Digital Twin-Enabled Domain Adaptation for Zero-Touch UAV Networks: Survey and Challenges
Digital Twin-Enabled Domain Adaptation for Zero-Touch UAV Networks: Survey and Challenges
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DOI：
10.48550/arxiv.2301.03359
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Maxwell Mcmanus;Yuqing Cui;Josh Zhang;Jiangqi Hu;Sabarish Krishna Moorthy;Zhangyu Guan;Nicholas Mastronarde
中科院分区：
文献类型：
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作者：
Maxwell Mcmanus;Yuqing Cui;Josh Zhang;Jiangqi Hu;Sabarish Krishna Moorthy;Zhangyu Guan;Nicholas Mastronarde
在现有的无线网络中，控制程序是手动设计的，并且用于某些预定义的场景。这个过程是复杂和容易出错的，并且产生的控制程序对破坏性的变化没有弹性。基于人工智能和机器学习（AI/ML）的数据驱动控制已被设想为复杂无线系统的自动化建模、优化和控制的关键技术。然而，现有的AI/ML技术依赖于足够的标记良好的数据，并且可能存在收敛速度慢和泛化能力差的问题。在这篇文章中，专注于数字双辅助无线无人机（UAV）系统，我们提供了一个调查的新兴技术，可以实现快速收敛的数据驱动控制的无线系统与增强的泛化能力，以新的环境。这些包括基于SLAM的传感和网络软件化，用于数字孪生结构，用于域适应的鲁棒强化学习和系统识别，以及测试设施共享和联合。同时也讨论了相应的研究机会。
In existing wireless networks, the control programs have been designed manually and for certain predefined scenarios. This process is complicated and error-prone, and the resulting control programs are not resilient to disruptive changes. Data-driven control based on Artificial Intelligence and Machine Learning (AI/ML) has been envisioned as a key technique to automate the modeling, optimization and control of complex wireless systems. However, existing AI/ML techniques rely on sufficient well-labeled data and may suffer from slow convergence and poor generalizability. In this article, focusing on digital twin-assisted wireless unmanned aerial vehicle (UAV) systems, we provide a survey of emerging techniques that can enable fast-converging data-driven control of wireless systems with enhanced generalization capability to new environments. These include SLAM-based sensing and network softwarization for digital twin construction, robust reinforcement learning and system identification for domain adaptation, and testing facility sharing and federation. The corresponding research opportunities are also discussed.