Improving Spectrum Sensing Under Impact of Noise Uncertainty Factor to Detect Primary User Traffic for Cognitive Radio System

Abstract

Nowadays, users of wireless communication networks are facing a challenge and a shortage in allocating the radio spectrum, as a result of the rapid and widespread development in the wireless field. A major cause of this scarcity is the reliance on the strategy and policy of distribution or fixed allocation of the radio spectrum, as it is a natural and limited source. Cognitive radio is the best system to deal with the scarcity of the radio spectrum at the present time, as the idea of this system is to allow secondary or unauthorized users to use the spectrum in times it is not used by the primary of authorized users. This use is made conditionally without affecting the original users. The spectrum sensing function is one of the most important functions of the cognitive radio system and is the essence of its work. The traditional methods have proven ineffective in sensing and detecting the dynamic primary user, due to its assumptions that either the primary user is totally present or completely absent, as well as neglecting the reuse of the spectrum again. The main objective of this research is to suggest an algorithm to improve the detection process and reduce sensing time. The efficiency of spectrum sensing is improved through the use of a dynamic threshold. The new work in this scheme is the modeling of the primary dynamic user signal called Activity period (A) which presents a more realistic modeling and dynamic threshold together. The signal-to-noise ratio was used low to some extent, this is to ensure the quality of communication services. Consequently, the simulation results clearly prove that the proposed scheme achieves and meets the sensing enhancing requirements much better than many other sensing technologies.