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    Energy-Efficient Power Allocation for Cognitive MIMO Channels

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    Type
    Conference Paper
    Authors
    Sboui, Lokman cc
    Rezki, Zouheir cc
    Salem, Ahmed Sultan
    Alouini, Mohamed-Slim cc
    KAUST Department
    Communication Theory Lab
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2017-03-20
    Online Publication Date
    2017-03-20
    Print Publication Date
    2016-09
    Permanent link to this record
    http://hdl.handle.net/10754/623830
    
    Metadata
    Show full item record
    Abstract
    Due to the massive data traffic in wireless networks, energy consumption has become a crucial concern, especially with the limited power supply of the mobile terminals and the increasing CO2 emission of the cellular industry. In this context, we study the energy efficiency (EE) of MIMO spectrum sharing cognitive radio (CR) systems under power and interference constraints. We present an energy-efficient power allocation framework based on maximizing the average EE per parallel channel resulting from the singular value decomposition (SVD) eigenmode transmission. We also present a sub-optimal low-complexity power allocation scheme based on the water-filling power allocation. In the numerical results, we show that the sub-optimal power allocation achieves at least 95% of the optimal performance. In addition, we show that adopting more antennas is more energy efficient for a given power budget. Finally, we show that the interference threshold has a significant effect on both the EE and the spectral efficiency at high-power regime.
    Citation
    Sboui L, Rezki Z, Sultan A, Alouini M-S (2016) Energy-Efficient Power Allocation for Cognitive MIMO Channels. 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall). Available: http://dx.doi.org/10.1109/vtcfall.2016.7880994.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)
    Conference/Event name
    84th IEEE Vehicular Technology Conference, VTC Fall 2016
    DOI
    10.1109/vtcfall.2016.7880994
    Additional Links
    http://ieeexplore.ieee.org/document/7880994/
    ae974a485f413a2113503eed53cd6c53
    10.1109/vtcfall.2016.7880994
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Electrical and Computer Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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