| With the spurt of data traffic in daily life,the challenges the network facing in the future are growing.In order to meet the demand for growth of network data traffic,ultra-dense networks have become the key method to address this demand.The reducing of the access point's radius,causing frequent handover of user equipment and data transmission interruption rate's increasing,which interferes with continuity and affects user's experience.In the UDN environment,traditional mobility management algorithms are no longer suitable for future networks.How to solve the mobility management problem in the UDN environment and enhance the user experience has become a key challenge for the future network development.Therefore,this thesis studies and designs a more effective handover method in mobility management for the UDN scenario,aiming at reducing handover delay,ensuring continuity of services and enhancing user experience.Firstly,based on the network architecture,this paper proposes an ultra-dense network mobility management algorithm based on SDN and NFV convergence.The traditional network architecture can not solve the problem of handover redundancy and too much handover signaling that the ultra-dense network will encounter.The concept of SDN and NFV combination is incorporated into the network architecture,and virtual network elements are used instead of physical network elements.In the network architecture of SDN and NFV combination,an optimized handover signaling process is proposed,and a mathematical model is established to perform performance analysis.The simulation results show that compared with the traditional algorithm,the algorithm can effectively reduce the signaling interaction,thereby reducing the total delay of handover,adapting to the ultra-dense network,enhancing the user experience,promoting load balancing between networks,and effectively improving the network capacity.Secondly,this thesis proposes an ultra-dense network handover algorithm based on threshold adaptive adjustment.The algorithm adds machine learning ideas to fuzzy logic and generates an improved dynamic fuzzy Q learning algorithm for mobility management in small cells.The algorithm gradually generates new fuzzy rules and adaptively adjusts the handover hysteresis margin through system learning.The simulation results show that the algorithm achieves a better balance between the signaling cost caused by the handover and the user experience affected by the call loss rate by obtaining better handover parameters.Finally,the design and implementation of the management and control platform for the ultra-dense networking of LTE small base stations is completed.The system is based on the actual ultra-dense network built by LTE small base stations and core network equipment.The small base station is connected to the core network through the SDN switch.The SDN controller and the small base station connect and communicate through the OPENSC protocol.The controller collects the base station configuration and resources,and tests through the controller and the small base station communication test,the base station configuration parameter acquisition test,and the base station performance optimization test,and verifies that the system reaches a small size.The effect of the visual management control of the dense network of the base station,and the switching optimization during the UE mobile process is realized. |
