基于双步灰狼优化器的LTE网络BTS位置优化

M. Komala, I. Wahidah, Istikmal
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引用次数: 4

摘要

基站是蜂窝网络部署的主要网元。广阔的放置区域、位置变化以及用户数量和环境因素产生了巨大的搜索解决方案空间。这些都使得基站定位成为一个np难题。网络规划优化经常使用元启发式算法来寻找最优解。灰狼优化算法(Grey Wolf Optimizer, GWO)是一种元启发式算法,具有参数多、过程简单等优点。GWO将勘探阶段和开发阶段划分在同一部分,使得求解过程中存在多样性问题。本研究提出了改进的GWO,延长了搜索阶段,使算法能够在更广阔的搜索空间中扩展搜索。双步GWO改变了系数矢量|A|沿迭代递减的方式。该算法区分了矢量系数在勘探阶段和开采阶段的变化行为。模拟涵盖了区域、用户数量和用户密度的变化。该工作评估了BTS的部署数量和位置、覆盖范围和用户数量,并将其与灰狼优化器进行了比较。
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LTE networks BTS location optimation with double step grey wolf optimizer
Base station is the main network element in cellular networks deployment. The vast area of placement, position variation and number of users and environmental factors spawned a vastly searching solution space. Those are makes base station location determination an NP-hard problem. Network planning optimization frequently use meta-heuristic algorithm to find the optimum solution. Grey Wolf Optimizer (GWO), one of meta-heuristic algorithm has advantages in number of parameters used and simplicity of the process. GWO allocates exploration phase and exploitation phase in the same portion makes diversity issue in the finding solution process. The research proposes modified GWO to prolong exploration phase that enabler the algorithm to expand the search through wider search space. Double step GWO change the way of coefficient vector |A| decrease along the iteration. This new algorithm differentiates changing behavior of vector coefficient between exploration phase and exploitation phase. Simulation conducted covered variation of areas, user number and user density. The work evaluated the number and locations of BTS deployed, coverage area and number of users that can be and compare it to grey wolf optimizer.
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