cogcom 17(11): e1

Research Article

Asymptotic Approximation of the Standard Condition Number Detector for Large Multi-Antenna Cognitive Radio Systems

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  • @ARTICLE{10.4108/eai.31-5-2017.152554,
    author={Hussein Kobeissi and Youssef Nasser and Amor Nafkha and Oussama Bazzi and Yves Louet},
    title={Asymptotic Approximation of the Standard Condition Number Detector for Large Multi-Antenna Cognitive Radio Systems},
    journal={EAI Endorsed Transactions on Cognitive Communications},
    volume={3},
    number={11},
    publisher={EAI},
    journal_a={COGCOM},
    year={2017},
    month={5},
    keywords={},
    doi={10.4108/eai.31-5-2017.152554}
}
  • Hussein Kobeissi
    Youssef Nasser
    Amor Nafkha
    Oussama Bazzi
    Yves Louet
    Year: 2017
    Asymptotic Approximation of the Standard Condition Number Detector for Large Multi-Antenna Cognitive Radio Systems
    COGCOM
    EAI
    DOI: 10.4108/eai.31-5-2017.152554
Hussein Kobeissi1,2,*, Youssef Nasser3, Amor Nafkha1, Oussama Bazzi2, Yves Louet1
  • 1: SCEE/IETR, CentraleSupélec - Campus de Rennes, Rennes, France.
  • 2: Department of Physics and Electronics, Faculty of Science 1, Lebanese University, Beirut, Lebanon.
  • 3: ECE Department, AUB, Bliss Street, Beirut, Lebanon.
*Contact email: hussein.kobeissi.87@gmail.com

Abstract

Standard condition number (SCN) detector is a promising detector that can work eÿciently in uncertain environments. In this paper, we consider a Cognitive Radio (CR) system with large number of antennas (eg. Massive MIMO) and we provide an accurate and simple closed form approximation for the SCN distribution using the generalized extreme value (GEV) distribution. The approximation framework is based on the moment-matching method where the expressions of the moments are approximated using bi-variate Taylor expansion and results from random matrix theory. In addition, the performance probabilities and the decision threshold are considered. Since the number of antennas and/or the number of samples used in the sensing process may frequently change, this paper provides simple form decision threshold and performance probabilities o ering dynamic and real-time computations. Simulation results show that the provided approximations are tightly matched to relative empirical ones.

Received
2016-05-17
Accepted
2017-05-07
Published
2017-05-31
Publisher
EAI
http://dx.doi.org/10.4108/eai.31-5-2017.152554

Copyright © 2017 H. Kobeissi et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.