Chinese Journal of Intelligent Science and Technology ›› 2020, Vol. 2 ›› Issue (4): 401-411.doi: 10.11959/j.issn.2096-6652.202043

• Special Issue: Deep Reinforcement Learning • Previous Articles    

Modeling signal propagation in wireless network:an interval type-2 fuzzy ensemble deep learning approach

Liang ZHAO, Zhifeng XIE, Kunpeng ZHANG, Yuqing ZHENG, Yuankun FU   

  1. College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
  • Revised:2020-11-30 Online:2020-12-15 Published:2020-12-01
  • Supported by:
    The National Natural Science Foundation of China(61105079);The National Natural Science Foundation of China(61473114);The National Natural Science Foundation of China(62002101);The Natural Science Foundation of Henan Province of China(162300410059);Fundamental Research Funds for the Henan Provincial Col-leges and Universities in Henan University of Technology(2018RCJH16);High-level Talent Research Fund of Henan University of Technology(2019BS044)


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.

Key words: signal propagation model, feature engineering, interval type-2 fuzzy inference, ensemble learning, deep learning

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