Interacting Federated and Transfer Learning-Aided CSI Prediction for Intelligent Cellular Networks
To establish more intelligent cellular networks for future ubiquitous access and heterogeneous devices, we need to obtain channel state information (CSI) in a more agile and economical manner, especially for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) architectures. Unlike conventional CSI feedback or limited feedback methods, we can predict downlink CSI by leveraging channel reciprocity between uplink and downlink. The downlink CSI prediction can be formulated as a data-driven deep learning task, however, there exist isolated data silos and online adaptation problem for the offline trained neural network-based models. In this article, we propose an interacting federated and transfer learning (IFTL) based framework for downlink CSI prediction and online update, where several factors including asynchrony of different clients are considered, and light heterogeneity of diverse cells can be tolerated. Both model-level and link-level simulations are conducted under standardized FDD massive MIMO scenarios. The results outline promising prospect and potential of the utilization of federated learning and transfer learning in physical layer of wireless communications.