CLC number: TN92
On-line Access: 2023-02-27
Received: 2022-04-26
Revision Accepted: 2023-02-27
Crosschecked: 2022-09-28
Cited: 0
Clicked: 1335
Citations: Bibtex RefMan EndNote GB/T7714
Zunwen HE, Yue LI, Yan ZHANG, Wancheng ZHANG, Kaien ZHANG, Liu GUO, Haiming WANG. Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2200169 @article{title="Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems", %0 Journal Article TY - JOUR
在非对称大规模MIMO系统中基于集成-迁移学习的信道参数预测1北京理工大学信息与电子学院,中国北京市,100081 2东南大学毫米波国家重点实验室,中国南京市,210096 摘要:为降低第六代移动网络中的数据处理负担和硬件成本,非对称大规模多入多出(multiple-input multiple-output,MIMO)系统被提出。然而,在非对称大规模MIMO系统中,上行和下行无线信道之间的互易性是无效的。因此,需要基站和用户设备都发送导频来预测双向信道,这会消耗更多传输和计算资源。本文提出一种基于集成迁移学习的非对称大规模MIMO系统的信道参数预测方法,可以预测多个下行信道参数,包括路径损耗、多径数、时延扩展和角度扩展。选择上行信道参数和环境特征来预测下行参数。此外,提出一种基于SHAP(SHapley Additive exPlanations)值和最小描述长度标准的两步特征选择算法,以降低由弱相关或不相关特征引起的计算复杂度和对模型准确性的负面影响。引入实例迁移方法,以支持预测模型应对在新的传播条件下难以在短时间内收集足够训练数据的问题。仿真结果表明,该方法比反向传播神经网络和3GPP TR 38.901信道模型更准确。当波束宽度或通信扇区发生变化时,所提出的基于实例迁移的方法在预测下行参数方面优于没有迁移学习的方法。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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