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