Pattern recognition (psychology)

Wireless

Computer science

Cognitive radio

Firefly algorithm

Unsupervised learning

Machine learning

Telecommunications

Particle swarm optimization

Classifier (UML)

Algorithm

Support vector machine

Artificial intelligence

Artificial neural network

Decision tree

Supervised learning

Statistical classification

Hidden Markov model

Naive Bayes classifier

IEEE Transactions on Vehicular Technology

In this paper, we analyze the spectrum occupancy in cognitive radio networks (CRNs) using different machine learning techniques. Both supervised techniques [naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)] and unsupervised algorithms [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 the CA is performed. The classified occupancy status is further utilized to evaluate the blocking probability of secondary user for 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 a firefly algorithm (FFA), which is shown to outperform all the other algorithms.

Analysis of Spectrum Occupancy Using Machine Learning Algorithms