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The advent of deep learning (DL)-based models has significantly advancedChannel State Information (CSI) feedback mechanisms in wireless communicationsystems. However, traditional approaches often suffer from high communicationoverhead and potential privacy risks due to the centralized nature of CSI dataprocessing. To address these challenges, we design a CSI feedback trainingframework called Dig-CSI, in which the dataset for training the CSI feedbackmodel is produced by the distributed generators uploaded by each user equipment(UE), but not through local data upload. Each UE trains an autoencoder, wherethe decoder is considered as the distributed generator, with local data to gainreconstruction accuracy and the ability to generate. Experimental results showthat Dig-CSI can train a global CSI feedback model with comparable performanceto the model trained with classical centralized learning with a much lightercommunication overhead.

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