TY - GEN
T1 - Sequential spectrum sensing based on higher-order statistics for cognitive radios
AU - Chang, Han Kui
AU - Lin, Jia Chin
AU - Ku, Meng Lin
PY - 2012
Y1 - 2012
N2 - Spectrum sensing is a crucial technique used to discover available bands that are not occupied by primary users in cognitive networks (CNs). With good sensing capability in terms of low probability of a miss occurrence, secondary users can effectively recycle the spectrum resource without disturbing active primary users. With low probability of a false alarm occurrence, spectral utilization may be relatively simple spectrum sensing technique. In practice, a cognitive radio (CR) receiver has to operate at low signal-to-noise ratio (SNR) regimes because of channel fades and noise. Therefore, low SNR inevitably degrades the performance of ED dramatically. In this paper, a sequential test detector based on higher-order statistics (HOS) is investigated to conduct effective spectral sensing, especially in low-SNR environments. By taking advantage of cumulant statistics, spectrum sensing reliability can be significantly improved as Gaussian noise can be overwhelmed. Based on binary hypothesis testing, a low-complexity sequential probability ratio test (SPRT) is thus developed for effectively detecting the vacant spectrum to meet the requirements of the sensing duty cycle. Simulation results show that the proposed detector outperforms conventional ED, especially in low SNR environments.
AB - Spectrum sensing is a crucial technique used to discover available bands that are not occupied by primary users in cognitive networks (CNs). With good sensing capability in terms of low probability of a miss occurrence, secondary users can effectively recycle the spectrum resource without disturbing active primary users. With low probability of a false alarm occurrence, spectral utilization may be relatively simple spectrum sensing technique. In practice, a cognitive radio (CR) receiver has to operate at low signal-to-noise ratio (SNR) regimes because of channel fades and noise. Therefore, low SNR inevitably degrades the performance of ED dramatically. In this paper, a sequential test detector based on higher-order statistics (HOS) is investigated to conduct effective spectral sensing, especially in low-SNR environments. By taking advantage of cumulant statistics, spectrum sensing reliability can be significantly improved as Gaussian noise can be overwhelmed. Based on binary hypothesis testing, a low-complexity sequential probability ratio test (SPRT) is thus developed for effectively detecting the vacant spectrum to meet the requirements of the sensing duty cycle. Simulation results show that the proposed detector outperforms conventional ED, especially in low SNR environments.
UR - http://www.scopus.com/inward/record.url?scp=84867345963&partnerID=8YFLogxK
U2 - 10.1109/ICCITechnol.2012.6285837
DO - 10.1109/ICCITechnol.2012.6285837
M3 - 會議論文篇章
AN - SCOPUS:84867345963
SN - 9781467319508
T3 - International Conference on Communications and Information Technology - Proceedings
SP - 413
EP - 417
BT - 2012 International Conference on Communications and Information Technology, ICCIT 2012
T2 - 2012 International Conference on Communications and Information Technology, ICCIT 2012
Y2 - 26 June 2012 through 28 June 2012
ER -