无线网络信号传输建模：一种区间二型模糊集成深度学习方法

赵亮; 谢志峰; 张坤鹏; 郑玉卿; 付园坤

doi:10.11959/j.issn.2096-6652.202043

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无线网络信号传输建模：一种区间二型模糊集成深度学习方法

专刊：深度强化学习 | 更新时间：2024-06-05

- 无线网络信号传输建模：一种区间二型模糊集成深度学习方法
- Modeling signal propagation in wireless network:an interval type-2 fuzzy ensemble deep learning approach
- 智能科学与技术学报 2020年2卷第4期页码：401-411
- 作者机构：
- 作者简介：
  
  [ "赵亮（1978- ），男，博士，河南工业大学电气工程学院副教授，主要研究方向为智能通信、智慧医疗、平行学习等。" ]
  [ "谢志峰（1996- ），男，河南工业大学电气工程学院硕士生，主要研究方向为深度学习、智能通信等。" ]
  [ "张坤鹏（1987- ），男，博士，河南工业大学电气工程学院讲师，主要研究方向为智能交通、智能通信等。" ]
  [ "郑玉卿（1987- ），男，博士，河南工业大学电气工程学院讲师，主要研究方向为智能通信、智能控制。" ]
  [ "付园坤（1995- ），男，河南工业大学电气工程学院硕士生，主要研究方向为智慧医疗、智能通信等。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61105079);国家自然科学基金资助项目;The National Natural Science Foundation of China(61473114);国家自然科学基金资助项目;The National Natural Science Foundation of China(62002101);河南省自然科学基金资助项目;The Natural Science Foundation of Henan Province of China(162300410059);河南工业大学省属高校基本科研业务费专项资金资助项目;Fundamental Research Funds for the Henan Provincial Col-leges and Universities in Henan University of Technology(2018RCJH16);河南工业大学高层次人才科研基金资助项目;High-level Talent Research Fund of Henan University of Technology(2019BS044)
- DOI：10.11959/j.issn.2096-6652.202043
  中图分类号： TP391
- 网络出版日期：2020-12，
  
  纸质出版日期：2020-12-15
- 稿件说明：
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赵亮, 谢志峰, 张坤鹏, 等. 无线网络信号传输建模：一种区间二型模糊集成深度学习方法[J]. 智能科学与技术学报, 2020,2(4):401-411.

Liang ZHAO, Zhifeng XIE, Kunpeng ZHANG, et al. Modeling signal propagation in wireless network:an interval type-2 fuzzy ensemble deep learning approach[J]. Chinese journal of intelligent science and technology, 2020, 2(4): 401-411.
赵亮, 谢志峰, 张坤鹏, 等. 无线网络信号传输建模：一种区间二型模糊集成深度学习方法[J]. 智能科学与技术学报, 2020,2(4):401-411. DOI： 10.11959/j.issn.2096-6652.202043.

Liang ZHAO, Zhifeng XIE, Kunpeng ZHANG, et al. Modeling signal propagation in wireless network:an interval type-2 fuzzy ensemble deep learning approach[J]. Chinese journal of intelligent science and technology, 2020, 2(4): 401-411. DOI： 10.11959/j.issn.2096-6652.202043.

摘要

针对常用的信号传输模型存在使用场景单一、预测精度不佳的问题，提出一种适用于多场景的数据驱动无线信号传输模型。首先根据先验知识从预处理后的数据构造初始特征，接着进行特征选择，以得到输入特征集合。然后分析建模需求，选择深度置信网络（DBN）、残差网络（ResNet）和堆叠自编码器（SAE）作为区间二型模糊规则的后件（个体深度学习器），经过区间二型模糊推理进行集成。最后采用5G网络信号传输实测数据，并进行实验验证。结果表明，3种个体深度学习器在测试集上的表现均优于Cost231-Hata模型和反向传播神经网络（BPNN）模型，其中ResNet的准确度高于DBN和SAE模型。区间二型模糊集成深度学习模型的性能与其个体深度学习器的性能以及模糊规则数目呈正相关，同时异质集成在测试集上的表现优于同质集成。

Abstract

There exist some problems in the commonly used signal propagation models

such as single usage scenario and poor prediction accuracy.A data-driven wireless signal propagation model suitable for multiple scenarios was proposed.Firstly

the initial features were constructed from the preprocessed data according to the prior knowledge

and then the input feature set was obtained by using feature selection technique.Then analyzing the modeling requirements

selecting the deep belief network (DBN)

residual network (ResNet) and stacked auto encoder (SAE) as the consequents (individual learners) of the interval type-2 fuzzy rules

and leveraged interval type-2 fuzzy inference to ensemble them.Finally

the actual measurement data of 5G signal propagation was applied for experimental verification.The results demonstrate that the performance of the three individual learners for the test set is better than those of the Cost231-Hata and back propagation neural network (BPNN)

as well as the accuracy of ResNet is higher than those of DBN and SAE.Moreover

the performance of the interval type-2 fuzzy ensemble deep learning model is positively correlated with those of its individual learners and the number of fuzzy rules.Meanwhile

the heterogeneous ensemble is superior to the homogeneous counterpart for the test set.

关键词

Keywords

references

SHAFI M , MOLISCH A F , SMITH P J , et al . 5G:a tutorial overview of standards,trials,challenges,deployment,and practice [J ] . IEEE Journal on Selected Areas in Communications , 2017 , 35 ( 6 ): 1201 - 1221 .

RAPPAPORT T S , MACCARTNEY G R , SAMIMI M K , et al . Wideband millimeter-wave propagation measurements and channel models for future wireless communication system design [J ] . IEEE Transactions on Communications , 2015 , 63 ( 9 ): 3029 - 3056 .

RAPPAPORT T S . Wireless communications:principles and practice [M ] . New Jersey : Prentice Hall PTR , 1996 .

ZAPPONE A , DI RENZO M , DEBBAH M . Wireless networks design in the era of deep learning:model-based,AI-based,or both? [J ] . IEEE Transactions on Communications , 2019 , 67 ( 10 ): 7331 - 7376 .

HATA M . Empirical formula for propagation loss in land mobile radio services [J ] . IEEE Transactions on Vehicular Technology , 1980 , 29 ( 3 ): 317 - 325 .

MEDEISIS A , KAJACKAS A . On the use of the universal Okumura-Hata propagation prediction model in rural areas [C ] // 2000 IEEE 51st Vehicular Technology Conference . Piscataway:IEEE Press , 2000 : 1815 - 1818 .

WERNER-ALLEN G , LORINCZ K , RUIZ M , et al . Deploying a wireless sensor network on an active volcano [J ] . IEEE Internet Computing , 2006 , 10 ( 2 ): 18 - 25 .

DUNCAN C A . Finite difference time domain analysis of fractal antennas used in wireless communications [D ] . Miami:Florida International University , 2004 .

POPOOLA S I , ATAYERO A A , FARUK N , et al . Standard propagation model tuning for path loss predictions in built-up environments [C ] // International Conference on Computational Science and Its Applications . Cham:Springer , 2017 : 363 - 375 .

SUN Y H , PENG M , ZHOU Y C , et al . Application of machine learning in wireless networks:key techniques and open issues [J ] . IEEE Communications Surveys ＆ Tutorials , 2019 , 21 ( 4 ): 3072 - 3108 .

ALDOSSARI S M , CHEN K C . Machine learning for wireless communication channel modeling:an overview [J ] . Wireless Personal Communications , 2019 , 106 ( 1 ): 41 - 70 .

POPESCU I , NIKITOPOULOS D , CONSTANTINOU P , et al . ANN prediction models for outdoor environment [C ] // 2006 IEEE 17th International Symposium on Personal,Indoor and Mobile Radio Communications . Piscataway:IEEE Press , 2006 : 1 - 5 .

ZHAO X N , HOU C P , WANG Q . A new SVM-based modeling method of cabin path loss prediction [J ] . International Journal of Antennas and Propagation , 2013 ( 2 ): 718 - 720 .

ZINEB A B , AYADI M . A multi-wall and multi-frequency indoor path loss prediction model using artificial neural networks [J ] . Arabian Journal for Science and Engineering , 2016 , 41 ( 3 ): 987 - 996 .

HERATH J D , SEETHARAM A , RAMESH A . A deep learning model for wireless channel quality prediction [C ] // 2019 IEEE International Conference on Communications . Piscataway:IEEE Press , 2019 : 1 - 6 .

SOTIROUDIS S P , GOUDOS S K , SIAKAVARA K . Deep learning for radio propagation:using image-driven regression to estimate path loss in urban areas [J ] . ICT Express , 2020 , 6 ( 3 ): 160 - 165 .

THRANE J , ZIBAR D , CHRISTIANSEN H L . Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz [J ] . IEEE Access , 2020 , 8 : 7925 - 7936 .

YUN Z Q , ISKANDER M F . Ray tracing for radio propagation modeling:principles and applications [J ] . IEEE Access , 2015 , 3 : 1089 - 1100 .

KHALID S , KHALIL T , NASREEN S . A survey of feature selection and feature extraction techniques in machine learning [C ] // 2014 Science and Information Conference . Piscataway:IEEE Press , 2014 : 372 - 378 .

CHANDRASHEKAR G , SAHIN F . A survey on feature selection methods [J ] . Computers ＆ Electrical Engineering , 2014 , 40 ( 1 ): 16 - 28 .

HINTON G E , SALAKHUTDINOV R R . Reducing the dimensionality of data with neural networks [J ] . Science , 2006 , 313 ( 5786 ): 504 - 507 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // The IEEE Conference On Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

ZADEH L A . The concept of a linguistic variable and its application on approximate reasoning-I [J ] . Information Sciences , 1975 , 8 ( 3 ): 199 - 249 .

MENDEL J M . Uncertain rule-based fuzzy systems:introduction and new directions (second edition) [M ] . Berlin : Springer , 2017 : 684 .

伍冬睿 , 曾志刚 , 莫红 , 等 . 区间二型模糊集和模糊系统:综述与展望 [J ] . 自动化学报 , 2020 , 46 ( 8 ): 1539 - 1556 .

WU D R , ZENG Z G , MO H , et al . Interval type-2 fuzzy sets and system:overview and outlook [J ] . Acta Automatica Sinica , 2020 , 46 ( 8 ): 1539 - 1556 .

DONG X B , YU Z W , CAO W M , et al . A survey on ensemble learning [J ] . Frontiers of Computer Science , 2020 : 1 - 18 .

KRAWCZYK B , MINKU L L , GAMA J , et al . Ensemble learning for data stream analysis:a survey [J ] . Information Fusion , 2017 , 37 : 132 - 156 .

BREIMAN L . Stacked regressions [J ] . Machine learning , 1996 , 24 ( 1 ): 49 - 64 .

WOLPERT D H . Stacked generalization [J ] . Neural networks , 1992 , 5 ( 2 ): 241 - 259 .

GARIBALDI JONATHAN M , 陈虹宇 , 李小双 , . 差异与学习:模糊系统与模糊推理 [J ] . 智能科学与技术学报 , 2019 , 1 ( 4 ): 319 - 326 .

GARIBALDI JONATHAN M , CHEN H Y , LI X S . Variation and learning:fuzzy system and fuzzy inference [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 4 ): 319 - 326 .

陈德旺 , 蔡际杰 , 黄允浒 . 面向可解释性人工智能与大数据的模糊系统发展展望 [J ] . 智能科学与技术学报 , 2019 , 1 ( 4 ): 327 - 334 .

CHEN D W , CAI J J , HUANG Y H . Development prospect of fuzzy system oriented to interpretable artificial intelligence and big data [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 4 ): 327 - 334 .

杜宏庆 , 陈德旺 , 黄允浒 , 等 . 基于改进遗传算法与支持度的模糊系统优化建模方法 [J ] . 智能科学与技术学报 , 2020 , 2 ( 2 ): 179 - 185 .

DU H Q , CHEN D W , HUANG Y H , et al . A fuzzy system optimization modeling method based on improved genetic algorithm and support degree [J ] . Chinese Journal of Intelligent Science and Technology , 2020 , 2 ( 2 ): 179 - 185 .

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