In massive multiple-input multiple-output (mMIMO) systems, traditional deep autoencoder methods for CSI feedback require substantial amounts of data, raising concerns around bandwidth consumption and user privacy. To address these challenges, the Gossip-GAN framework minimizes data requirements while enhancing feedback accuracy through a novel GAN model. This methodology enables users to collect minimal data, and utilize a decentralized learning approach that not only speeds up the training process but also protects user privacy effectively.
The implications of this framework are significant for mobile users who frequently encounter varying channel conditions. By mitigating catastrophic forgetting, the Gossip-GAN ensures consistent performance even when users return to familiar environments. Furthermore, the inherent robustness of this approach leads to increased reliability and efficiency in CSI feedback systems, significantly reducing uplink bandwidth usage. Overall, the Gossip-GAN framework is poised to offer a more practical and efficient solution for real-world mMIMO applications, balancing accuracy with crucial privacy considerations.
👉 Pročitaj original: arXiv AI Papers
