1.1. Background
The limited accessible spectrum and the inefficiency of the licensed spectrum usage demand a novel communication pattern to abuse the present wireless spectrum opportunistically. Cognitive radio (CR) suggests a new solution to overcome the underutilization problem by allowing an opportunistic usage of the spectrum resources (Yucek & Arslan, 2009).
Spectrum sensing (SS) is considered as the basic component of cognitive radio establishing. By definition, spectrum sensing is the mission of obtaining awareness about the spectrum usage by primary users in a geographical area (Yucek & Arslan, 2009). There are many SS techniques such as energy detector (ED), matched filter (MF), cylostationary detection technique (CSD), and eigenvalues based detection. These techniques differ from each other in term of latency, complexity, reliability, and the need for prior information. Each technique has advantages, disadvantages, and limitations. For example, the fast and low complex energy detector is paid by its low performance in low SNR values. In addition, eigenvalues based detection methods provide high sensing accuracy that is paid by high complexity and long detection times (Mashta, Altabban, & Wainakh, 2019).
Maximum eigenvalues detection (MED) technique provides better detection performance than the others eigenvalues techniques like maximum-minimum eigenvalues (MME) (Zeng, Koh, & Liang, 2008). In addition, MED technique is very accurate at low SNRs. However, it requires high computational complexity. The combination of ED and MED techniques makes use of the advantages of each detector in each stage as it exploits the speed of detection of ED at high SNRs and good detection performance of MED at low SNRs (Mashta, Altabban, &Wainakh, 2020). Furthermore, both techniques do not need any prior information about PU' transmission, and they do not require accurate synchronization. However, they are subject to noise uncertainty due to dependence of the test statistics on the noise power (Zeng, Koh, & Liang, 2008).
Multistage SS has been proposed in several literature in order to improve the performance and overcome the limitations of traditional spectrum sensing techniques (Gunichetty, Hiremath, & Patra, 2015; Anaand & Charan, 2016; Kanti, Tomar, &Bagwari, 2017; Latha, Gohain, & Chaudhari, 2018; Mourougayane, Amgothu, Bhagat, & Srikanth, 2019). The first driving feature for the multistage detector is the SNR. When the SNR values are high, the simplicity of the first stages is the main advantage. However, when the SNR goes down, it require more sensing accuracy. Therefore, more accurate detectors with a price of their complication are needed in higher stages to achieve better sensing accuracy (Hamid, Bjorsell, & Ben Slimane, Feb 2016).