Deep Reinforcement Learning-Based Mobility-Aware UAV Content Caching and Placement in Mobile Edge Networks

With the proliferation of smart mobile devices, there is now an ever increasing craving for higher bandwidth for end user satisfaction. Increasing mobile traffic leads to congestion of backhaul networks. One promising solution to this problem is the mobile edge network and consequently mobile edge c...

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Bibliographic Details
Published in:IEEE systems journal 2022-03, Vol.16 (1), p.275-286
Main Authors: Anokye, Stephen, Ayepah-Mensah, Daniel, Seid, Abegaz Mohammed, Boateng, Gordon Owusu, Sun, Guolin
Format: Article
Language:English
Subjects:
Algorithms
Bandwidth
Caching
Cellular communication
Deep learning
Edge computing
Electronic devices
Flooding
Interference
Machine learning
Mobile edge caching
mobile edge network (MEN)
Natural disasters
Networks
Optimization
Quality of experience
reinforcement learning (RL)
Resource utilization
Throughput
Traffic congestion
Trajectory
unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
User satisfaction
Wireless communication
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Summary:With the proliferation of smart mobile devices, there is now an ever increasing craving for higher bandwidth for end user satisfaction. Increasing mobile traffic leads to congestion of backhaul networks. One promising solution to this problem is the mobile edge network and consequently mobile edge caching. There is an emerging paradigm shift toward the use of unmanned aerial vehicles (UAVs) to assist the traditional cellular networks and also to provide connectivity in places where there are no small base stations or faulty ones as a result of some natural disaster such as flooding. Hence, UAVs can be used to assist in content caching as well. This work proposes the use of human centric features, random waypoint user mobility model, and deep reinforcement learning to predict the location of the UAVs and the contents to cache at the UAVs. We formulated our problem as a Markov decision problem (MDP) and proposed a dueling reinforcement learning-based algorithm to solve the MDP problem. Our simulation results prove that our algorithm converges to an optimal solution and performs better than other baseline reinforcement learning algorithms in terms of quality of experience satisfaction, transmit power, and cache resource utilization
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2021.3082837