Intelligent Spectrum Sharing and Sensing in Cognitive Radio Network by Using AROA (Adaptive Rider Optimization Algorithm)

Wireless spectrum has been allocated to licensees for large geographic areas on a long-term basis in recent years. Cognitive Radio Networks (CRN) will offer mobile users with a huge amount of available bandwidth. Due to spectrum management issues such as spectrum sensing and sharing, CRN networks pose some challenges. Hence in this paper, Adaptive Rider Optimization (AROA) is developed to improve the energy efficiency for different spectrum sensing conditions. The proposed algorithm is utilized to compute the sensing time, sequence length, and detection threshold. In order to detect the spectrum with optimal values of transmission power and sensing bandwidth, the AROA uses the adaptive threshold detection method. The spectrum sensing and sharing of the CRN network are achieved with the help of the AROA algorithm. The proposed method is implemented in MATLAB and the performances such as Normalized Energy consumption, delay, SNR, Jitter, blocking probability, convergence analysis, and Throughput are evaluated. The proposed method is contrasted with the existing methods such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), respectively.

Automated summary

Recently, wireless spectrum has been allocated to licensees for large geographic areas on a long-term basis. Cognitive Radio Networks (CRN) can provide mobile users with a significant amount of available bandwidth. However, CRN networks face challenges due to spectrum management issues such as spectrum sensing and sharing. In this paper, an Adaptive Rider Optimization (AROA) algorithm is proposed to enhance the energy efficiency for different spectrum sensing conditions. The AROA algorithm is utilized to compute the sensing time, sequence length, and detection threshold, and it uses an adaptive threshold detection method to detect the spectrum with optimal values of transmission power and sensing bandwidth. The AROA algorithm enables spectrum sensing and sharing in CRN networks. The proposed method is implemented in MATLAB, and various performance metrics such as Normalized Energy consumption, delay, SNR, Jitter, blocking probability, convergence analysis, and Throughput are evaluated. The proposed method is compared with existing methods like Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO).