A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks  

作　　者：Nada M.Elfatih Elmustafa Sayed Ali Maha Abdelhaq Raed Alsaqour Rashid A.Saeed 

机构地区：[1]Department of Electrical and Electronics Engineering,Red Sea University,Port Sudan,33311,Sudan [2]Department of Information Technology,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,Riyadh,11671,Saudi Arabia [3]Department of Information Technology,College of Computing and Informatics,Saudi Electronic University,Riyadh,93499,Saudi Arabia [4]Department of Computer Engineering,College of Computers and Information Technology,Taif University,Taif,21944,Saudi Arabia [5]Department of Electronics Engineering,Sudan University of Science and Technology,Khartoum,11111,Sudan

出　　处：《Computer Systems Science & Engineering》2023年第4期329-342,共14页计算机系统科学与工程（英文）

基　　金：This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

关 键 词：Cognitive radio spectrum sensing energy detection double threshold neural network machine learning OPTIMIZATION quality of service 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]