sensing and communication (ISAC) further enables the sharing of wireless resources for data transmission, localization, target sensing, and environmental awareness. This article surveys RF-based AI-empowered UAV-assisted backscatter localization and ISAC for zero-energy IoT. It reviews enabling technologies, presents a structured PRISMA-informed methodology, and develops a unified taxonomy covering network architectures, UAV roles, backscatter modes, RF sources, localization and sensing functions, AI techniques, and performance metrics. It also discusses UAV-assisted BackCom, passive localization, ISAC-enabled UAV-backscatter systems, and AI-driven optimization through comparative tables, quantitative trend analysis, coverage evaluation, and tutorial-style numerical illustrations. Finally, it identifies open challenges and future directions in realistic channel modeling, energy-neutral operation, benchmarking, reproducibility, scalable and trustworthy AI, security, privacy, hardware validation, and integration with RIS, MEC, digital twins, and 6G technologies.
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