Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks

The proliferation of wireless mobile devices has led to a number of challenges in mobile data communication. The world is experiencinganincreasingusage of finite spectrum bands for social media and other data communication services. It is due to this high usage that the Federal Communications Commission(FCC) sought to open up some spectrum bands to be used opportunistically by secondary users (SUs). However, the coexistence of Primary Users (PUs) and SUs may cause interference which leads to wastage of spectrum resources. This study investigates the impact of interferences between PUs and SUs. To ensure higher detection of PU signal, a cooperative rule was used to decide which SU to share and makea final decision about the availability of the spectrum band. To maximize the throughput of SU, a maximum likelihood function was designed to reduce delays in searching for the next available channel for data transmission. To discover more transmission opportunities and ensuring that a good number of free channels are detected, a parallel sensing technique was employed. Matlabwas used to simulate and generate the results in a distributed cognitive radio environment. The proposed extended generalizedpredictive channel selection algorithm (EXGPCSA) outperformed otherschemes in literature in terms of throughput, service timeandprobability of detection.