Machine Learning Based Unmanned Aerial Vehicle Enabled Fog-Radio Aerial Vehicle Enabled Fog-Radio Access Network and Edge Computing

doi:10.12142/ZTECOM.201904006

ZTE Communications ›› 2019, Vol. 17 ›› Issue (4): 33-45.DOI: 10.12142/ZTECOM.201904006

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Machine Learning Based Unmanned Aerial Vehicle Enabled Fog-Radio Aerial Vehicle Enabled Fog-Radio Access Network and Edge Computing

Mohammed SEID^1,²(), Stephen ANOKYE^1,³, SUN Guolin¹

^1.University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
^2.Dilla University, Dilla, Ethiopia
^3.University of Mines and Technology, Tarkwa 237, Ghana

Received:2019-10-27 Online:2019-12-25 Published:2020-04-16
About author:Mohammed SEID (Julian. Ahrens@dfki.de)received his B. Sc. and M. Sc. degrees in computer sci? ence from Ambo University, Ethiopia in 2010 and Addis Ababa University, Ethiopia in 2015, respectively. He is currently pursuing his Ph. D. degree in computer science and technology at University of Electronic Science and Technology of China. From 2010 to 2016 he worked in Dilla University, Ethiopia as a graduate assistant and lecturer. His interests include mobile edge computing, fog computing, UAV networks, IoT, and 5G wireless networks.|Stephen ANOKYEreceived his M. Eng. degree in computer science from Hunan University, China in 2009 and his B. Sc. in computer science from Kwame Nkrumah University of Science and Technology, Ghana in 2004. He’s currently a Ph. D. candidate at the University of Electronic Science and Technology of China. Between 2010 and 2012, he worked as a lecturer in the department of computer science at Garden City University College in Ghana. Since 2012, he has become a lecturer at the Department of Computer Science and Engineering, the University of Mines and Technology, Ghana. His research interests are security in wireless sensor networks, mobile and cloud networks with AI, UAV networks, IoT, and 5G wireless networks.|SUN Guolinreceived his B. S. , M. S. and Ph. D. degrees all in communication and information system from University of Electronic Science and Technology of China (UESTC) in 2000, 2003 and 2005 respectively. After his Ph. D. graduation in 2005, he has got eightyear industrial work experience in wireless research and development for LTE, Wi-Fi, and IoT, Cognitive radio, Localization and navigation. Before he joined the UESTC as an associate professor in August 2012, he worked in Huawei Technologies Sweden. Dr. SUN has filed over 30 patents and published over 40 scientific conference and journal papers. He acted as TPC member of several conferences. Currently, he serves as a vice-chair of the 5G oriented cognitive radio SIG of the IEEE Technical Committee on Cognitive Networks (TCCN). His general research interests are software defined networks, network function virtualization, and radio resource management.

Abstract

Abstract:

The emerging unmanned aerial vehicle (UAV) technology and its applications have become part of the massive Internet of Things (mIoT) ecosystem for future cellular networks. Internet of things (IoT) devices have limited computation capacity and battery life and the cloud is not suitable for offloading IoT tasks due to the distance, latency and high energy consumption. Mobile edge computing (MEC) and fog radio access network (F-RAN) together with machine learning algorithms are an emerging approach to solving complex network problems as described above. In this paper, we suggest a new orientation with UAV enabled F-RAN architecture. This architecture adopts the decentralized deep reinforcement learning (DRL) algorithm for edge IoT devices which makes independent decisions to perform computation offloading, resource allocation, and association in the aerial to ground (A2G) network. Additionally, we summarized the works on machine learning approaches for UAV networks and MEC networks, which are related to the suggested architecture and discussed some technical challenges in the smart UAV-IoT, F-RAN 5G and Beyond 5G (6G).

Key words: unmanned aerial vehicle, machine learning, F-RAN, edge computing

Mohammed SEID, Stephen ANOKYE, SUN Guolin. Machine Learning Based Unmanned Aerial Vehicle Enabled Fog-Radio Aerial Vehicle Enabled Fog-Radio Access Network and Edge Computing[J]. ZTE Communications, 2019, 17(4): 33-45.

Figures/Tables 4

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Terrestrial Networks	UAV Networks
Insufficient spectrum	Insufficient spectrum
Well defined energy constraints and models	Elaborate and stringent energy constraints and models
Mainly static association	Varying cell association
No timing constraints, with BS being always there	Hover and flight time constraints
Terrestrial BS	UAV BS
Typical two-dimensional deployment	By nature, three-dimensional deployment
Mostly long-term and permanent deployments	Short-term and frequently changing deployments
Few and selected locations	Mostly unrestricted locations
Fixed and static	Mobility dimension
Not suitable for mobility tracking	Suitable for mobility tracking

Terrestrial Networks	UAV Networks
Insufficient spectrum	Insufficient spectrum
Well defined energy constraints and models	Elaborate and stringent energy constraints and models
Mainly static association	Varying cell association
No timing constraints, with BS being always there	Hover and flight time constraints
Terrestrial BS	UAV BS
Typical two-dimensional deployment	By nature, three-dimensional deployment
Mostly long-term and permanent deployments	Short-term and frequently changing deployments
Few and selected locations	Mostly unrestricted locations
Fixed and static	Mobility dimension
Not suitable for mobility tracking	Suitable for mobility tracking

Paper	Network	Agent	Model	Learning Algorithm
[50]	CRN	Base station	MDP	DQN using FNN
[52]	Vehicular Network	Service Provider	MDP	DQN using FNN
[53]	Vehicular Network	Service Provider	MDP	DQN using CNN
[55]	Cellular System	Base Station	MDP	DQN using CNN
[56]	Cellular System	Mobile User	MDP	DQN using CNN
[57]	Cellular System	Base Station	MDP	DQN using FNN
[58]	Cellular System	Mobile User	MDP	DQN using FNN
[59]	Cellular System	Mobile User	MDP	DDQN, SARSA
[60]	Cellular System	Mobile User	Game theory	DQN using CNN, Q-learning

Paper	Network	Agent	Model	Learning Algorithm
[50]	CRN	Base station	MDP	DQN using FNN
[52]	Vehicular Network	Service Provider	MDP	DQN using FNN
[53]	Vehicular Network	Service Provider	MDP	DQN using CNN
[55]	Cellular System	Base Station	MDP	DQN using CNN
[56]	Cellular System	Mobile User	MDP	DQN using CNN
[57]	Cellular System	Base Station	MDP	DQN using FNN
[58]	Cellular System	Mobile User	MDP	DQN using FNN
[59]	Cellular System	Mobile User	MDP	DDQN, SARSA
[60]	Cellular System	Mobile User	Game theory	DQN using CNN, Q-learning

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Machine Learning Based Unmanned Aerial Vehicle Enabled Fog-Radio Aerial Vehicle Enabled Fog-Radio Access Network and Edge Computing

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