Research On Mobility Management Algorithm In LTE-A Cellular Neteork

With the increasing in the number of mobile users, how to effectively improve system quality in the mobile state has become a critical question to solve for the wireless mobile communication system. The mobility management of cellular network is an important technology to improve system throughput and service quality, which is important for improving system performance and user service experience. In this paper, we focus on two key technologies, i.e. Coordinated Multiple Points (CoMP)Transmission and Heterogeneous Network (HetNet), aiming at improving system throughout, reducing the probability of handover failure,considering the balance of system resource allocation and meeting the service requirement. Meanwhile, the optimization theory and the Q learning algorithm are employed to make wide research deeply.In the traditional macro cellular network, a CoMP handover algorithm based on user mobility prediction is proposed. Analyzing user quality of service as well as network resource allocation, the cycle selection algorithm to choose cooperation set and transmission set based on the radial velocity and signal to interference plus noise ratio (SINR).Simulation results demonstrate the effectiveness of our proposed algorithm in reducing the total number of handover, the probability of handover failure and the impact of system delay on system performance, and improving the service quality of cell-edge users.In the cellular heterogeneous network, a mobility management strategy based on Q learning is proposed. This algorithm puts maximizing the data rate of each base station as an optimization target. Give that different types of base stations have different transmission power and backhaul, a Q learning based cell range extension technology is proposed by jointly considering cell handover and network resource allocation. Agent selects the optimal hysteresis value for the base stations by sensing environment state information, so as to ensure the base station throughput optimization. Simulation results show that the proposed scheme can effectively improve the system throughput and achieve load balancing.