Spectrum-Awareness-Based Performance and Scalability of Cognitive Radio Networks
Spectrum awareness (SA) stretches the performance bounds of spectrum-sensing-based dynamic spectrum access by intelligently exploiting the big spectrum data (BSD) generated by a network. Hence, in order to analyze the performance and scalability of large scale cognitive radio networks (CRNs), spectrum awareness capacity would take preeminence over spectrum sensing capacity. Although, conventional methods use techniques such as the receiver operating characteristic (ROC) curve and the root mean square error (RMSE) technique to quantify the performance of CRN SA, they do not consider the impact of BSD velocity, variety and volume. Therefore, this research work proposes a novel knowledge-centric method for quantifying, analyzing and comparing the performance and scalability of CRNs based on spectrum awareness. The proposed method considers key performance indices including reliability, computational complexity and latency of the network parameters that are generated by spectrum data acquisition, conversion and dissemination. The steps and applicability of the proposed method in user and network-level performance measurement are also analyzed.