Enhancing Spectral and Energy Efficiencies in a Cognitive Radio CRAN with PUEAs

Abstract

In a cloud radio access network, malicious users mitigate the attributes of primary users in order to occupy a specific idle spectrum band by sending false signals or carry out a denial of service attack. Moreover, with the increase in number of users and limited spectral and energy resources, the malicious users will compete for the spectrum with legitimate users, thus resulting in increase in spectrum scarcity problem. The most widely used defense approach against malicious users is the received signal strength method. However, harmful users can still imitate signal attributes and transmit powers of the primary users. Therefore, in order to elaborate the best method to tackle this vulnerability and hence make more spectrum available, the modified adaptive orthogonal matching pursuit localization algorithm is proposed to detect harmful users existing in the network. However, in order to elaborate the convergence speed of the proposed method, the regularized particle filter algorithm is applied to evaluate the performance of the modified adaptive orthogonal matching pursuit under real-time conditions. The restricted isometry property is used for the performance evaluation. Further, spectral and energy efficiencies are used in the simulation results for performance evaluation, in order to observe spectrum and energy utilization efficiencies. The simulation results show that the proposed method is better in terms of computational complexity, spectral efficiency, and energy efficiency compared to other matching pursuit approaches.