Optimized smart grid energy procurement for LTE networks using evolutionary algorithms

Hakim Ghazzai, Elias E. Yaacoub, Mohamed-Slim Alouini, Adnan A. Abu-Dayya

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Moreover, introducing renewable energy as an alternative power source has become a real challenge among network operators. In this paper, we formulate an optimization problem that aims to maximize the profit of Long-Term Evolution (LTE) cellular operators and to simultaneously minimize the CO2 emissions in green wireless cellular networks without affecting the desired quality of service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches, such as the genetic (GA) and particle swarm optimization (PSO) algorithms. In this paper, we propose GA-based and PSO-based methods that reduce the energy consumption of BSs by not only shutting down underutilized BSs but by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers), as well. A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods.
Original languageEnglish (US)
Pages (from-to)4508-4519
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Issue number9
StatePublished - Nov 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported in part by the Qatar National Research Fund (a member of Qatar Foundation) under NPRP Grant 6-001-2-001. The review of this paper was coordinated by Dr. Y. Ji.

ASJC Scopus subject areas

  • Automotive Engineering
  • Applied Mathematics
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Aerospace Engineering


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