Federated Unfolding Learning for CSI Feedback in Distributed Edge Networks

In distributed edge networks employing frequency division duplex, the feedback of channel state information (CSI) from the edge devices to the edge server always consumes a lot of spectrum resources, resulting in a serious communication burden. In this paper, we first propose an end-to-end unfolding neural network framework inspired by the soft threshold iterative algorithm (U-ISTANet). The proposed U-ISTANet integrates the advantages of compression awareness and neural networks. Especially, the compression matrix and sparse transformation of channel matrix can be learned for accurate CSI compression and recovery. And a lightweight version of U-ISTANet, called U-ISTANet-L, is proposed to reduce the training parameters. To reduce the data transmission overhead in the centralized learning framework, we extend the proposed U-ISTANet-L to a federated U-ISTANet-L (FU-ISTANet-L), which can train a more generalizable model by increasing the number of edge devices to enlarge the data set in a distributed learning manner. The proposed FU-ISTANet-L reduces the transmission overhead and increases the training speed while achieving a performance close to that of centralized learning. Furthermore, we propose a personalized FU-ISTANet-L (P-FU-ISTANet-L) to solve the heterogeneous data training problem in different communication environments. Specifically, we first obtain a pre-trained model by federation unfolding learning, and then each edge device fine-tunes the model using only a small amount of train data to obtain a personalized model for local channel environment. Extensive experimental results are provided to show that the proposed networks achieve a significant performance over the benchmarking schemes in terms of the normalized mean square error.