Abstract
In cognitive radio networks (CRNs), secondary users (SUs) can access vacant spectrum licensed to a primary user (PU). Therefore, accurate and timely spectrum sensing is vital for efficient utilization of available spectrum. The sensing result at each SU is unauthentic due to fading, shadowing, and receiver uncertainty problems. Cooperative spectrum sensing (CSS) provides a solution to these problems. In CSS, false sensing reports at the fusion center (FC) received from malicious users (MUs) drastically degrade the performance of cooperation in PU detection. In this paper, we propose a robust spectrum sensing scheme to minimize the effects of false sensing reports by MUs. The proposed scheme focuses on double-sided neighbor distance (DSND) based on genetic algorithm (GA) in order to filter out the MU sensing reports in CSS. The simulation results show that the sensing results are more accurate and reliable for the proposed GA majority-voting hard decision fusion (GAMV-HDF) and GA weighted soft decision fusion (GAW-SDF) compared to conventional equal gain combination soft decision fusion (EGC-SDF), maximum gain combination soft decision fusion (MGC-SDF), and majority-voting hard decision fusion (MV-HDF) schemes in the presence of MUs.
Highlights
Rapid developments in wireless communication system demand new wireless services in both used and unused parts of electromagnetic spectrum [1]. e underutilization of the spectrum fallout in spectrum holes representing the frequency band assigned to a legitimate primary user (PU), but it is not utilized by the PU at certain time and specific geographical locations. e motivation to introduce cognitive radio technology is increasing demands for higher data rates under underutilized spectral scarcity issues [2,3,4]
To solve the spectrum scarcity issues, federal communications commission (FCC) permits secondary users (SUs) to dynamically utilize the spectrum in different services or even to lease the spectrum to a third party [5, 6]. e cognitive radio network (CRN) consists of an intelligent wireless communication system embedded with key functionalities to provide seamless communications at all times and all geographical places based on the needs with proficient utilization of the spectrum resources [7]
In the proposed double-sided neighbor distance (DSND) algorithm, history log is developed against the reporting SUs at the fusion center (FC) to filter out any abnormal SU from the global decision by computing the distance of each SU with its neighbors. e fitness function is based on the absolute sum of the Hamming distances of the individuals with the sensing reports provided by all other SUs
Summary
Rapid developments in wireless communication system demand new wireless services in both used and unused parts of electromagnetic spectrum [1]. e underutilization of the spectrum fallout in spectrum holes representing the frequency band assigned to a legitimate primary user (PU), but it is not utilized by the PU at certain time and specific geographical locations. e motivation to introduce cognitive radio technology is increasing demands for higher data rates under underutilized spectral scarcity issues [2,3,4]. E motivation to introduce cognitive radio technology is increasing demands for higher data rates under underutilized spectral scarcity issues [2,3,4]. To solve the spectrum scarcity issues, federal communications commission (FCC) permits secondary users (SUs) to dynamically utilize the spectrum in different services or even to lease the spectrum to a third party [5, 6]. E cognitive radio network (CRN) consists of an intelligent wireless communication system embedded with key functionalities to provide seamless communications at all times and all geographical places based on the needs with proficient utilization of the spectrum resources [7]. One of major issues in CRN is to properly detect the status of PU channel. Cooperative spectrum sensing (CSS) performs well in fading and shadowing environments, where multiple radios provide an independent realization of related random variable in the course of distributed transmission [8,9,10]. e
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