摘要
为适应当今无线通信产业的快速发展,解决基站选址是否合理,本文提出了一种基站定位算法,结合均值漂移算法和DBSCAN聚类算法来解决基站的合理选址问题。该算法将大区域划分为较小的子区域,并利用均值漂移算法计算每个子区域内局部服务体密度的密度极值点。根据每个高密区域的大小,建立覆盖范围不同的基站。采用DBSCAN聚类算法对近距离弱覆盖区域进行聚类,优化传统均值漂移算法的收敛速度。实验结果表明,该算法在寻找基站定位和数据分类的最优解方面具有实用性和有效性。
To adapt to the rapid development of the wireless communication industry and solve the problem of whether a base station location is reasonable, this paper proposes a base station positioning algo-rithm that combines the mean shift algorithm and the DBSCAN clustering algorithm to address the issue of rational base station selection. This algorithm divides a large area into smaller sub- regions and uses the mean shift algorithm to calculate the density extreme points of the local service body density in each sub-region. Based on the size of each high-density area, base stations with different coverage ranges are established. The DBSCAN clustering algorithm is used to cluster the weak cov-erage areas at close range, optimizing the convergence speed of the traditional mean shift algorithm. Experimental results show that this algorithm is practical and effective in finding the optimal solu-tions for base station positioning and data classification.
出处
《应用数学进展》
2023年第3期847-859,共13页
Advances in Applied Mathematics