Comparative Analysis Of Cyclostationary Feature Detection Techniques For Spectrum Sensing In Cognitive Radio Networks

Abstract

Modern wireless communication systems place a great deal of emphasis on the efficient use of radio frequency spectrum. Cognitive Radio networks (CR) have emerged as a promising solution to address spectrum scarcity by enabling opportunistic access to underutilized spectrum bands. In order to avoid interference, spectrum sensing, an integral part of CR, is based on detecting the presence of primary users. As part of this study, we present a comprehensive comparative analysis of the effectiveness of cyclostationary feature detection techniques for the detection of spectrum features in cognitive radio networks. Cyclostationary analysis takes advantage of the periodic properties of signals and noises to enhance the detection capability. In this paper, prominent cyclostationary-based methods, such as cyclic autocorrelation, cyclic periodogram and spectral correlation density, are examined in detail to determine their performance, robustness, and computational complexity. As a result of real-world scenarios and simulation results, it is possible to gain insight into the strengths and limitations of these techniques, thereby assisting in the selection of the optimal spectrum sensing strategy for cognitive radio networks