Comparative Analysis of Primary Transmitter Detection Based Spectrum Sensing Techniques in Cognitive Radio Systems

A key component in cognitive radios systems is the spectrum sensing. Cognitive radios are increasingly being considered to overcome the shortage and inefficiency of current static spectrum allocations. Spectrum sensing involves the detection of the primary user transmissions on a pre-assigned frequency band. If this particular band is not currently in use, it may be assigned to another user for certain duration of time. There are three main techniques for spectrum sensing: cooperative detection, primary transmitter detection and interference based detection. The focus of this work is on the analysis of an important class of spectrum sensing methods known as the transmitter detection. Generally transmitter detection includes three techniques: energy detection, matched filter detection and cyclostationary feature detection. Using simulations, a comparative analysis of the three techniques has been carried out in terms of probability of primary user detection and probability of false detection. It has been found that cyclostationary feature detection out performs the other techniques as it needs about 16-24 dBs lower SNR to achieve comparable performance levels