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Modeling signal propagation in wireless network:an interval type-2 fuzzy ensemble deep learning approach
- Chinese Journal of Intelligent Science and Technology Vol. 2, Issue 4, Pages: 401-411(2020)
- 作者机构：
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202043
CLC： TP391-
Published Online：2020-12，
Published：15 December 2020
- 稿件说明：
移动端阅览
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.
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
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