基于改进的中心点划分聚类技术的移动网络规划
Using Modified Partitioning Around Medoids Clustering Technique in Mobile Network Planning
摘要：
每个蜂窝网络部署都需要规划和优化，以便提供足够的覆盖、容量和服务质量（QoS）。优化移动无线网络规划是一项非常复杂的任务，因为必须考虑许多方面。随着移动网络的快速发展，需要有效的网络规划工具来满足用户的需求。然而，决定基站（BS）的最佳布局以实现最佳服务同时降低成本是一项复杂的任务，需要大量的计算资源。本文引入空间聚类方法来解决移动网络规划问题。它解决了天线布局问题或小区规划问题，涉及到定位和配置移动网络的基础设施，通过修改原始的分割周围Medoids PAM算法。M-PAM（Modified Partitioning Around Medoids）算法的提出正是为了满足这一要求和约束。PAM需要在开始搜索基站的最佳位置之前指定簇的数量（k）。M-PAM算法使用无线电网络规划来确定k。我们计算每个集群的覆盖范围和容量，并确定它们是否满足移动要求，如果不满足，我们将增加（k），并根据两种方法重新应用算法进行聚类。该算法的实施，以一个真正的案例研究。实验结果和分析表明，采用方法二时的M-PAM算法在重负载分布情况下是有效的，并导致基站数量最少，这直接影响到网络规划的成本。
英文摘要：
Every cellular network deployment requires planning and optimization in order to provide adequate coverage, capacity, and quality of service (QoS). Optimization mobile radio network planning is a very complex task, as many aspects must be taken into account. With the rapid development in mobile network we need effective network planning tool to satisfy the need of customers. However, deciding upon the optimum placement for the base stations (BS s) to achieve best services while reducing the cost is a complex task requiring vast computational resource. This paper introduces the spatial clustering to solve the Mobile Networking Planning problem. It addresses antenna placement problem or the cell planning problem, involves locating and configuring infrastructure for mobile networks by modified the original Partitioning Around Medoids PAM algorithm. M-PAM (Modified Partitioning Around Medoids) has been proposed to satisfy the requirements and constraints. PAM needs to specify number of clusters (k) before starting to search for the best locations of base stations. The M-PAM algorithm uses the radio network planning to determine k. We calculate for each cluster its coverage and capacity and determine if they satisfy the mobile requirements, if not we will increase (k) and reapply algorithms depending on two methods for clustering. Implementation of this algorithm to a real case study is presented. Experimental results and analysis indicate that the M-PAM algorithm when applying method two is effective in case of heavy load distribution, and leads to minimum number of base stations, which directly affected onto the cost of planning the network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
