Research on adaptive optimization of KNN-DBSCAN parameters based on MDT overlapping coverage data - Lu LIU; Ruijie CHEN; Jia LI (Dianxin kexue)
In the traditional network optimization, the drive test (DT) work has obvious disadvantages, such as difficult to fully test roads and buildings, large test workload, low work efficiency, long cycle, affected by human factors, unable to dynamically pay attention to the network quality of each area, and the conventional measurement report (MR) data does not have positioning information, so it is impossible to accurately locate the location where the overlapping coverage problem occured.Based on minimization drive test (MDT), the precision positioning system collected the MDT data of the underlying base station and outputted the grid with high overlapping coverage according to the overlapping coverage algorithm.Then, the sensitivity of DBSCAN algorithm to parameter setting was solved through the adaptive K-nearest neighbor density-based spatial clustering of applications with noise (KNN-DBSCAN)joint algorithm.The problem grid was unsupervised clustered, the problem contiguous area was converged, and the grid area was mapped through the cell sampling contribution.Finally, the global top cell was accurately adjusted to optimize the high overlap coverage.