Joint Optimization Methods For Heterogeneous Networks Data Offloading

To solve the problem of high traffic load on the cellular network base station,data offloading,as a new technology,has received widespread attention in recent years.The basic idea is to transfer the data that the user needs from the cellular network to the heterogeneous network.However,due to the differences in the number of APs around the base station,it may result in situations where traditional heterogeneous networks such as dense Wi-Fi hotspots may exist or only opportunistic heterogeneous networks may exist.Therefore,in view of the above two different situations,the joint optimization methods for heterogeneous networks data offloading proposed in This thesis regards the third-party network as a third-party operator independent of the cellular network in both traditional heterogeneous networks and opportunistic heterogeneous networks,and joint cellular network to achieve data offloading.Furthermore,these corresponding data allocation optimization solutions for the problem of overloading the cellular network in two different situations are provided.Firstly,for the case of dense third-party traditional heterogeneous networks,a joint optimization model for the traditional heterogeneous networks data offloading.The model treats Wi-Fi hotspot owners as third-party operators,leveraging the high-speed transport features of Wi-Fi hotspots.In view of the sparseness of AP around the base station,this thesis proposes a joint optimization model for the opportunistic networks data offloading.The model views seed users as third-party operators and employs Wi-Fi Direct technology to build a D2 D network.By dividing the unlicensed frequency bands for different seed users by cellular network operators,transmission interference caused by the same frequency band between seed users is avoided,and the high transmission rate and wide coverage of Wi-Fi Direct technology are fully utilized.Secondly,based on the proposed joint optimization model for traditional heterogeneous networks data offloading,this thesis proposed a joint optimization method for traditional heterogeneous networks data offloading.We first introduces the S-type utility function and the principle of economics supply to depict user satisfaction metrics,models the traffic distribution problem as a joint optimization problem that maximizes user satisfaction and minimizes operator profit.Then the S-type utility function is transformed into the convex function by the principle of economics supply,and the dynamic co-ordinate search algorithmis used to solve the bi-level optimization problem.The experimental results show that this method can obtain the approximate optimal solution in finite number of iterations of the bi-level optimization problem.In the case of the traditional heterogeneous network joint the cellular network,a data distribution optimization solution is obtained.Finally,based on the proposed joint optimization model for opportunistic heterogeneous networks data offloading,this thesis proposes a joint optimization method.This method first proposes the problem of minimizing energy consumption of multiple operators to ensure the benefits of cellular network operators and seed users.After the problem of minimization is proved to be a restricted multiple knapsack problem,this thesis further proposes a solution method based on dynamic programming algorithm and a solution method based on binary splitting algorithm.In addition,in order to ensure the interests of multiple types of requesting users,this thesis further proposed the issue of maximizing user satisfaction with multiple types of requests.And proposed an improved Hungarian algorithm to solve it.Experimental results show that the three algorithms can find the approximate optimal solutions.In the case of the opportunistic heterogeneous network joint the cellular network,a data distribution optimization solution is obtained.