一种无线网络空间深度覆盖规划方法

A Deep Coverage Planning Method of Wireless Network Space

为解决无线网络空间深度覆盖问题,提高空间深度覆盖效果,提出了一种新的无线网络空间深度覆盖规划方法。利用基于K-means聚类算法挖掘无线网络空间深度覆盖规划特征,依据该特征构建递阶层次结构模型,评估无线网络空间深度覆盖性,判定影响覆盖性的全部因素,构建无线网络空间深度覆盖规划模型,运用文化蚁群算法对该模型进行优化求解。实验结果表明,该方法可获取无线网络空间节点最大覆盖率,节点覆盖率极大限度逼近理想覆盖率,且深度覆盖规划所需时间较短。

In order to solve the problem of wireless network space depth coverage and improve the effect of space depth coverage, a new wireless network space depth coverage planning method was proposed. The K-means clustering algorithm is used to mine the characteristics of wireless cyberspace deep coverage planning. According to the characteristics, a hierarchical structure model is constructed to evaluate the wireless cyberspace deep coverage, determine all factors affecting the coverage, construct the wireless cyberspace deep coverage planning model, and optimize the model by using the cultural ant colony algorithm. The experimental results show that this method can obtain the maximum coverage of wireless network nodes, and the node coverage is close to the ideal coverage, and the time required for deep coverage planning is short.