Detection of primary user´s signal in cognitive radio networks: Angle of Arrival based approach

Spectrum sensing is an essential process in cognitive radio networks. The majority of existing sensing approaches aim to detect the existence of a signal on a busy channel without differentiating whether a signal originates from a primary user or not. In this paper, we address this issue and propose to employ an Angle of Arrival (AoA) based sensing approach to effectively distinct a primary user´s signal from a secondary user´ signal. Multiple Signal Classification (MUSIC), a classical AoA algorithm, is selected due to easy implementation and high resolution. Unlike the previous works on AoA based sensing, we thoroughly investigate its sensing performance based on a practical model which captures typical characteristics in cognitive radio networks. Two performance metrics named false alarm probability and miss detection probability are theoretically analyzed. Closed-form analytical expressions are derived for both metrics. Extensive simulations are carried out under various scenarios to evaluate AoA based sensing approach.