基于计算图的移动通信网络物联网业务覆盖优化算法及实现

doi:10.11959/j.issn.2096-3750.2019.00102

物联网学报 ›› 2019, Vol. 3 ›› Issue (2): 100-107.doi: 10.11959/j.issn.2096-3750.2019.00102

基于计算图的移动通信网络物联网业务覆盖优化算法及实现

王浩彬^1,^2,³,皇甫伟^1,²,刘娅汐^1,²,刘玮⁴

¹ 北京科技大学计算机与通信工程学院人工智能研究院，北京 100083
² 北京市融合网络与泛在业务工程技术研究中心，北京 100083
³ 中国电信股份有限公司北京研究院，北京 102209
⁴ 中国移动通信集团设计院有限公司，北京 100080

修回日期:2019-03-25 出版日期:2019-06-30 发布日期:2019-07-17
作者简介:王浩彬（1993? ），男，北京人，北京科技大学计算机与通信工程学院硕士生，中国电信股份有限公司北京研究院工程师，主要研究方向为无线网络规划、深度学习和移动通信。|皇甫伟（1975? ），男，河南开封人，博士，北京科技大学计算机与通信工程学院副教授，主要研究方向为移动通信和物联网。|刘娅汐（1994? ），女，山东烟台人，北京科技大学计算机与通信工程学院博士生，主要研究方向为基站网络优化与规划、绿色通信和传感器覆盖。|刘玮（1986? ），女，山西太原人，硕士，中国移动通信集团设计院有限公司工程师，主要研究方向为移动通信网络规划。
基金资助:
教育部-中国移动科研基金资助项目(MCM20160103);无线自组织网络仿生学机理及其优化算法研究资助项目(61370191)

Coverage optimization algorithm and implementation based on computational graph for mobile communication network and IoT service

Haobin WANG^1,^2,³,Wei HUANGFU^1,²,Yaxi LIU^1,²,Wei LIU⁴

¹ Institute of Artificial Intelligence,School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China
² Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services,Beijing 100083,China
³ China Telecom Beijing Research Institute (CTBRI) under China Telecom Co.,Ltd.,Beijing 102209,China
⁴ China Mobile Group Design Institute Co.,Ltd.,Beijing 100080,China

Revised:2019-03-25 Online:2019-06-30 Published:2019-07-17
Supported by:
The Joint Foundation Project of Ministry of Education and China Mobile Group(MCM20160103);The Foundation Project of Research on Bionics Mechanism and Optimized Algorithms of Wireless Ad Hoc Networks(61370191)

摘要/Abstract

摘要：

面向物联网高密度、大连接和差异性服务质量的移动通信网络优化具有重要的研究意义。在此条件下，移动通信网络优化是一种多参数的、针对高计算成本函数的复杂优化问题，基于计算图的移动通信网络覆盖质量评估的计算方式，为运算的并行化提供依据，进而基于计算图获得了覆盖质量指标的梯度计算方式，利用基于反向传播获取的梯度信息指导基站天线工作参数的优化，并引入动量法加速了优化问题的收敛速度。仿真结果表明，本算法适用于移动通信网络的覆盖优化计算。

关键词: 物联网, 5G, 网络参数优化, 计算图, 动量法

Abstract:

The research on the mobile communication network optimization for the Internet of things large connection and differential service quality has great significance.Mobile communication network optimization is a multi-parameter complex optimization problem with high computational cost function.In order to provide the basis for parallelization of operations,the calculation method of the coverage quality assessment of the mobile communication network based on the computational graph was introduced.Based on the calculation graph,the derivative calculation method of the coverage quality index was obtained,by back propagation to guide the optimization of the antenna parameters.The momentum method was used to accelerate the convergence speed of the optimization algorithm.The simulation results show that the algorithm is suitable for the coverage optimization in mobile communication network.

Key words: Internet of things, 5G, network parameter optimization, computational graph, momentum method

中图分类号:

TN915

王浩彬,皇甫伟,刘娅汐,刘玮. 基于计算图的移动通信网络物联网业务覆盖优化算法及实现[J]. 物联网学报, 2019, 3(2): 100-107.

Haobin WANG,Wei HUANGFU,Yaxi LIU,Wei LIU. Coverage optimization algorithm and implementation based on computational graph for mobile communication network and IoT service[J]. Chinese Journal on Internet of Things, 2019, 3(2): 100-107.

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参考文献 20

[1]	AL-FALAHY N , ALANI O Y . Technologies for 5G networks:challenges and opportunities[J]. IT Professional, 2017,19(1): 12-20.
[2]	RATASUK R , VEJLGAARD B , MANGALVEDHE N ,et al. NB-IoT system for M2M communication[C]// 2016 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). IEEE, 2016.
[3]	SINHA R S , WEI Y Q , HWANG S H . A survey on LPWA technology:LoRa and NB-IoT[J]. ICT Express, 2017,3(1): 14-21.
[4]	RATASUK R , VEJLGAARD B , MANGALVEDHE N ,et al. NB-IoT system for M2M communication[C]// Wireless Communications and Networking Conference (WCNC). IEEE, 2016: 1-5.
[5]	FAN P Z , ZHAO J , CHIH-LIN I . 5G high mobility wireless communications:challenges and solutions[J]. China Communications, 2016,13(2): 1-13.
[6]	BUZZI S , CHIH-LIN I , KLEIN T E ,et al. A survey of energy-efficient techniques for 5G networks and challenges ahead[J]. IEEE Journal on Selected Areas in Communications, 2016,34(4): 697-709.
[7]	BOCCARDI F , HEATH R W , LOZANO A ,et al. Five disruptive technology directions for 5G[J]. IEEE Communications Magazine, 2013,52(2): 74-80.
[8]	MENGJUN Y , LEI F , WENJING L ,et al. Cell outage compensation based on CoMP and optimization of tilt[J]. The Journal of China Universities of Posts and Telecommunications, 2015,22(5): 71-79.
[9]	EI-BEAINO W , EI-HAJJ A M , DAWY Z . On radio network planning for next generation 5G networks:a case study[C]// International Conference on Communications. IEEE, 2015.
[10]	PHAN N Q , BUI T O , JIANG H L ,et al. Coverage optimization of LTE networks based on antenna tilt adjusting considering network load[J]. China Communications, 2017,14(5): 48-58.
[11]	HUANG L , ZHOU Y Q , HU J L ,et al. Coverage optimization for femtocell clusters using modified particle swarm optimization[C]// IEEE International Conference on Communications. IEEE, 2012: 611-615.
[12]	GAO M H , HUANG L F , CUI X N ,et al. Intelligent coverage optimization with multi-objective genetic algorithm in cellular system[C]// Computer Science ＆ Education (ICCSE). IEEE, 2013: 859-863.
[13]	ABADI M , BARHAM P , CHEN J M ,et al. TensorFlow:a system for large-scale machine learning[C]// OSDI. IEEE, 2016: 265-283.
[14]	SCHAUBACH K R , DAVIS N J , RAPPAPORT T S . A ray tracing method for predicting path loss and delay spread in microcellular environments[C]// Vehicular Technology Conference. IEEE, 2002.
[15]	SINGH Y . Comparison of okumura,hata and cost—231 models on the basis of path loss and signal strength[J]. International Journal of Computer Applications, 2012,59(11): 37-41.
[16]	BAUER F L . Computational graphs and rounding error[J]. SIAM Journal on Numerical Analysis, 1974,11(1): 87-96.
[17]	YVES C , RUMELHART D E . Backpropagation:theory,architectures and applications[M]. London: Psychology PressPress, 2013.
[18]	LéON B . Large-scale machine learning with stochastic gradient descent[J]. 19th International Conference on Computational Statistics, 2010: 177-186.
[19]	KINGMA D , BA J . Adam:a method for stochastic optimization[J]. Computer Science, 2014:
[20]	SUTSKEVER I , MARTENS J , DAHL G ,et al. On the importance of initialization and momentum in deep learning[C]// International Conference on Machine Learning. IEEE, 2013.

参数	数值
发射功率	18.2 dBm
天线频率	2 600 MHz
N_s	40 000
N_a	70
噪声	-110 dBm
Th ^RSRP	-88 dBm
T ^SINRh	-3 dBm

基于计算图的移动通信网络物联网业务覆盖优化算法及实现

Coverage optimization algorithm and implementation based on computational graph for mobile communication network and IoT service

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