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Knowledge-Driven Meta-Learning for CSI Feedback
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DOI：10.1109/TWC.2023.3327984.png)
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摘要
En 中文
Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.
Keyword:
Training
Metalearning
Wireless communication
Task analysis
Convergence
Downlink
Decoding
CSI feedback
massive MIMO
deep learning
meta-learning
knowledge-driven
期刊
IF:
10.7
论文数:
1.3W
被引数:
6.2W
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引用论文
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