Detection of spectrum hole from n-number of primary users using machine learning algorithms

Abstract

A method for detecting spectrum holes based on the n-number of primary users (PU's) in a cognitive radio environment, using a cooperative spectrum sensing model is proposed in this study. The fusion centre, senses the n-number of PUs. When the number of PUs is >200, the probability of detection decreases, while the probability of a false alarm increases. The authors use the random forest (RF) algorithm to classify a customised dataset of 600 training samples. Further, they compare the RF algorithm and the k-means clustering algorithm, using test datasets with a minimum of ten PUs and a maximum of 500 PUs. Five different signal features are considered as the attributes in the proposed model. The maximum probability of detection is achieved using the k-means clustering algorithm in the case of 200 PUs and is 99.17%, while the false alarm probability is 0.8%. The receiver operating characteristic curves indicated that probability of detecting a spectrum hole in the case of the dataset with 500 PUs is 97.67% with the signal to noise ratio ranging from 10 to −12 dB. The accuracy can be increased if the number of clusters formed is increased, depending on the number of test samples.

Keywords

• cognitive radio
• signal detection
• radio spectrum management
• probability
• learning (artificial intelligence)
• spectrum hole
• primary users
• machine learning algorithms
• pu
• cognitive radio environment
• spectrum sensing model
• detection decreases
• false alarm increases
• random forest algorithm
• rf algorithm
• clustering algorithm
• 500 pus
• maximum probability
• 200 pus
• false alarm probability
• noise figure -12.0 db to 10.0 db