Research On User Selection Algorithm Of Crowd Spectrum Sensing

With the rapid growth of mobile terminal equipment,people’s demand for limited spectrum resources is increasing.However,the traditional spectrum allocation mode is static,which leads to a low utilization rate of spectrum resources.Cognitive radio technology uses spectrum sensing technology to search spectrum holes and dynamically accessed users to realize spectrum sharing,which greatly improves the efficiency of spectrum resources.By monitoring the radio signals of a certain frequency band,the illegal signals can be identified to avoid the phenomenon of illegal use of frequency band,which provides the basis for spectrum management.A reasonable incentive mechanism in the crowd spectrum sensing can attract a large number of users to participate in the sensing,improve the performance of cooperative spectrum sensing,and more accurate sensing results.This thesis mainly studies the crowd spectrum sensing user selection algorithm.Aiming at the problems of limited coverage and high cost of traditional radio spectrum monitoring methods,a crowd spectrum monitoring algorithm based on two-stage user selection is proposed.Aiming at the problem of payment from sensing requirement secondary user to cooperative sensing secondary user,a multitask-oriented spectral sensing game algorithm is proposed.Aiming at the sensing problem of spatial spectrum intensity distribution,an auction-based spatial user selection method for two-dimensional and three-dimensional spectrum monitoring is designed.The main research contents and achievements of this thesis are as follows:(1)Aiming at the problems of limited coverage and high cost of traditional radio spectrum monitoring methods,a spectral monitoring algorithm based on two stage user selection is proposed.In the first stage,the algorithm selects a group of opportunity users from the total user set and completes the sensing task in the daily life route.In the second stage,a group of participating users is selected from the set of remaining users.It needs to change the original activity route and move to a specific area to complete the sensing task that the winning users in the first stage have not completed.The purpose of this algorithm is to maximize the number of tasks completed by selecting the winning user in two stages under a limited budget.The simulation results show that the algorithm can significantly improve the number of tasks completed.(2)Aiming at the problem of payment from sensing requirement secondary user to cooperative sensing secondary user,a multitask-oriented spectral sensing game algorithm is proposed.In this algorithm,the problem of payment from the sensing requirement secondary user to the cooperative sensing secondary user is modeled as the Stackelberg game model,where the former is the leadership in the game model,and the latter is the subordinate layer in the game model.In the leadership game,the utility of the sensing requirement secondary user is defined by considering the detection probability and reward comprehensively,and the optimal utility is obtained by optimizing the reward through the game.In the subordinate layer game,the utility of cooperative sensing secondary user is defined by considering the detection probability and sensing time comprehensively,and the sensing time is optimized according to the rewards released by sensing requirement secondary user to obtain the best utility.Moreover,it is deduced that there is a Nash equilibrium in the optimization of sensing time.The simulation results show that this algorithm can improve the detection probability of cooperative spectrum sensing.(3)Aiming at the sensing problem of spectrum intensity distribution in two-dimensional and three-dimensional space,a user selection algorithm based on auction for two-dimensional and three-dimensional spectrum monitoring is designed.When users are required to publish tasks monitoring wireless signal strength through the monitoring platform,in order to attract a large number of users to participate in the sensing task,a reasonable incentive mechanism is designed to compensate the cost paid by the sensing users to participate in the task,and the winning sensing users are selected based on the comprehensive consideration of distance and quotation.The objective of the monitoring platform is to select sensing users that are far away from each other as far as possible under the premise of not exceeding the budget,reduce the possibility of overlapping sensing data of sensing users,and make the collected sensing data have a larger spatial coverage.Simulation results show that the proposed algorithm can improve the spatial coverage of sensing data and obtain more accurate two-dimensional and three-dimensional spectral intensity distribution.