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.
@article{arxiv.2301.03359,
title = {Digital Twin-Enabled Domain Adaptation for Zero-Touch UAV Networks: Survey and Challenges},
author = {Maxwell McManus and Yuqing Cui and Josh and Zhang and Jiangqi Hu and Sabarish Krishna Moorthy and Zhangyu Guan and Nicholas Mastronarde and Elizabeth Serena Bentley and Michael Medley},
journal= {arXiv preprint arXiv:2301.03359},
year = {2023}
}