title: Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.
abstract: 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.
authors.affiliation: Division of Electronics & Electrical Engineering, Dongguk University-Seoul, Seoul, Republic of Korea.
authors.forename: Insoo
authors.initials: I
authors.lastname: Sohn
authors.affiliation: School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, United States of America.
authors.forename: Huaping
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, United States of America.
authors.forename: Nirwan
authors.initials: N
authors.lastname: Ansari
mesh_terms: D003246:Conservation of Energy Resources; D005716:Game Theory; D007858:Learning; D008962:Models, Theoretical