【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-inputmultiple-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(SHapleyAdditiveex Planations)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3GPPTR38.901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。更多还原

【Abstract】 Asymmetric massive multiple-input multiple-output(MIMO) systems have been proposed to reduce the burden of data processing and hardware cost in sixth-generation mobile networks(6G). However, in the asymmetric massive MIMO system, reciprocity between the uplink(UL) and downlink(DL) wireless channels is not valid. As a result, pilots are required to be sent by both the base station(BS) and user equipment(UE) to predict doubledirectional channels, which consumes more transmission and computational resources. In this paper we propose an ensemble-transfer-learning-based channel parameter prediction method for asymmetric massive MIMO systems. It can predict multiple DL channel parameters including path loss(PL), multipath number, delay spread(DS), and angular spread. Both the UL channel parameters and environment features are chosen to predict the DL parameters.Also, we propose a two-step feature selection algorithm based on the SHapley Additive exPlanations(SHAP) value and the minimum description length(MDL) criterion to reduce the computation complexity and negative impact on model accuracy caused by weakly correlated or uncorrelated features. In addition, the instance transfer method is introduced to support the prediction model in new propagation conditions, where it is difficult to collect enough training data in a short time. Simulation results show that the proposed method is more accurate than the back propagation neural network(BPNN) and the 3GPP TR 38.901 channel model. Additionally, the proposed instancetransfer-based method outperforms the method without transfer learning in predicting DL parameters when the beamwidth or the communication sector changes.更多还原