Machine Learning Driven UAV-assisted Edge Computing
Document Type
Conference Proceeding
Publication Date
1-1-2022
Abstract
The high agility and maneuverability of the unmanned aerial vehicles (UAVs) provide a unique opportunity to carry communications and edge-computing facilities on board to serve mobile users in the cellular networks. An important problem would be to maximize the average aggregate quality-of-experience of all users over time slots. However, this is a non-convex, nonlinear and mixed discrete optimization problem, which is difficult to solve and obtain the optimal solution. We thus propose a deep reinforcement learning algorithm to solve this problem by considering UAV path planning, user assignment, bandwidth and computing resource assignment. The UAVs and base stations are to serve mobile users in multiple continuous time slots, and machine learning is leveraged to facilitate joint resource allocation and path planning in provisioning UAV-assisted edge computing. We compare the performance of our proposal with two baseline cases through simulations 1) with fixed UAV locations and 2) without UAVs. We demonstrate that the deep reinforcement learning algorithm performs better than these two baseline cases.
Identifier
85130713593 (Scopus)
ISBN
[9781665442664]
Publication Title
IEEE Wireless Communications and Networking Conference Wcnc
External Full Text Location
https://doi.org/10.1109/WCNC51071.2022.9771769
ISSN
15253511
First Page
2220
Last Page
2225
Volume
2022-April
Grant
CNS-1814748
Fund Ref
National Science Foundation
Recommended Citation
Zhang, Liang; Jabbari, Bijan; and Ansari, Nirwan, "Machine Learning Driven UAV-assisted Edge Computing" (2022). Faculty Publications. 3449.
https://digitalcommons.njit.edu/fac_pubs/3449