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Article Dans Une Revue EAI Endorsed Transactions on Cognitive Communications Année : 2017

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

Résumé

Standard condition number (SCN) detector is a promising detector that can work efficiently 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.

Dates et versions

hal-01533171 , version 1 (06-06-2017)

Identifiants

Citer

Hussein Kobeissi, Youssef, Joseph Nasser, Amor Nafkha, Oussama Bazzi, Yves Louet. Asymptotic Approximation of the Standard Condition Number Detector for Large Multi-Antenna Cognitive Radio Systems. EAI Endorsed Transactions on Cognitive Communications, 2017, 3 (11), ⟨10.4108/eai.31-5-2017.152554⟩. ⟨hal-01533171⟩
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