@Article{cmes.2020.07861,
AUTHOR = {Yanli Ji, Weidong Wang, Yinghai Zhang},
TITLE = {Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {122},
YEAR = {2020},
NUMBER = {2},
PAGES = {691--701},
URL = {http://www.techscience.com/CMES/v122n2/38326},
ISSN = {1526-1506},
ABSTRACT = {In view of the uncertainty of the status of primary users in cognitive networks 
and the fact that the random detection strategy cannot guarantee cognitive users to 
accurately find available channels, this paper proposes a joint random detection strategy 
using the idle cognitive users in cognitive wireless networks. After adding idle cognitive 
users for detection, the compressed sensing model is employed to describe the number of 
available channels obtained by the cognitive base station to derive the detection 
performance of the cognitive network at this time. Both theoretical analysis and 
simulation results show that using idle cognitive users can reduce service delay and 
improve the throughput of cognitive networks. After considering the time occupied by 
cognitive users to report detection information, the optimal participation number of idle 
cognitive users in joint detection is obtained through the optimization algorithm.},
DOI = {10.32604/cmes.2020.07861}
}