Dig-CSI: A Distributed and Generative Model Assisted CSI Feedback Training Framework

• URL: http://arxiv.org/abs/2312.05921v1
• Date: Sun, 10 Dec 2023 15:55:57 GMT
• Title: Dig-CSI: A Distributed and Generative Model Assisted CSI Feedback Training Framework
• Authors: Zhilin Du, Haozhen Li, Zhenyu Liu, Shilong Fan, Xinyu Gu, Lin Zhang
• Abstract summary: We design a CSI feedback training framework called Dig-CSI. The dataset for training the CSI feedback model is produced by the distributed generators uploaded by each user equipment (UE) but not through local data upload. Experimental results show that Dig-CSI can train a global CSI feedback model with comparable performance to the model trained with classical centralized learning.
• Score: 6.216538343278333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of deep learning (DL)-based models has significantly advanced Channel State Information (CSI) feedback mechanisms in wireless communication systems. However, traditional approaches often suffer from high communication overhead and potential privacy risks due to the centralized nature of CSI data processing. To address these challenges, we design a CSI feedback training framework called Dig-CSI, in which the dataset for training the CSI feedback model is produced by the distributed generators uploaded by each user equipment (UE), but not through local data upload. Each UE trains an autoencoder, where the decoder is considered as the distributed generator, with local data to gain reconstruction accuracy and the ability to generate. Experimental results show that Dig-CSI can train a global CSI feedback model with comparable performance to the model trained with classical centralized learning with a much lighter communication overhead.

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