Toward Multi-Functional LAWNs With ISAC: Opportunities, Challenges, and the Road Ahead

作者：Jun Wu, Weijie Yuan, Xiaoqi Zhang, Yaohuan Yu, Yuanhao Cui, Fan Liu, Geng Sun, Jiacheng Wang, Dusit Niyato, Dong In Kim · 发表于：IEEE Wireless Communications · 年份：2026 · DOI：10.1109/mwc.2026.3672360 · 被引用次数：7 · 研究领域：Natural Language Processing Techniques、Intelligent Tutoring Systems and Adaptive Learning、Underwater Vehicles and Communication Systems

Integrated sensing and communication (ISAC) has been envisioned as a foundational technology for future low-altitude wireless networks (LAWNs), enabling real-time environmental perception and data exchange across aerial-ground systems. In this article, we first explore the roles of ISAC in LAWNs from both node-level and network-level perspectives. We highlight the performance gains achieved through hierarchical integration and cooperation, wherein key design trade-offs are demonstrated. Apart from physical-layer enhancements, emerging LAWN applications demand broader functionalities. To this end, we propose a multi-functional LAWN framework that extends ISAC with capabilities in control, computation, wireless power transfer, and large language model (LLM)-based intelligence. We further provide a representative case study to present the benefits of ISAC-enabled LAWNs and the promising research directions are finally outlined.