Research On Resource Management In Software-defined Cellular Networks

With the development of Internet,Internet of Things and mobile commu-nications,the traffic carried by cellular networks shows an exponential growth trend,which results in that the services provided by traditional cellular systems with rough,static and local features cannot meet the QoS demands of users.To solve these problems,based on the software-defined cellular network(SDCN)architecture,which utilize the global view of the network,the thesis studies the resource management in the scenarios of LTE/WiFi coexistence and ground base station(GB S)/Unmanned Aerial Vehicle(UAV)coexistence under differ-ent network deployments by using the real-time and dynamic network status information.The proposed methods provide an effective solution in the re-source management for future cellular networks,which are of great significant.The contribution of this thesis are as follows:Firstly,in the scenario of LTE/WiFi coexistence,this thesis studied the re-source management of licensed and unlicensed band in SDCN.A resource al-location algorithm for future network is proposed.The proposed algorithm uti-lizes the integrated femto-WiFi base station(IFW fBSN)to allow smart devices use both the licensed band(via LTE interface)and the unlicensed band(via WiFi interface)to alleviate the spectrum shortage.The proposed algorithm also con-siders the smart devices(sDevices)with both cellular and WiFi interfaces,the WiFi devices(wDevices)with WiFi-only interfaces,and the macrocell device(mDevice).Using the global view of the SDCN controller,a weighted utility maximization problem is proposed to allocate resources of licensed and unli-censed bands,and also keeps the interference from fBSN to mDevices below predefined thresholds.Furthermore,an alternating algorithm is used to solve the complex non-convex problem.Numerical results show that the proposed re-source allocation algorithm significantly improves the average throughput and average utility of all devices in the network.Throughput gains as large as 41.6%for the average of all sDevices and wDevices are achieved by using the new de-signs.Secondly,in scenario of GB S/UAV coexistence,this thesis considered the UAV-assisted technology to improve user performance in the emergency situa-tions,and proposed the UAV deployment and resource allocation algorithm by utilizing the centralized control logic of SDCN.First,a drone cell users’ ag-gregate utility maximization problem is proposed by optimizing the 3D UAV placement and the user association.Then,a 3D UAV placement and resource allocation algorithm is proposed by optimizing the 3D UAV placement,user as-sociation and UAV transmission power.The proposed algorithm maximizes the network revenue utility of the associated number of users and the transmitted power of UAV,and also keeps QoS demand of users greater than the threshold.After mathematical analysis,the mixed integer multidimensional non-convex problem was reduced to a two-stage optimization algorithm.The bisection search method and the Convex-Concave Procedure(CCCP)algorithm were used to solve the problem.Simulation results show that the proposed 3D UAV placement and user association algorithm improves the network throughput and utility compared with other traditional placement methods,and the maximum throughput gain can reach up to 35.4%;while the proposed 3D UAV placement and resource allocation algorithm improves the network revenue utility com-pared with other existed placement methods,and the maximum gain is 3 5.08%.Finally,in view of the problem that the performance of flexibly deployed UAV is limited by the backhaul link,this thesis further studied the multi-dimensional air-ground resource allocation with the wireless backhaul.A utility maximiza-tion problem for multi-UAV enabled SDCN is proposed.The proposed problem j ointly optimize the 3D UAV deployment,user scheduling and association,and the spectrum resources allocation.Furthermore,an alternating maximization problem is proposed to solve the mixed integer combined non-convex prob-lem by decoupling it into three alternating subproblem blocks which are solved via the successive convex optimization(SCO)technique and the modified al-ternating direction method of multipliers(ADMM)in the proposed algorithm.Theoretical analysis and simulation results verify the convergence of the algo-rithm,and extensive numerical results verify the superiority of the algorithm.Compared with the traditional UAV placement methods,this algorithm signif-icantly improves the throughput and utility of the overall users,in which the maximum throughput gain is up to 74.9%.