计算机科学
强化学习
无线网络
背景(考古学)
无人机
无线
开放式研究
桥(图论)
资源(消歧)
计算机网络
人工智能
电信
医学
古生物学
遗传学
万维网
内科学
生物
作者
Yu Bai,Huijun Zhao,X. L. Zhang,Zheng Chang,Riku Jäntti,Kun Yang
标识
DOI:10.1109/comst.2023.3323344
摘要
Unmanned aerial vehicle (UAV)-based wireless networks have received increasing research interest in recent years and are gradually being utilized in various aspects of our society. The growing complexity of UAV applications such as disaster management, plant protection, and environment monitoring, has resulted in escalating and stringent requirements for UAV networks that a single UAV cannot fulfill. To address this, multi-UAV wireless networks (MUWNs) have emerged, offering enhanced resource-carrying capacity and enabling collaborative mission completion by multiple UAVs. However, the effective operation of MUWNs necessitates a higher level of autonomy and intelligence, particularly in decision-making and multi-objective optimization under diverse environmental conditions. Reinforcement Learning (RL), an intelligent and goal-oriented decision-making approach, has emerged as a promising solution for addressing the intricate tasks associated with MUWNs. As one may notice, the literature still lacks a comprehensive survey of recent advancements in RL-based MUWNs. Thus, this paper aims to bridge this gap by providing a comprehensive review of RL-based approaches in the context of autonomous MUWNs. We present an informative overview of RL and demonstrate its application within the framework of MUWNs. Specifically, we summarize various applications of RL in MUWNs, including data access, sensing and collection, resource allocation for wireless connectivity, UAV-assisted mobile edge computing, localization, trajectory planning, and network security. Furthermore, we identify and discuss several open challenges based on the insights gained from our review.
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