@article{MAKHILLJEAS2017121614749,
    title = {Use of Artificial Intelligence Based Models to Estimate the
Use of a Spectral Band in Cognitive Radio},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {16},
    pages = {4259-4266},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.4259.4266},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.4259.4266},
    author = {Danilo,Edwin and},
    keywords = {ANFIS,cognitive radio,prediction primary user,RNA,licensed spectrum,optimal},
    abstract = {Currently one of the major challenges in wireless networks is the optimal use of the radio spectrum
as most researcher agree that the licensed frequency band is not in use most of the time. There has been a large
amount of research in this area that converges in the use of Cognitive Radio (CR) as an essential parameter so
that the use of the available licensed spectrum is possible (by secondary users) well above the usage values
that are currently detected; thus allowing the opportunistic use of the channel in the absence of Primary Users
(PU). This study presents the results found when estimating or predicting the future use of a spectral
transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series
prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The
results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The
results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.}
    }