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Frontiers of Information Technology & Electronic Engineering 

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Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems


Author(s):  Zunwen HE, Yue LI, Yan ZHANG, Wancheng ZHANG, Kai-en ZHANG, Liu GUO, Haiming WANG

Affiliation(s):  School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China; more

Corresponding email(s):  zhangy@bit.edu.cn

Key Words:  Asymmetric massive multiple-input multiple-output (MIMO) system; Channel model; Ensemble learning; Instance transfer; Parameter prediction


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Zunwen HE, Yue LI, Yan ZHANG, Wancheng ZHANG, Kai-en ZHANG,Liu GUO, Haiming WANG. Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems[J]. Frontiers of Information Technology & Electronic Engineering , 1998, -1(1): .

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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200169"
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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 (6G) mobile networks. 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. This paper proposes an ensemble-transfer-learning-based channel parameter prediction method is proposed 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 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 instance-transfer-based method outperforms the method without transfer learning for predicting DL parameters when the beamwidth or the communication sector changes.

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