Soft decision cooperative spectrum sensing with noise uncertainty reduction

Abstract

Cognitive radio (CR) is a potential strategy for opportunistic access of idle resources to solve the conflicts between spectrum scarcity and underutilization. Spectrum sensing (SS) constitutes the most critical part in CR systems since the CR needs to detect the presence of primary signals reliably and quickly. Energy detection (ED) based SS is considered as the most preferable SS technique due to its simplicity and applicability. However, it is influenced by the effect of noise uncertainty which highly degrades its sensing performance. Cooperative spectrum sensing (CSS) is also introduced to mitigate some sensing problems such as multipath fading, shadowing, and hidden node problems. In this paper, we propose an enhanced fusion center (FC) rule for soft decision CSS using ED, which highly alleviates the noise uncertainty effect and enhances the sensing performance of cognitive radio networks (CRNs). In the proposed fusion rule, to increase the probability of detection and decrease the probability of false alarm, two dynamic thresholds are utilized by the FC. These thresholds are toggled based on predicting the current activity of the primary user (PU), and their values are dynamically changed based on estimating the noise uncertainty factor of the collected energy measurements from the CRs. To effectively predict (estimate) the current PU activity (the noise uncertainty factor), simple successive averaging processes over the collected energy measurements (the estimated noise variances) are performed by the FC, respectively. Theoretical analysis is performed on the proposed fusion rule for soft decision CSS to evaluate its enhanced false alarm and detection probabilities using different data combining schemes. Performance evaluations are also investigated to confirm the theoretical claims and to prove the effectiveness of the proposed scheme over the conventional ED based soft decision CSS.