A Novel Atmosphere-Informed Data-Driven Predictive Channel Modeling for B5G/6G Satellite-Terrestrial Wireless Communication Systems at Q-Band
A Novel Atmosphere-Informed Data-Driven Predictive Channel Modeling for B5G/6G Satellite-Terrestrial Wireless Communication Systems at Q-Band
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DOI：
10.1109/tvt.2020.3037212
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发表时间：
2020-12-01
影响因子：
6.8
通讯作者：
Goussetis, George
中科院分区：
文献类型：
--
作者：
Bai, Lu;Xu, Qian;Goussetis, George
提出了一种新的Q频段B5G/6G卫星-地面无线通信系统的大气感知预测卫星信道模型，用于模拟和预测任意时刻的信道衰落。该模型是一种基于多层感知器(MLP)和长短期记忆(LSTM)两种深度学习网络的数据驱动模型。信道模型的精确度由信道衰落的绝对误差和均方误差(MSE)的累积密度函数(CDF)衡量。通过深度学习网络的训练时间、加载时间和测试时间来评估所提出的信道模型的复杂性。为了进一步提高通道模型的精度，在数据库建设阶段进行了天气分类。基于我们建立的信道和天气测量活动，综合考察和分析了基于不同深度学习网络(如MLP和LSTM)的数据驱动信道模型的性能。最后，利用大气信息预测卫星信道模型对信道衰落进行了建模/预测，结果与实际信道测量结果吻合较好，验证了该模型的实用性。
This paper proposes a novel atmosphere-informed predictive satellite channel model for beyond the fifth-generation (B5G)/the sixth-generation (6G) satellite-terrestrial wireless communication systems at Q-band to model/predict channel attenuation at any specific time. The proposed channel model is a data-driven model based on either of two deep learning networks, i.e., multi-layer perceptron (MLP) and long short-term memory (LSTM). The accuracy of the proposed channel model is measured by cumulative density function (CDF) of absolute error and mean square error (MSE) between modeled/predicted and measured channel attenuation. The complexity of the proposed channel model is assessedby the training time, loading time, and test time of deep learning networks. To further improve the accuracy of the proposed channel model, weather classification is developed at the stage of database construction. Based on our established channel and weather measurement campaign, the performance of the proposed data-driven channel model based on different deep learning networks, e.g., MLP and LSTM, with or without the weather classification is investigated and analyzed comprehensively. Finally, the close agreement is achieved between the channel attenuation modeled/predicted from the proposed atmosphere-informed predictive satellite channel model and the one from real channel measurements, verifying the utility of proposed channel model.