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

doi:10.11959/j.issn.2096-6652.202043

Abstract

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.

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

CLC Number:

TP391

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.

Figures/Tables 12

References 32

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特征名称	特征含义
dis_2d	信号接收端与信号发射端之间的二维距离
dis_2d_log	信号接收端与信号发射端之间二维距离的对数形式
dis_3d	信号接收端与信号发射端之间的三维距离
dis_3d_log	信号接收端与信号发射端之间三维距离的对数形式
h	基站发射机的有效高度
h_log	基站发射机有效高度的对数形式
hori_beta	信号接收端和信号发射端的连线与发射机信号发射方向之间的二维夹角
spare_beta	信号接收端和信号发射端的连线与发射机信号发射方向之间的三维夹角

模型	MaxPE/dBm	MAE/dBm	MAPE	RMSE/dBm
Cost231-Hata	118.799	19.289	21.655%	25.952
BPNN	52.028	10.263	12.097%	12.557
ResNet	43.545	7.112	7.946%	9.025
SAE	42.889	7.445	8.303%	9.397
DBN	47.012	7.419	8.413%	9.352

模型	MaxPE/dBm	MAE/dBm	MAPE	RMSE/dBm
IT2_ResNet3	42.727	6.923	7.732%	8.794
IT2_ResNet4	43.010	6.907	7.715%	8.772
IT2_ResNet5	43.344	6.904	7.711%	8.770
IT2_DBN3	42.018	7.332	8.213%	9.264
IT2_DBN4	42.545	7.249	8.117%	9.167
IT2_DBN5	45.084	7.127	7.976%	9.034
IT2_SAE3	42.995	7.454	8.357%	9.404
IT2_SAE4	43.096	7.429	8.331%	9.377
IT2_SAE5	41.643	7.387	8.278%	9.324

模型	MaxPE/dBm	MAE/dBm	MAPE	RMSE/dBm
IT2_RSD	41.628	6.959	7.778%	8.836
IT2_2RSD	41.742	6.909	7.722%	8.775
IT2_R2SD	41.895	6.947	7.753%	8.827
IT2_RS2D	41.910	6.955	7.778%	8.828
IT2_3RSD	42.153	6.883	7.663%	8.758
IT2_2R2SD	41.809	6.906	7.713%	8.775
IT2_2RS2D	42.676	6.891	7.679%	8.763
IT2_R3SD	43.464	6.951	7.767%	8.827
IT2_R2S2D	41.057	6.949	7.763%	8.825

个体深度学习器	评价标准	集成方法
个体深度学习器	评价标准	FSE	FCE	BE	IT2FE
ResNet3	MaxPE/dBm	43.344	43.250	43.450	42.727
	MAE/dBm	7.011	6.969	6.999	6.923
	MAPE	7.838%	7.768%	7.820%	7.732%
	RMSE/dBm	8.899	8.850	8.885	8.794
RSD	MaxPE/dBm	42.553	41.596	43.318	41.628
	MAE/dBm	7.025	6.969	7.010	6.959
	MAPE	7.858%	7.792%	7.842%	7.778%
	RMSE/dBm	8.915	8.846	8.895	8.836

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

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