6,707 research outputs found
Experimental detection using cyclostationary feature detectors for cognitive radios
© 2014 IEEE. Signal detection is widely used in many applications. Some examples include Cognitive Radio (CR) and military intelligence. Without guaranteed signal detection, a CR cannot reliably perform its role. Spectrum sensing is currently one of the most challenging problems in cognitive radio design because of various factors such as multi-path fading and signal to noise ratio (SNR). In this paper, we particularly focus on the detection method based on cyclostationary feature detectors (CFD) estimation. The advantage of CFD is its relative robustness against noise uncertainty compared with energy detection methods. The experimental result present in this paper show that the cyclostationary feature-based detection can be robust compared to energy-based technique for low SNR levels
Deep Learning Meets Cognitive Radio: Predicting Future Steps
Learning the channel occupancy patterns to reuse
the underutilised spectrum frequencies without interfering with
the incumbent is a promising approach to overcome the spectrum
limitations. In this work we proposed a Deep Learning (DL)
approach to learn the channel occupancy model and predict its
availability in the next time slots. Our results show that the
proposed DL approach outperforms existing works by 5%. We
also show that our proposed DL approach predicts the availability
of channels accurately for more than one time slot
Analysis of Spectrum Occupancy Using Machine Learning Algorithms
In this paper, we analyze the spectrum occupancy using different machine
learning techniques. Both supervised techniques (naive Bayesian classifier
(NBC), decision trees (DT), support vector machine (SVM), linear regression
(LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to
find the best technique with the highest classification accuracy (CA). A
detailed comparison of the supervised and unsupervised algorithms in terms of
the computational time and classification accuracy is performed. The classified
occupancy status is further utilized to evaluate the probability of secondary
user outage for the future time slots, which can be used by system designers to
define spectrum allocation and spectrum sharing policies. Numerical results
show that SVM is the best algorithm among all the supervised and unsupervised
classifiers. Based on this, we proposed a new SVM algorithm by combining it
with fire fly algorithm (FFA), which is shown to outperform all other
algorithms.Comment: 21 pages, 6 figure
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