Access Selection Models And Methods Of Cognitive Network Based On Multi-Attributes Decision-Making

With the rapid development of wireless network technology and mobile communication technology,a variety of new cognitive network technologies have been emerging.Cognitive networks present the characteristics of complexity,heterogeneity,intelligence and selectivity.At the same time,intelligent devices often have multiple network interfaces and can access different types of networks.In this cognitive heterogeneous network environment,how to make the network selection decision or handover decision automatically has become a hot and difficult problem in the cognitive network field.In this paper,the problems of network access selection and handover in cognitive network environment are analyzed comprehensively,the relevant algorithms in recent years are analyzed and compared,the existing problems and deficiencies are found,and the related problems are deeply studied.In the research process,based on the cognitive network and multi-attribute decision theory,the research focuses on the cross-layer perception based cognitive network architecture,cognitive network access selection decision method and vertical handover decision method.Firstly,in view of the complexity of cognitive network environment and the deficiency of traditional network architecture,a cognitive network architecture model based on cross-layer perception is constructed.The multi-plane management mode is adopted to design the cognitive network architecture,and the problems of perception of cognitive network environment and cross-layer interaction of network protocol stack are solved.This model has the characteristics of cognition and cross-layer,can provide users end-to-end service with low latency and high reliability,and achieve the goal of efficient and intelligent network resource management.The analysis and comparison show that this model has good stability and can improve the efficiency of cross-layer perception.Under the guidance of this model,research on the decision-making method of cognitive network access selection is carried out.Secondly,an intelligent access network selection decision algorithm based on multiple attribute decision making(MADM)is proposed to solve the limitations of single attribute decision making algorithm such as inaccurate decision results,high ping-pong switching rate,and inability to truly reflect the needs of users.An efficient access network selection decision method is the key to guarantee quality of service(Qo S)and quality of user experience(Qo E)in heterogeneous network environment.According to the network condition and user preference,the multi-attribute decision making method for access network selection decision is proposed by combining the cross-layer perception framework model of cognitive network,AHP,and TOPSIS.The simulation results proved the effective of the proposed method.It can improve the accuracy of access network selection and reduce unnecessary handovers and sorting anomalies.Mobile users can access the most suitable network according to the different application need.Then,In view of the one-sidedness and subjective problems of AHP,as well as the inherent incompleteness of cognitive network system and the high anomalous rate of ranking in TOPSIS,a vertical handover decision scheme of cognitive heterogeneous network combining subjective and objective weighting and grey relational analysis(GRA)is proposed.Under the condition of limited spectrum resources and low utilization rate of allocated spectrum,how to reduce the total number of handovers while maintaining the optimal continuous network connection has become a focus in handover decision of cognitive heterogeneous network.This scheme solves the problem that the traditional access network selection method can not meet the future network demand by relying on the received signal intensity,bandwidth or some other single network attribute index,uses subjective and objective weighting method to assign weights to the indexes,and uses grey relational analysis method to rank the candidate access networks.Simulation results show that this algorithm is effective in comparison with traditional algorithms in total handover times and ranking anomaly ratio,and it can significantly reduce total handover times and ranking anomaly ratio,which fully shows the superiority of the algorithm proposed in this paper.Finally,in view of the fuzziness and uncertainty of network attributes in subjective and objective weighting method,and the conflict of network attributes in GRA,an access network selection algorithm based on fuzzy multi-attribute decision making(FMADM)and compromise sorting is proposed.FMADM is used to assign weights to attribute indexes and compromise ranking method is used to rank candidate access networks.In the current cognitive heterogeneous network environment,efficient and stable access network selection algorithm is the key to ensure that cognitive users can always get the best network connection service.According to the characteristics of cognitive heterogeneous networks,a cognitive loop based access selection protocol stack structure is proposed.According to network conditions and user experience,multiple network attributes across layers are used to make decisions.The realization processes of FMADM and compromise sorting method are analyzed in detail.The algorithm is analyzed by using static attributes of network and compared the sorting results with two traditional algorithms in different traffic scenarios.The NS3 network simulation tool is used to evaluate and compare the performance of the three algorithms in dynamic network environment.Simulation results verified the effectiveness of the network selection algorithm.It can improve the accuracy and reliability of the access network selection decision;the sorting result is more stable and accurate;it can effectively reduce the number of vertical handovers,ping-pong handovers,end-to-end delay and packet loss rate;and it can balance the network load,improve the quality of network service,and the quality of user experience.