Analyses On The Fractal Patterns In Cellular Networks And The Important Nodes In Complex Networks

In conventional cellular networks,for base stations(BSs)that are deployed far away from each other,it is general to assume them to be mutually independent.Nevertheless,after long-term evolution of cellular networks through various generations,this assumption no longer holds.Instead,the BSs,which seem to be gradually deployed by various operators in a service-oriented manner,have embedded many fundamentally distinctive features in their locations,coverage,and traffic loading.These features can be leveraged to analyze the intrinsic pattern in BSs and even human community.Firstly,according to large-scale measurement datasets,we build up a correlation model of BSs by utilizing one of the most important features,i.e.,spatial traffic.Coupling with the theory of complex networks,we make further analysis on the structure and characteristics of this traffic load correlation model.Numerical results verify that the degree distribution follows scale-free property.In particular,the datasets unveil the characteristics of fractality and small-world.Furthermore,we apply collective influence(Cl)algorithm to localize the influential base stations.On the other hand,complex networks present heterogeneous nature with nodes playing far different roles in structure.Therefore,identifying vital nodes contributes to improving the network robustness,maintaining the network connectivity and controlling the outbreak of epidemics.To define the relative importance of nodes,various conventional methods such as heuristic strategies and collective influence algorithm have been previously proposed.However,extensive empirical analyses demonstrate that the majority of the known methods only make use of the structural information,but neglect the potentially valuable information from other aspects.In this thesis,we apply a new method based on dual population genetic algorithm(DPGA)to localize the influential nodes,which intends to construct a most reasonable importance sequence(IS)to measure the relative importance of nodes in complex networks.In this case,we define a collapse speed to evaluate the feasibility of each importance sequence.Numerical results of synthetic datasets reveal that this approach performs even better compared to the centrality-based method and collective influence algorithms.In the end,the thesis makes a summary about the whole work and points out the potential future research directions.