﻿ 基于模拟退火算法的移动站址规划问题 Mobile Site Planning Based on Simulated Annealing Algorithm

Modeling and Simulation
Vol. 12  No. 02 ( 2023 ), Article ID: 63474 , 13 pages
10.12677/MOS.2023.122156

1上海理工大学，机械工程学院，上海

2安徽理工大学，材料科学与工程学院，安徽 淮南

Mobile Site Planning Based on Simulated Annealing Algorithm

Xiandi Shen1, Jiake Liu2

1School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai

2School of Materials Science and Engineering, Anhui University of Science and Technology, Huainan Anhui

Received: Feb. 22nd, 2023; accepted: Mar. 24th, 2023; published: Mar. 31st, 2023

ABSTRACT

This paper mainly studies to solve the coverage problem of the weak coverage area of the existing network. The coordinates of the site are selected from 2500 × 2500 points in a given area to carry out site planning, so that 90% of the total service volume of the weak coverage points is covered by the planned base station. By constructing the target linear programming model, the iterative algorithm is adopted to obtain the optimal solution, and the site planning of the base station is carried out. By cleaning the data, the distance between the existing base station and the weak coverage point is determined according to the minimum threshold of the macro station and the micro base station, and the points that do not meet the requirements are screened out and deleted, so as to reduce the amount of data computation. Finally, the simulated annealing algorithm was used to change the number of initial layout of macro stations and the number of initial layout of micro base stations, and the objective function was optimized to get the optimal number of macro stations and micro base stations, and the coverage rate reached 90.1%.

Keywords:Site Planning, Optimization Algorithm, Simulated Annealing

1. 引言

2. 基站的覆盖分析

$P=1-\frac{0.3387×5+0.8954×5}{50×60}=99.79%$

Figure 1. Schematic diagram of base station coverage

(a) (b)

Figure 2. (a) The distance between the cell center and the existing base station is greater than 10; (b) The distance between the cell center and the existing base station is less than 10

Figure 3. Schematic diagram of Acer station gap

Figure 4. Void filling method

3. 模型建立的预处理

3.1. 数据预处理

3.1.1. 删除业务量小于1的点(为减少计算量)

Table 1. Proportion of weak coverage points with traffic less than 1

Figure 5. Proportion of weak coverage points with service volume less than 1

3.1.2. 弱覆盖点的分布情况分析

Figure 6. Plane distribution of weak coverage points

Figure 7. Frequency distribution histogram of weak coverage point traffic

3.1.3. 宏/微基站的性价比分析

Table 2. Cost performance analysis of macro/micro base station

3.2. 模型假设与约定

1) 服务量小于1的弱覆盖点不考虑；

2) 还有信号强度与距离中心点的距离无关，假设在覆盖范围内强度不变；

3) 业务量小的点在实际场景中，将其覆盖的优先级低。

3.3. 符号说明及名词定义

60 × 60网格：面积为60 × 60网格，具体划分个数为42 × 42。

Traffic：业务量。

4. 模拟退火算法模型的建立

1) 宏基站具体位置的选则。60 × 60方格的中心可能不符合与已有基站的门限约束，如何在60 × 60方格中选择最佳宏基站位置成为主要面临的问题。

2) 已有站址的门限约束处理。容易得出，在2500 × 2500的平面上，约包含625 w个可选择点，已有站址也多达1474个，快速求解可选择点或已有站址覆盖点成为限制计算速度重要问题。

5. 模型关键问题求解

60 × 60方格区域划分，按照长度对网格划分，划分完成后形成42 × 42个方格；对方格中弱覆盖点进行密度统计，得到下图9

Figure 8. Flow chart of base station site selection algorithm

Figure 9. Distribution of traffic density in the 60 × 60 grid area

Figure 10. Histogram of frequency distribution in 60 × 60 grid

Figure 11. Schematic diagram of Acer site selection algorithm

Figure 12. Schematic diagram of optimal coordinate selection algorithm in 60 × 60 grid

Figure 13. Schematic diagram of integer points covered by a circle with radius 10

Table 3. Base station type and number planning table (part)

Figure 14. Effect of simulated annealing algorithm to optimize the number of base stations

Figure 15. Schematic diagram of site planning for weak coverage points

Table 4. Summary of planned base stations

Table 5. Uncovered points are listed in reverse order by traffic volume

Figure 16. Coverage diagram of the planned site

6. 结论

Mobile Site Planning Based on Simulated Annealing Algorithm[J]. 建模与仿真, 2023, 12(02): 1678-1690. https://doi.org/10.12677/MOS.2023.122156

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