无线网络规划与优化问题的优化策略与估计技术比较

V. Prokopets, L. Globa
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The purpose of the paper is to create a toolkit that allows finding the proper relationships between network parameters to define target values that will help to build an effective network plan in terms of performance and costs for its creation and operation. The tools should be able to work efficiently using the minimum set of available statistical data, as well as taking into account their imperfections. \nMethods. Mathematical estimation and optimization methods are used, namely Ordinary Least Squares, Ridge Regression, Lasso, Elastic-net, LARS lasso, Bayesian Ridge Regression, Automatic Relevance Determination, Stochastic gradient descent, Theil-Sen estimator, Huber Regression, Quantile regression, Polynomial regression. We consider 12 estimation methods in combination with two optimization strategies. Additionally, the method of partial analysis of the search space with different number of configurations is considered. \nResults. 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引用次数: 0

摘要

背景。无线网络规划是蜂窝网络生命周期的主要阶段之一,因为它决定了资本和运营成本,并允许在任何给定时间对系统性能进行评估。在网络扩展过程中,需要对现有网络统计数据进行准确而全面的分析,以便进行适当的小区规划。这些统计数据是在蜂窝网络的整个生命周期中收集的,通常具有一定的缺陷(统计数据的异质性,在搜索空间的不同部分具有不同的密度,直至存在重要的空洞等)。描述无线网络功能的系统可以表示为黑盒子,因为其内部过程太复杂而无法用数学函数定义。这决定了是否需要使用合适的工具。目标。本文的目的是创建一个工具包,允许找到网络参数之间的适当关系,以定义目标值,这将有助于在其创建和操作的性能和成本方面建立有效的网络计划。这些工具应该能够有效地使用最少的可用统计数据集,并考虑到它们的不完善之处。方法。使用数学估计和优化方法,即普通最小二乘、Ridge回归、Lasso、Elastic-net、LARS Lasso、贝叶斯Ridge回归、自动相关性确定、随机梯度下降、Theil-Sen估计、Huber回归、分位数回归、多项式回归。我们考虑了12种估计方法与两种优化策略的结合。此外,还考虑了不同构型数搜索空间的局部分析方法。结果。已经创建了一个使用Python编程语言的软件包,其中包含所有考虑的估计和优化方法的实际实现,以及用于评估软件包(基准)的任意配置和可视化结果的工具。最佳的估计方法是普通最小二乘,用于寻找4G无线网络统计参数的最优配置,以最大限度地提高下载速度。为了获得满意的结果,考虑25个初始点和250个估计点就足够了,更大的点数量不会显著提高预测精度。结论。结果表明,将创建的软件包用于无线网络规划任务是可行的。进一步的研究旨在扩展创建的软件包的功能,并考虑额外的估计方法和优化策略。关键词:无线网络规划;无线网络优化;蜂窝网络;评估技术;优化策略;大数据;2 g;3 g;4 g;CQI;能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COMPARISON OF OPTIMIZATION STRATEGIES AND ESTIMATION TECHNIQUES FOR RADIO NETWORK PLANNING AND OPTIMIZATION PROBLEMS
Background. Radio network planning is one of the main phases of the cellular network lifecycle, as it determines capital and operating costs and allows system performance evaluation at any given time. An accurate and comprehensive analysis of existing network statistics is necessary for proper cell planning during network expansion. These statistics are collected throughout the life cycle of the cellular network and usually have certain imperfections (heterogeneity of statistics, which have different densities in different parts of the search space, up to the presence of significant voids, etc.) The system describing the functioning of the radio network can be represented as a black box because its internal processes are too complex to be defined by mathematical functions. This determines the need to use appropriate tools. Objective. The purpose of the paper is to create a toolkit that allows finding the proper relationships between network parameters to define target values that will help to build an effective network plan in terms of performance and costs for its creation and operation. The tools should be able to work efficiently using the minimum set of available statistical data, as well as taking into account their imperfections. Methods. Mathematical estimation and optimization methods are used, namely Ordinary Least Squares, Ridge Regression, Lasso, Elastic-net, LARS lasso, Bayesian Ridge Regression, Automatic Relevance Determination, Stochastic gradient descent, Theil-Sen estimator, Huber Regression, Quantile regression, Polynomial regression. We consider 12 estimation methods in combination with two optimization strategies. Additionally, the method of partial analysis of the search space with different number of configurations is considered. Results. A software package using the Python programming language has been created, which contains a practical implementation of all the considered estimation and optimization methods, as well as tools for evaluating arbitrary configurations of the software package (benchmark) and visualizing the results. The best estimation method is Ordinary Least Squares for finding the optimal configuration of the statistical parameters of the 4G radio network to maximize the download speed. To obtain satisfactory results, it is enough to consider 25 initial and 250 estimated points - a larger number of points will not significantly increase prediction accuracy. Conclusions. The results indicate the possibility of using the created software package for radio network planning tasks. Further research is aimed at expanding the created software package's functionality and considering additional estimation methods and optimization strategies. Keywords: radio network planning; radio network optimization; cellular networks; estimation techniques; optimization strategies; big data; 2G; 3G; 4G; CQI; capacity.
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