ABC-PLOSS:一个使用人工蜂群算法的GSM电信网络路径损失最小化的软件工具

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
V. Anireh, E. N. Osegi
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引用次数: 5

摘要

在本文中,我们提出了一个开源软件工具“ABC-PLOSS”,这是开发用于优化过程。路径损耗优化处理的是在电信系统中搜索一组最佳的运营商特定参数,使其产生最小的运营成本。如何调整移动基站网络,使其高效、可靠地运行,是移动通信运营商,特别是全球移动系统(GSM)运营商面临的首要问题。该工具使用基于称为人工蜂群(ABC)的群体智能算法的顺序处理器架构和成本-231 Hata路径损失模型作为路径损失最小化(PLM)的成本函数。利用ABC- ploss框架,将ABC算法与另外两种现有和流行的人工智能(AI)算法(遗传算法(GA)和粒子群优化(PSO))进行比较。仿真研究结果表明,与传统技术相比,该工具确实有用,因为它提供了具有竞争力或更低的路径损失估计。它还表明,与遗传算法和粒子群算法相比,ABC算法有可能分别实现7倍和2倍的路径损失改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ABC-PLOSS: a software tool for path-loss minimisation in GSM telecom networks using artificial bee colony algorithm
In this paper, we present an open-source software tool 'ABC-PLOSS', which is developed for use in optimisation processes. Path-loss optimisation deals with searching for the best set of operator-specific parameters in telecommunication that gives the least cost of operation. It is a primary issue that challenges mobile communication operators, particularly the global system mobile (GSM) operators in tuning mobile-base station networks for efficient and reliable operation. The tool uses a sequential processor architecture based on a swarm intelligence algorithm called artificial bee colony (ABC) and the cost-231 Hata path-loss model as cost function for path-loss minimisation (PLM). Using the ABC-PLOSS framework, the ABC algorithm is compared with two other existing and popular artificial intelligent (AI) algorithms called the genetic algorithm (GA) and particle swarm optimisation (PSO). Results of simulation studies show that this tool is indeed useful as it gives a competitive or lower path-loss estimate when compared with conventional techniques. It also shows that it is possible for the ABC to attain an estimated seven-fold and two-fold path-loss improvement over the GA and the PSO techniques respectively.
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来源期刊
International Journal of Swarm Intelligence Research
International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.50
自引率
0.00%
发文量
76
期刊介绍: The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.
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