Optimized Complexity Problem Based on Combined Fully Blind Self Adapted Method using PSO for Cognitive Radio Spectrum Sensing
Rakesh Singh Rajput1, Rekha Gupta2, Aditya Trivedi3
1Rakesh Singh Rajput, Madhav Institute of technology and Science, Gwalior, (MP), India.
2Rekha Gupta, Madhav Institute of technology and Science, Gwalior, (MP), India.
3Aditya Trivedi, ABV Indian Institute of Information technology and Management, Gwalior, (MP), India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3879-3883 | Volume-8 Issue-6, March 2020. | Retrieval Number: D7199118419/2020©BEIESP | DOI: 10.35940/ijrte.D7199.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The main purpose of this paper is to solve the complexity problem and improved existing methods such as energy detection, Maximum-Minimum Eigen value detectors (MME), MME with blind two stage detector and adaptive covariance threshold method. PSO algorithm stands for Particle Swarm Optimization is utilized to novelty the optimal sensing time at that there will be maximum detection of probability for the Primary user. In which, we compare the optimal-PSO algorithm with non-optimal method. The simulation of the paper is depend on the performance of probability of detection based on the false alarm rate on the different region of the signal to noise ratio. The proposed optimized method of detector shows a superior performance values when compared to three individual detectors. The performance metrics of proposed method are performed better than other three individual detector.
Keywords: Blind Sensing, Energy Detector, Maximum– Minimum Eigenvalue(MME) Detector, PSO.
Scope of the Article: Cognitive Radio Networks.