loading page

Density-based Clustering for Next-Generation Network Planning Assisted by Real Data
  • M. Umar Khan
M. Umar Khan

Corresponding Author:[email protected]

Author Profile


We propose a novel density-based learning technique assisted by real data to determine the appropriate Epsilon and to locate the HTC. We have also studied the correlation of anomalous BSs (ABs) in the data in the context of network planning. The algorithm, density-based network clustering (DNC), determines the ABs, identifies the HTC and the appropriate value of Epsilon by satisfying the MNOs’ requirements on the highest traffic density MB/km2 and the target deployment area in km2 . We use the k nearest neighbors (k-NN) as a benchmark to compare the appropriate value of Epsilon and other performance parameters.