ZTE Communications ›› 2019, Vol. 17 ›› Issue (4): 33-45.DOI: 10.12142/ZTECOM.201904006
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Mohammed SEID1,2(), Stephen ANOKYE1,3, SUN Guolin1
Received:
2019-10-27
Online:
2019-12-25
Published:
2020-04-16
About author:
Mohammed SEID (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.
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URL: http://zte.magtechjournal.com/EN/10.12142/ZTECOM.201904006
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 |
Table 1 Comparison between UAV networks with base stations and terrestrial networks with base stations
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 |
---|---|---|---|---|
[ | CRN | Base station | MDP | DQN using FNN |
[ | Vehicular Network | Service Provider | MDP | DQN using FNN |
[ | Vehicular Network | Service Provider | MDP | DQN using CNN |
[ | Cellular System | Base Station | MDP | DQN using CNN |
[ | Cellular System | Mobile User | MDP | DQN using CNN |
[ | Cellular System | Base Station | MDP | DQN using FNN |
[ | Cellular System | Mobile User | MDP | DQN using FNN |
[ | Cellular System | Mobile User | MDP | DDQN, SARSA |
[ | Cellular System | Mobile User | Game theory | DQN using CNN, Q-learning |
Table 2 Machine learning algorithms for computation offloading and resource allocation in vehicular networks and cellular networks
Paper | Network | Agent | Model | Learning Algorithm |
---|---|---|---|---|
[ | CRN | Base station | MDP | DQN using FNN |
[ | Vehicular Network | Service Provider | MDP | DQN using FNN |
[ | Vehicular Network | Service Provider | MDP | DQN using CNN |
[ | Cellular System | Base Station | MDP | DQN using CNN |
[ | Cellular System | Mobile User | MDP | DQN using CNN |
[ | Cellular System | Base Station | MDP | DQN using FNN |
[ | Cellular System | Mobile User | MDP | DQN using FNN |
[ | Cellular System | Mobile User | MDP | DDQN, SARSA |
[ | Cellular System | Mobile User | Game theory | DQN using CNN, Q-learning |
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