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无人机(Unmanned Aerial Vehicle, UAV)辅助的通感算一体化(Integrated Sensing, Communication, and Computation, ISCC)作为第六代移动通信技术(6th Generation Mobile Communication System, 6G)的重要组成部分，以协同优化的方式提升系统性能。通过整理现有研究关注的核心性能指标，简单概括了UAV-ISCC系统中经典的模型，对2种UAV辅助ISCC系统下的资源分配研究进行概述，包括UAV作为中继与协同感知节点辅助ISCC系统和多UAV辅助ISCC系统的2种场景。简要概括了UAV-ISCC系统所使用的经典优化算法并进行比对，深入探讨这些场景下所优化的关键性能指标，涵盖的指标包括数据吞吐量、信息年龄(Age of Information, AoI)、服务成功率和能效等。同时总结解决UAV辅助ISCC系统中资源优化问题的主要技术方法，包括凸优化、分解算法以及新兴的深度强化学习(Deep Reinforcement Learning, DRL)等。总结当前面临的挑战并对未来研究做出展望，主要贡献可概括为：从优化范式和系统架构2个维度对UAV-ISCC系统资源分配研究进行系统性归纳，总结了在不同场景下各类优化方法的优劣势，深入探讨了UAV-ISCC系统未来发展的方向。
Abstract：Unmanned Aerial Vehicle(UAV)-assisted Integrated Sensing, Communication, and Computation(ISCC) constitutes a crucial component of the 6th Generation Mobile Communication System(6G) networks, enhancing system performance through collaborative optimization. By reviewing the core performance metrics prevalent in existing research, classical models in UAV-ISCC systems are briefly summarized firstly. Subsequently, an overview of resource allocation studies is provided for two types of UAV-assisted ISCC scenarios: systems where UAVs act as relays and cooperative sensing nodes, and multi-UAV-assisted ISCC systems. Next, classic optimization algorithms used in UAV-ISCC systems are briefly summarized and compared, and the key performance metrics optimized in these scenarios are explored in depth. These metrics encompass data throughput, Age of Information(AoI), service success rate, and energy efficiency. Additionally, the main technical methods for addressing resource optimization issues in UAV-assisted ISCC systems are summarized, including convex optimization, decomposition algorithms, and emerging Deep Reinforcement Learning(DRL) techniques. Finally, the current challenges faced are summarized and an outlook for future research is provided. The main contributions of the research can be summarized as follows: a systematic review of UAV-ISCC resource allocation research from the perspectives of optimization paradigm and system architecture; a summary of the advantages and disadvantages of various optimization methods in different scenarios; and an in-depth exploration of the future development directions of UAV-ISCC systems.
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基本信息:
中图分类号:TN929.5;V279
引用信息:
[1]王正强,商鸿赫,万晓榆,等.无人机辅助的通感算一体化系统资源分配研究综述[J].无线电工程,2025,55(10):1965-1975.
基金信息:
四川省区域创新合作项目(2022YFQ0017)~~
2025-10-05
2025-10-05