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Game Theoretic Spectrum Allocation in Femtocell Networks for Smart Electric Distribution Grids

Author

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  • Ali Mohammadi

    (Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Mohammad Javad Dehghani

    (Department of Electrical and Electronic Eng, Shiraz University of Technology, Shiraz 71557-13876, Iran
    Regional Information Center for Science and Technology, Shiraz 71946-94171, Iran)

  • Elham Ghazizadeh

    (Department of Electrical and Systems Engineering, Washington University, St. Louis, MO 63130, USA)

Abstract

Ever growing penetration of the behind-the-meter technologies is changing the electricity consumption profiles of end-users. Intelligent coordination of these emerging technologies through a robust communication infrastructure enables their seamless integration with electric utilities’ operation. In this context, an efficient and reliable communication infrastructure plays a pivotal role in enabling optimal integration of emerging resources. In this paper, we propose a game-theory based method to enhance efficiency of the underlying communication network. Specifically, we focus on Femtocell communication technology which is one the promising options for improving poor indoor communication coverage. The major drawback for using femtocell communication technology is cross-layer interference of femto users (FUs) and macro users (MUs) which adversely impact network performance. In this paper, we propose a novel approach for sharing spectrum in a cognitive radio system with FUs and MUs as primary and secondary users, respectively. The underlying problem is formulated as Stackelberg game that is joined with a convex optimization problem. In this study, MUs and FUs are assumed to be selfish, rational and motivated to achieve maximum utility function, while MUs are competing to obtain maximum bandwidth. Finally, we present a closed form solution for the proposed approach which obtains a unique Nash Equilibrium and prioritizes the access of MUs to femto-base stations. Simulation results provide proof of concept and verify the effectiveness of our mathematical modeling.

Suggested Citation

  • Ali Mohammadi & Mohammad Javad Dehghani & Elham Ghazizadeh, 2018. "Game Theoretic Spectrum Allocation in Femtocell Networks for Smart Electric Distribution Grids," Energies, MDPI, vol. 11(7), pages 1-18, June.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1635-:d:153955
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    References listed on IDEAS

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    1. M. Hadi Amini & Orkun Karabasoglu, 2018. "Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks," Energies, MDPI, vol. 11(1), pages 1-25, January.
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    Cited by:

    1. Yongsheng Cao & Guanglin Zhang & Demin Li & Lin Wang & Zongpeng Li, 2018. "Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable Energy," Energies, MDPI, vol. 11(8), pages 1-20, August.
    2. Weijun Wang & Weisong Peng & Xin Tan & Haoyue Wang & Chenjun Sun, 2018. "Forecasting the Low-Voltage Line Damage Caused by Typhoons in China Based on the Factor Analysis Method and an Improved Gravitational Search Algorithm-Extreme Learning Machine," Energies, MDPI, vol. 11(9), pages 1-12, September.
    3. Bo Li & Yudong Wang & Jian Li & Shengxian Cao, 2018. "A Fully Distributed Approach for Economic Dispatch Problem of Smart Grid," Energies, MDPI, vol. 11(8), pages 1-21, August.
    4. Cesare Biserni & Paolo Valdiserri & Dario D’Orazio & Massimo Garai, 2018. "Energy Retrofitting Strategies and Economic Assessments: The Case Study of a Residential Complex Using Utility Bills," Energies, MDPI, vol. 11(8), pages 1-15, August.
    5. Ye, Chengjin & Ding, Yi & Song, Yonghua & Lin, Zhenzhi & Wang, Lei, 2018. "A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing," Applied Energy, Elsevier, vol. 232(C), pages 9-25.
    6. Neeraj Priyadarshi & Sanjeevikumar Padmanaban & Dan M. Ionel & Lucian Mihet-Popa & Farooque Azam, 2018. "Hybrid PV-Wind, Micro-Grid Development Using Quasi-Z-Source Inverter Modeling and Control—Experimental Investigation," Energies, MDPI, vol. 11(9), pages 1-15, August.

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