Provisioning Green Energy for Base Stations in Heterogeneous Networks

Tao Han, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Cellular networks are among the biggest energy hogs of communication networks, and their contributions to the global energy consumption rapidly increase due to the surge of data traffic. With the development of green energy technologies, base stations (BSs) can be powered by green energy to reduce on-grid energy consumption and subsequently reduce carbon footprints. However, equipping a BS with a green energy system incurs additional capital expenditure (CAPEX) that is determined by the size of the green energy generator, the battery capacity, and other installation expenses. In this paper, we introduce and investigate the green energy provisioning (GEP) problem, which aims to minimize the CAPEX of deploying green energy systems in BSs while satisfying the quality-of-service (QoS) requirements of cellular networks. The GEP problem is challenging because it involves optimization over multiple time slots and across multiple BSs. We decompose the GEP problem into the weighted energy minimization problem and the green energy system sizing problem and propose a GEP solution consisting of the provision-cost-aware traffic load balancing algorithm and the binary energy system sizing algorithm to solve the subproblems and subsequently solve the GEP problem. We validate the performance and the viability of the proposed GEP solution through extensive simulations, which also conform to our analytical results.

Original languageEnglish (US)
Article number7182373
Pages (from-to)5439-5448
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Volume65
Issue number7
DOIs
StatePublished - Jul 2016

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

Keywords

  • Energy system provision
  • green communications
  • heterogeneous networks
  • renewable energy

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