Abstract
Cognitive Radio (CR) aims to achieve efficient utilization of scarcely available radio spectrum. Spectrum sensing in CR is a basic process for identifying the existence or absence of primary users. In spectrum sensing, CR users suffer from deep fading effects and it requires additional sensing time to identify the primary user. To overcome these challenges, we frame Spectrum Prediction-Channel Allocation (SP-CA) algorithm which consists of three phases. First, clustering mechanisms to select the spectrum coordinator. Second, Eigenvalue based detection method to expand the sensing accuracy of the secondary user. Third, Bayesian inference approach to reduce the performance degradation of the secondary user. The Eigenvalue based detection method is compared with Energy detection method in terms of varying false alarm rates and samples. The Eigenvalue detection method achieves better performance than Energy detection method. The Simulation results show that our approach gives better performance in terms of reducing sensing time and increasing sensing accuracy.
Highlights
Nowadays, the abundant growth of wireless applications requires radio spectrum resources
We proposed clustering algorithms to form the spectrum coordinator the spectrum coordinator identifies the primary user with Eigenvalue based detection method
Bayes inference approach is utilized to predict the channel quality based on inferred channel idle duration and spectrum, sensing accuracy
Summary
The abundant growth of wireless applications requires radio spectrum resources. The centralized authority is equipped with cognitive capabilities that identify the idle spectrum band and allocates it to the secondary users in accordance with a pre-defined policy. It acts as a coordinator and the remaining secondary users act as members. The information sharing between the coordinator and its members are spectrum sensing results, SNR (Signal to Noise Ratio) level and the present status of the secondary user. This type of network architecture is a suitable choice for effective dynamic spectrum management [4]. The Bayes method is utilized to predict the spectrum and the channel is allocated to the secondary user
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