Machine Learning Driven UAV-assisted Edge Computing

Liang Zhang, Bijan Jabbari, Nirwan Ansari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2022 IEEE Wireless Communications and Networking Conference, WCNC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2220-2225
Number of pages6
ISBN (Electronic)9781665442664
DOIs
StatePublished - 2022
Event2022 IEEE Wireless Communications and Networking Conference, WCNC 2022 - Austin, United States
Duration: Apr 10 2022Apr 13 2022

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2022-April
ISSN (Print)1525-3511

Conference

Conference2022 IEEE Wireless Communications and Networking Conference, WCNC 2022
Country/TerritoryUnited States
CityAustin
Period4/10/224/13/22

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • computation offloading
  • edge computing
  • joint optimization
  • Machine learning
  • path planning
  • unmanned aerial vehicle (UAV)

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