Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning Public Deposited

http://ir.library.oregonstate.edu/concern/articles/k0698930d

This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Public Library of Science. The published article can be found at:  http://www.plosone.org/.

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.
Resource Type
DOI
Date Available
Date Issued
Citation
  • Sohn, I., Liu, H., & Ansari, N. (2015). Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning. PLoS ONE, 10(7), e0132997. doi:10.1371/journal.pone.0132997
Series
Rights Statement
Funding Statement (additional comments about funding)
Publisher
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Submitted by Patricia Black (patricia.black@oregonstate.edu) on 2015-08-18T16:26:12Z No. of bitstreams: 2 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) LiuHuapingEECSOptimizingCellularNetworks.pdf: 2705123 bytes, checksum: be7ec6b7118454f346799c492d8ce7f4 (MD5)
  • description.provenance : Made available in DSpace on 2015-08-18T16:26:44Z (GMT). No. of bitstreams: 2 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) LiuHuapingEECSOptimizingCellularNetworks.pdf: 2705123 bytes, checksum: be7ec6b7118454f346799c492d8ce7f4 (MD5) Previous issue date: 2015-07-13
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2015-08-18T16:26:44Z (GMT) No. of bitstreams: 2 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) LiuHuapingEECSOptimizingCellularNetworks.pdf: 2705123 bytes, checksum: be7ec6b7118454f346799c492d8ce7f4 (MD5)

Relationships

Parents:

This work has no parents.

Last modified

Downloadable Content

Download PDF

Items