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

• Main Authors: Hussein Kobeissi, Youssef Nasser, Amor Nafkha, Oussama Bazzi, Yves Louet
• Format: Article
• Language: English
• Published: European Alliance for Innovation (EAI) 2017-05-01
• Series: EAI Endorsed Transactions on Cognitive Communications
• Online Access: http://eudl.eu/doi/10.4108/eai.31-5-2017.152554

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 offering dynamic and real-time computations. Simulation results show that the provided approximations are tightly matched to relative empirical ones.