Artificial Intelligence Aided Next-Generation Networks Relying on UAVs

Сети следующего поколения с опорой на БПЛА, поддерживаемые искусственным интеллектом
Shuguang Cui, Mingzhe Chen, Lajos Hanzo, Yuanwei Liu, Xiao Liu, Yue Chen
2020-11-25

AI-enabled UAV-aided wireless networksUAV swarm cooperationdynamic trajectory designresource allocationunmanned aerial vehicle base stations
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.
1
AI facilitates interaction among UAV swarms for cooperative system optimization, improving network performance, reliability, and adaptability.
2
AI-enabled UAV-aided wireless networks (UAWN) can overcome wireless channel fluctuations, blocking, and user mobility by collecting user position and traffic demand data.
3
Multiple UAVs acting as aerial base stations can promptly adapt to random environments through learning and acting on user satisfaction feedback.
4
The AI framework enables dynamic trajectory design and resource allocation for UAVs in next-generation wireless networks.
5
The paper identifies remaining research challenges and opportunities for AI-integrated UAWN development.

Artificial intelligence-enabled unmanned aerial vehicle aided wireless network (AI-enabled UAWN) with multiple UAVs acting as aerial base stations

AI-driven adaptation, cooperative optimization, dynamic trajectory design and resource allocation to mitigate channel fluctuations, blocking, and user mobility and to improve network performance, reliability and adaptability

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2020-11-25
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Authors
Shuguang Cui
Mingzhe Chen
Lajos Hanzo
Yuanwei Liu
Xiao Liu
Yue Chen
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