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

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. Specifically, contrasting with conventional approaches that lead to bandwidth inefficiency in distributed data collection, this letter proposes Dig-CSI which adopts a unique approach where each user equipment (UE) acts as a distributed lightweight generator for dataset creation, utilizing local data without necessitating uploads. This design involves each UE training an autoencoder, with its decoder acting as the distributed generator, which enhances reconstruction accuracy and generative performance. Our experimental results reveal that Dig-CSI efficiently trains a global CSI feedback model, matching the performance of conventional centralized learning models while significantly minimizing communication overhead.