Crowdsensing Enabled Dynamic And Collaborated Spectrum Sensing Technology

With the explosion of smart user equipment and the rapid growth of service demand,wireless communication networks need to support various applications through limited spectrum resources.How to timely and accurately discover available spectrum opportunities and maximize spectrum utilization is important for improving the mobile network's efficiency.Data rate and quality of service are crucial.Cooperative spectrum sensing can detect spectrum opportunities in time and accurately through mutual cooperation between mobile users and access them in a timely manner.It is a key technology in a spectrum sharing system.In order to obtain good spectrum sensing performance,a sufficient number of user terminals are required to cooperate.Especially in B5 G networks,ultra-high frequency signals are more easily attenuated during propagation,which makes deep fading and hidden terminal problems more serious.In addition,the dense deployment of small cells and the heterogeneous distribution of spectrum resources have aggravated the difficulty of spectrum sensing,and also highlighted the importance of multi-terminal cooperation in dynamic spectrum sensing.Current researches on cooperative spectrum sensing assumes that there are a sufficient number of secondary users in the sensing system to participate in collaborative sensing.However,in reality,considering the large cost of sensing and data reporting,selfish and rational secondary users do not need to contact themselves.In the case of entering the frequency spectrum for data transmission,it will not actively participate in the cooperative spectrum sensing voluntarily,and the insufficient number of cooperative users will directly lead to a sharp drop in the cooperative sensing performance.Therefore,how to incentivize enough user equipment to participate in cooperative sensing and improve the dynamics and flexibility of cooperative spectrum sensing are key challenges for 5G networks to achieve high spectrum efficiency.As a new type of sensing paradigm,crowdsensing can use ubiquitous and widely distributed intelligent mobile user devices as candidate sensing users,and applying it to dynamic cooperative spectrum sensing scenarios can ensure the improvement of cooperative spectrum sensing performance.In addition,the dynamic spectrum sensing mechanism based on the collaborated crowdsensing also has high flexibility.It can be dynamically adapted to The constantly changing channel conditions in 5G networks,by adjusting the parameters of cooperative sensing,such as the number of perceived users,the location of cooperative users,the sensing period,and the accuracy of sensing data,thus is with broad application prospects.This dissertation conducts an in-depth study on the dynamic spectrum sensing mechanism based on the collaborated crowdsensing,and proposes the incentive mechanisms based on monetary incentive and social incentive,respectively.For specific dynamic spectrum sensing scenarios and from the perspective of crowdsensing users,the sensing time is optimized and the corresponding privacy protection mechanism is designed;from the perspective of sensing requestors,in order to maximize the global throughput,their transmission power has been jointly optimized.In addition,considering the consumption of energy and limited network resources in the process of crowd spectrum sensing and data reporting,backscatter communication and air computing are used to achieve an optimal balance between cooperative perception performance and resource consumption.The main research contents and innovations of this dissertation are as follows:(1)In order to motive enough users to participate in the crowdsensing enabled cooperative spectrum sensing,a Stackelberg game based incentive mechanism is proposed where the fusion center is leader and mobile sensing users are followers.In Leader game,the fusion center provides an optimal reward to incentive sensing users.And in Follower game,mobile sensing users adjust their own detection probabilities for obtaining more profits when given the reward published by the fusion center.Furthermore,the strategy adjusting process for each sensing user is modeled as a Coalition game where sensing users decide whether join in a coalition based on its utility maximization.We prove the existence of unique Nash Equilibrium in both Follower game and Leader game.Extensive simulations evaluate the feasibility and practically of proposed incentive mechanism.And the results show that a better detection performance can be obtained by the fusion center in our algorithm.(2)A social incentive mechanism is adapted to motivate the cooperative spectrum-sensing participations of mobile crowdsensing users.In particular,we model the interactions among the motivated sensing users as a co-operative game where they adjust their own sensing time strategies to maximize the co-operative sensing utility,which eventually guarantees the detection performance and prevents the global sensing cost being too high.We prove that the game based optimization problem is NP-hard and exists a unique optimal equilibrium.An improved differential evolution algorithm is proposed to solve the optimization problem.Simulation results prove the better performance in our proposed multi-user sensing time optimization model and the proposed improved differential evolution algorithm,respectively compared with the non-optimization model and the other two typical equilibrium solution algorithms.(3)Aiming at achieving a maximal spectrum utilization,we propose a privacy-aware crowdsourced spectrum sensing and multi-user sharing mechanism for dynamic spectrum sensing.Particularly,in the sensing stage,the advanced mobile crowd sensing is adapted to economically provide sufficient candidate sensing helpers for a sensing requestor.Considering the individual rationality and energy consumption,an incentive mechanism based on both monetary and social motivation is designed to motivate the final participations of the sensing helpers.Moreover,with the increasing attention to individual privacy,a social network and location-based k-anonymity grouping algorithm are proposed to prevent each helper's privacy being attacked by the malicious requestor or mobile users.Then,for a sensing requestor,aiming at achieving a target detection performance with minimal payment,a truthful reverse auction-based winning group selection algorithm is designed.Furthermore,in the transmitting stage,a realistic scenario is considered where multiple transmitters may discover the same idle spectrum based on sensing helpers' detections and will transmit data simultaneously.Thus,we model this problem as a potential-game-based multi-user transmission mechanism where all the transmitters act as game players and will jointly adjust their transmission powers to maximize the global throughput.Accordingly,we take advantage of an improved differential evolution algorithm for obtaining a better equilibrium solution in a decentralized way.Both the theoretical analysis and the simulation results prove the rationality and superiority of our proposed algorithms.(4)In order to reduce the consumption of energy and limited network resources in the crowdsensing based cooperative spectrum sensing,we propose a green crowd spectrum sensing scheme by jointly utilizing the ambient backscatter communication and the air computation.In this scheme,the ambient backscatter communication enables each device to directly backscatter the received spectrum signal without performing sensing process,and the energy for backscattering can be provided by ambient radio frequency sources.Then utilizing the air computation,all the backscattered spectrum signals can be airfused over backscattering link by taking advantage of the summation property of wireless channel.The proposed scheme enables a battery-less fashion and benefits the conservation of limited network resources.The data fusion over wireless channel further facilitates the timeliness and computation efficiency.This dissertation details the proposed green crowd spectrum sensing scheme and derives the closed-form expressions of the crowd detecting performance metrics.Comprehensive simulation results verify the theoretical derivation and demonstrate the superior properties of the proposed green crowd spectrum sensing scheme.