Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks
Joint Authors
Gu, Xuemai
Zhang, Lingyan
Wu, Chenyu
Shi, Shuo
Gu, Shushi
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-14
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
Cache-enabled unmanned aerial vehicles (UAVs) have been envisioned as a promising technology for many applications in future urban wireless communication.
However, to utilize UAVs properly is challenging due to limited endurance and storage capacity as well as the continuous roam of the mobile users.
To meet the diversity of urban communication services, it is essential to exploit UAVs’ potential of mobility and storage resource.
Toward this end, we consider an urban cache-enabled communication network where the UAVs serve mobile users with energy and cache capacity constraints.
We formulate an optimization problem to maximize the sum achievable throughput in this system.
To solve this problem, we propose a deep reinforcement learning-based joint content placement and trajectory design algorithm (DRL-JCT), whose progress can be divided into two stages: offline content placement stage and online user tracking stage.
First, we present a link-based scheme to maximize the cache hit rate of all users’ file requirements under cache capacity constraint.
The NP-hard problem is solved by approximation and convex optimization.
Then, we leverage the Double Deep Q-Network (DDQN) to track mobile users online with their instantaneous two-dimensional coordinate under energy constraint.
Numerical results show that our algorithm converges well after a small number of iterations.
Compared with several benchmark schemes, our algorithm adapts to the dynamic conditions and provides significant performance in terms of sum achievable throughput.
American Psychological Association (APA)
Wu, Chenyu& Shi, Shuo& Gu, Shushi& Zhang, Lingyan& Gu, Xuemai. 2020. Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1214694
Modern Language Association (MLA)
Wu, Chenyu…[et al.]. Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1214694
American Medical Association (AMA)
Wu, Chenyu& Shi, Shuo& Gu, Shushi& Zhang, Lingyan& Gu, Xuemai. Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1214694
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214694