About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 – December 1, 2019, Proceedings, Part II

Research Article

Energy Efficient Computation Offloading for Energy Harvesting-Enabled Heterogeneous Cellular Networks (Workshop)

Download(Requires a free EAI acccount)
4 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-030-41117-6_32,
        author={Mengqi Mao and Rong Chai and Qianbin Chen},
        title={Energy Efficient Computation Offloading for Energy Harvesting-Enabled Heterogeneous Cellular Networks (Workshop)},
        proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II},
        proceedings_a={CHINACOM PART 2},
        year={2020},
        month={2},
        keywords={Mobile edge computing Heterogeneous cellular network Computation offloading Resource allocation Hotbooting Q-learning},
        doi={10.1007/978-3-030-41117-6_32}
    }
    
  • Mengqi Mao
    Rong Chai
    Qianbin Chen
    Year: 2020
    Energy Efficient Computation Offloading for Energy Harvesting-Enabled Heterogeneous Cellular Networks (Workshop)
    CHINACOM PART 2
    Springer
    DOI: 10.1007/978-3-030-41117-6_32
Mengqi Mao1,*, Rong Chai1, Qianbin Chen1
  • 1: School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications
*Contact email: maomengqii@163.com

Abstract

Mobile edge computing (MEC) is regarded as an emerging paradigm of computation that aims at reducing computation latency and improving quality of experience. In this paper, we consider an MEC-enabled heterogeneous cellular network (HCN) consisting of one macro base station (MBS), one small base station (SBS) and a number of users. By defining workload execution cost as the weighted sum of the energy consumption of the MBS and the workload dropping cost, the joint computation offloading and resource allocation problem is formulated as a workload execution cost minimization problem under the constraints of computation offloading, resource allocation and delay tolerant, etc. As the formulated optimization problem is a Markov decision process (MDP)-based offloading problem, we propose a hotbooting Q-learning-based algorithm to obtain the optimal strategy. Numerical results demonstrate the effectiveness of the proposed scheme.

Keywords
Mobile edge computing Heterogeneous cellular network Computation offloading Resource allocation Hotbooting Q-learning
Published
2020-02-27
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-41117-6_32
Copyright © 2019–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL