Joint Computation and Communication Resource Allocation for Energy-Efficient Mobile Edge Networks

Johnson Opadere, Qiang Liu, Ning Zhang, Tao Han

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

9 Scopus citations

Abstract

In this paper, an ultra-dense mobile edge network is studied, where base stations (BSs) are equipped with computation resources to execute users' offloaded tasks. Although an ultradense BS deployment provides seamless coverage and reduced computation latency of the offloaded tasks, the cost of network power consumption is increased. We formulate an optimization problem to jointly optimize active BSs set, uplink and downlink beamforming vector selection, and computation resource allocation in order to tackle the power consumption and latency tradeoff. To efficiently solve this problem, we propose a sequential solution framework. Specifically, we first select the active BSs based on communication and computation power-aware selection rule. The computation resources and dual-link beamformers are subsequently optimized for the satisfaction of task computation deadline, network energy savings and improved coverage. Simulation results show that the proposed joint optimization framework significantly reduces the network power consumption.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538680889
DOIs
StatePublished - May 2019
Externally publishedYes
Event2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China
Duration: May 20 2019May 24 2019

Publication series

NameIEEE International Conference on Communications
Volume2019-May
ISSN (Print)1550-3607

Conference

Conference2019 IEEE International Conference on Communications, ICC 2019
Country/TerritoryChina
CityShanghai
Period5/20/195/24/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Base station sleep-mode
  • Computation resource optimization
  • Mobile edge computing

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