The Research And Implement Of LTE Coverage Self-optimization

LTE networks can provide faster connectivity, larger capacity and more service options, but the increase in the density of LTE base stations and the complexity of coordination with the 2 / 3G networks also increase the operating expense. The self-organizing network (SON) is put forward in such a context. Subscribers directly determine the quality of the LTE networks through the coverage performance of network. In order to save costs and improve competitiveness, operators need to solve optimization problem of network coverage by detecting network performance indexes to perceive the changes of network environment and traffic and adjusting network parameters timely. In this way, operators can provide users with good experience.This paper optimizes the coverage performance of LTE network.This paper extracts the performance index from the measurement reports(MR), identifies the problem coverage area, and optimizes base stations antennas' azimuth through the proposed optimization process and algorithm.Firstly, this paper studies the development and application prospect of SON, introduces the LTE network architecture and SON function, and clear the basic concepts of coverage self-optimization. Coverage self-optimization is a continuous closed-loop process, automatic collection and analysis of network performance indexes. When the performance indexes reach the present threshold, then triggers the optimization.Secondly, this paper researches the performance indicators that affect coverage and capacity performance and selects the source of indicators. The data obtained from the conventional drive test and call quality test can not satisfy the real-time requirements of coverage self-optimization function. This paper selects MR data reported by UE and eNB as the performance indexes source; and sets the criteria of weak coverage cell and overlapping coverage cell. This paper identifies the corresponding cell by selecting and analyzing the MR data, which is prepare for coverage self-optimization.Finally, this paper proposes a closed-loop self-optimizing process,which is based on an improved simulated annealing algorithm to generate optimized antennas' azimuth adjustment for weak coverage cells and overlapping coverage cells. Simulated annealing algorithm may miss the best solution in the convergence process, so by increasing the memory function and saving the current optimal solution in time to improve the search efficiency in the process of annealing. The simulation results show that the proposed self - optimizing process and the improved algorithm can effectively optimize the weak coverage cells and overlapping coverage cells, and the convergence of the improved algorithm is improved. The test results also show that the self-optimizing process and algorithm can optimize the coverage performance of the related cells.