A deep learning approach for reduce CSI feedback overhead in massive MIMO

Abstract

The Massive MIMO system in FDD communication mode is still one of the indispensable technologies in future cellular communication systems. In order to reduce CSI feedback overhead and enhance the quality of CSI channel reconstruction, this paper proposes a RiCsiNet network model and designs an iNetBlock module with larger convolutional kernels and a wider network structure. By connecting parallel convolution kernels of 1 × 1, 3 × 3, 5 × 5, and 7 × 7, both the width of the network and the receptive field are increased simultaneously, enabling calculation of different feature details at each layer. The compressed CSI is accurately estimated through residual connections at the BS end. The network model proposed in this paper, when considering both the model parameter size and the estimated CSI accuracy, outperforms the existing algorithm model in the NMSE index of 70%. In this paper, we verify that using different learning rate decay algorithms to train the model, a good learning rate decay algorithm can effectively improve the quality of the model, the best result is 12% higher than the worst result. Furthermore, we demonstrate that training different network models with varying amounts of data can not only save time but also maximize CSI channel estimation quality on the BS side. Finally, the RiCsiNet model was verified using different SNR under COST2100 channel model. QuaDRiGa data sets with different truncated subcarriers are used to verify the RiCsiNet model. The CSI estimated by the RiCsiNet model all showed high accuracy.