基于AI的信道估计的泛化性能提升方法 孙布勒.pdf

无线电通信技术
Radio Communications Technology
ISSN 1003-3114,CN 13-1099/TN
，基于 MAML 的方案以最少的微调次数实现了最高的信道估计精
度，是一种非常有潜力的训练方案。
关键词：泛化；无线通信；迁移学****联合训练；MAML；微调
中图分类号：TN92 文献标志码：A

Approaches for Improving the Generalization Capability of AI Based Channel Estimation
SUN Bule, YANG Ang, SUN Peng, JIANG Dajie
（Vivo Mobile Communication Co., Ltd, Beijing 100015, China)
Abstract: Artificial intelligence (AI) technology will play an important role in future wireless communications,
in which channel estimation is a typical combination of AI and wireless communication technologies. AI-based
channel estimation techniques can significantly improve estimation accuracy, especially for cases with low signal to
noise ratio (SNR) and nonlinear impacts. However, AI based schemes have the common problem of insufficient
generalization capability, especially in the scenario where channel estimation is frequently changed and labeled data
is difficult to obtain. To improve the generalization performance, an AI-based channel estimation scheme combining
transfer learning, joint training and model-agnostic meta-learning (MAML) is