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Joint Optimization of Computation Offloading and Power Allocation Based on DRL in Secure LEO Satellite Edge Computing System

  • School of Electronics and Information Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Low earth orbit (LEO) satellites can be equipped with mobile edge computing (MEC) servers to form LEO satellite edge computing system to provide global services for ground users. In order to solve eavesdropping attacks in LEO satellite edge computing system, this paper designs a secure LEO satellite edge computing system, which uses deep reinforcement learning (DRL) to obtain offloading decisions and convex optimization algorithm to obtain sub-optimal power allocation scheme. Simulation results show that the proposed algorithm has excellent convergence effect and is superior to other comparative algorithms.

Original languageEnglish
Title of host publication2023 IEEE 23rd International Conference on Communication Technology
Subtitle of host publicationAdvanced Communication and Internet of Things, ICCT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1668-1671
Number of pages4
ISBN (Electronic)9798350325959
DOIs
StatePublished - 2023
Externally publishedYes
Event23rd IEEE International Conference on Communication Technology, ICCT 2023 - Wuxi, China
Duration: 20 Oct 202322 Oct 2023

Publication series

NameInternational Conference on Communication Technology Proceedings, ICCT
ISSN (Print)2576-7844
ISSN (Electronic)2576-7828

Conference

Conference23rd IEEE International Conference on Communication Technology, ICCT 2023
Country/TerritoryChina
CityWuxi
Period20/10/2322/10/23

Keywords

  • DRL
  • LEO satellite
  • convex optimization
  • edge computing
  • power allocation

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