Performance evaluation of spectrum sensing techniques for cognitive radio systems

With the increase in utilization of portable devices and ever-growing demand for greater data rates in wireless transmission, an increasing demand for spectrum channels was observed since last decade. Currently, radio spectrum channels are assigned for quite long time periods to licensed subscribers who may not constantly use these bands, which leads to an inefficient use of the radio spectrum. The concept of cognitive radio technology was suggested to deal with the problem of spectrum scarcity caused by the underutilization of the radio spectrum. A cognitive radio system is able to sense its functional and geographical surrounding and adjust its operation. Therefore, cognitive radio operation has to be considered with intelligent sensing and smart decision-making methods. The aim of the paper is to examine and evaluate the three most fundamental spectrum sensing techniques i.e. energy detection-based, autocorrelation-based, and matched filter-based sensing. Simulation platforms were developed for each of the approaches using GNU radio and Python language.