TY - GEN
T1 - Optimal decision threshold for eigenvalue-based spectrum sensing techniques
AU - He, Yibo
AU - Ratnarajah, Tharmalingam
AU - Xue, Jiang
AU - Yousif, Ebtihal H.G.
AU - Sellathurai, Mathini
PY - 2014
Y1 - 2014
N2 - This paper investigates optimization of the sensing threshold that minimizes the total error rate (i.e., the sum of the probabilities of false alarm and missed detection) of eigenvalue-based spectrum sensing techniques for multiple-antenna cognitive radio networks. Four techniques are investigated, which are maximum eigenvalue detection (MED), maximum minimum eigenvalue (MME) detection, energy with minimum eigenvalue (EME) detection, and the generalized likelihood ratio test (GLRT) detection. The contribution of this paper is of four parts. Firstly, we present the derivative of the matrix-variate confluent hypergeometric function, which is required for the MED case. Secondly, we derive the probabilities of false alarm for both cases MME and EME detection. Thirdly, we derive the probability of missed detection for the GLRT detector. Finally, we provide the exact expressions required to obtain the optimal sensing thresholds for all cases. The simulation results reveal that for all the investigated cases the chosen optimal sensing thresholds achieve the minimum total error rate.
AB - This paper investigates optimization of the sensing threshold that minimizes the total error rate (i.e., the sum of the probabilities of false alarm and missed detection) of eigenvalue-based spectrum sensing techniques for multiple-antenna cognitive radio networks. Four techniques are investigated, which are maximum eigenvalue detection (MED), maximum minimum eigenvalue (MME) detection, energy with minimum eigenvalue (EME) detection, and the generalized likelihood ratio test (GLRT) detection. The contribution of this paper is of four parts. Firstly, we present the derivative of the matrix-variate confluent hypergeometric function, which is required for the MED case. Secondly, we derive the probabilities of false alarm for both cases MME and EME detection. Thirdly, we derive the probability of missed detection for the GLRT detector. Finally, we provide the exact expressions required to obtain the optimal sensing thresholds for all cases. The simulation results reveal that for all the investigated cases the chosen optimal sensing thresholds achieve the minimum total error rate.
KW - Cognitive radio
KW - eigenvalue-based spectrum sensing
KW - matrix-variate confluent hyper-geometric function
KW - optimal decision threshold
UR - https://www.scopus.com/pages/publications/84905281163
U2 - 10.1109/ICASSP.2014.6855105
DO - 10.1109/ICASSP.2014.6855105
M3 - 会议稿件
AN - SCOPUS:84905281163
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 7734
EP - 7738
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -