Prediction method of 5G indoor depth coverage based on 3D modeling and ray-tracking algorithm

马敏; 李威

doi:10.13992/j.cnki.tetas.2020.05.014

Chinese

您当前的位置：

首页 >

文章列表页 >

Prediction method of 5G indoor depth coverage based on 3D modeling and ray-tracking algorithm

更新时间：2026-04-21

- Prediction method of 5G indoor depth coverage based on 3D modeling and ray-tracking algorithm
- Vol. 33, Issue 5, Pages: 72-76(2020)
- 作者机构：
  
  中国移动通信集团设计院有限公司陕西分公司
- 作者简介：
- 基金信息：
- DOI：10.13992/j.cnki.tetas.2020.05.014
  CLC： TN929.5
- Published：2020
- 稿件说明：
移动端阅览
[1]马敏,李威.基于3D建模和射线跟踪算法的5G室内深度覆盖预测方法[J].电信工程技术与标准化,2020,33(05):72-76.

马敏, 李威. Prediction method of 5G indoor depth coverage based on 3D modeling and ray-tracking algorithm[J]. 2020, 33(5): 72-76.
[1]马敏,李威.基于3D建模和射线跟踪算法的5G室内深度覆盖预测方法[J].电信工程技术与标准化,2020,33(05):72-76. DOI： 10.13992/j.cnki.tetas.2020.05.014.

马敏, 李威. Prediction method of 5G indoor depth coverage based on 3D modeling and ray-tracking algorithm[J]. 2020, 33(5): 72-76. DOI： 10.13992/j.cnki.tetas.2020.05.014.

摘要

5G室内覆盖方案的复杂性和多样化使得5G室内覆盖效果难以预测

本文提出5G室内深度覆盖预测方法

将射线跟踪算法与建筑物3D建模相结合

为5G室内深度覆盖的精确预测提供一种科学和高效的方法。本预测方法可以快速、准确判断5G室内覆盖方案的合理性和经济性

同时也降低了因覆盖方案不合理导致的后期建设维护成本。

Abstract

5G indoor depth coverage prediction is increasingly difficult as its coverage solution is more complex and diversified. One method to forecast accurately the effect of 5G indoor coverage is proposed. It combines ray-tracing algorithm with 3D modeling of buildings. This method can evaluate the rationality and economy of a 5G indoor coverage solution quickly and precisely

and it can also reduce the maintenance cost caused by unreasonable coverage solution.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Discussion on the NB-IoT network deployment scheme in a certain area

Related Author

刘方森

王建

Related Institution

中国移动通信集团设计院有限公司山东分公司

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰