Cooperative Caching Strategy In D2D Assisted Cellular Networks

As the emergence of small cell densification and cache-enabled smart devices,mobile edge caching is regarded as a promising tool to relieve traffic burden of core network.Under such paradigm,popular contents are prefetched and stored in small base stations(SBSs)or user devices.However,the popularity of a certain content may change over time due to human factors.Considering the change of content popularity,this thesis studies the cooperative content caching problem for device-to-device(D2D)assisted networks.Firstly,the thesis expounds the research background and research significance,analyzes the research status at home and abroad in detail.Secondly,the thesis summarizes the research work,the content and structure of the thesis,and finally,the thesis summarizes the whole thesis and looks forward to the future research direction.The main research work is as follows:Firstly,the focus of the thesis is to study a delay minimization problem by j ointly considering the spatiotemporal variation of content variation,the cost of content sharing between user devices,and the cost of cooperative caching among BSs.To address this problem,we propose a two-stage multi-armed bandit learning based online cooperative(MAB-LOC)algorithm.In the first stage,we design a MAB based algorithm to estimate the content popularity.In the second stage,we design a semidefimite relaxation based approach to obtain the caching strategy.Through simulation results,we show that the performance of the proposed algorithm is competitive in terms of caching-hit probability and end-to-end delay.Secondly,the focus of the thesis is to study a minimizing energy consumption problem by jointly considering the similarities between users(including affinity similarity,that is,the similarity of user application content,and location similarity),the cost of user devices cache,the cost of content sharing between user devices,and the cost of content transmission between the base station and user devices.To address this problem,we propose a two-stage K-means Learning based Cooperative(KM-LC)algorithm.In the first stage,we use K-means algorithm to classify users.In the second stage,we design a clustering-based greedy algorithm obtain the caching strategy.Through simulation results,we show that the performance of the proposed algorithm is competitive in terms of energy efficiency.