Journal
IEEE WIRELESS COMMUNICATIONS
Volume 28, Issue 1, Pages 120-127Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MWC.001.2000174
Keywords
Resource management; Artificial intelligence; Wireless communication; Three-dimensional displays; Social networking (online); Optimization; Big Data
Categories
Funding
- Engineering and Physical Sciences Research Council [EP/N004558/1, EP/P034284/1, EP/P003990/1]
- Royal Society's Global Challenges Research Fund Grant
- European Research Council's Advanced Fellow Grant QuantCom
- Key Area R&D Program of Guang dong Province [2018B030338001]
- Natural Science Foundation of China [NSFC61629101]
- Guang dong Research Project [2017ZT07X152]
Ask authors/readers for more resources
In this article, the use of artificial intelligence-enabled unmanned aerial vehicles (UAVs) as aerial base stations for wireless networks is proposed to overcome challenges such as random fluctuation of wireless channels, blocking, and user mobility effects. The AI enables the UAVs to adapt quickly to changing environments, learn from user feedback, and interact cooperatively for system optimization. This approach not only enhances network performance and reliability but also facilitates dynamic trajectory design and resource allocation, while discussing potential research challenges and opportunities.
In this article, we propose artificial intelligence (AI) enabled unmanned aerial vehicle (UAV) aided wireless networks (UAWN) for overcoming the challenges imposed by the random fluctuation of wireless channels, blocking and user mobility effects. In UAWN, multiple UAVs are employed as aerial base stations, which are capable of promptly adapting to the randomly fluctuating environment by collecting information about the users' position and tele-traffic demands, learning from the environment and acting upon the satisfaction level feedback received from the users. Moreover, AI enables the interaction among a swarm of UAVs for cooperative optimization of the system. As a benefit of the AI framework, several challenges of conventional UAWN may be circumvented, leading to enhanced network performance, improved reliability and agile adaptivity. As a further benefit, dynamic trajectory design and resource allocation are demonstrated. Finally, potential research challenges and opportunities are discussed.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available