Super dense network clustering method based on density improvement K-Means algorithm

Abstract

The invention discloses a super dense network clustering method based on a density improvement K-Means algorithm. The method comprises the following steps: firstly calculating distribution density anda clustering density threshold of microcell base stations in a super dense network; selecting the base stations having the distribution density being greater than the clustering density threshold asinitial cluster centers, and forming an initial cluster center pool; screening a final cluster center point by making the distance between any two initial cluster centers in the initial cluster centerpool be greater than a cluster center isolation distance; and using the final cluster center number K and corresponding geographic positions as input parameters of the traditional K-Means, and executing the K-Means algorithm to obtain a clustering result of all base stations in the super dense network. By adoption of the super dense network clustering method, dynamic clustering can be performed according to the change of the network topology, the situation of being caught in a locally optimal solution is avoided by screening the cluster center points, thereby improving the clustering accuracy, meanwhile accelerating the clustering convergence speed, and the super dense network clustering method can be applied to network clustering and base station resource scheduling.

Description

technical field [0001] The invention belongs to the field of wireless communication, and relates to a super-dense network clustering method based on a density-improved K-means algorithm, which is suitable for rationally clustering the network and scheduling base station resources. Background technique [0002] In recent years, more and more communication devices have been connected to the network, making the entire network structure large and complex. At the same time, users' demand for data traffic has also shown explosive growth. Wireless networks will face huge challenges. In an ultra-dense network, the system capacity of a wireless network can be greatly increased by densely deploying microcell base stations in a cell. However, due to the dense deployment of a large number of base stations, the interference problem in the network is becoming more and more serious, and the problem of unreasonable resource allocation also needs to be solved urgently. Dividing the entire n...

AI Technical Summary

Problems solved by technology

However, the traditional K-means algorithm needs to artificially set the number of clusters in advance, and cannot adaptively set the number of clusters according to changes in the network structure.

In addition, due to the random selection of the initial cluster center, the final clustering result is easy to fall into the local optimal solution.

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