  Due to the ability of feature extraction, deep learning (DL)-based methods
have been recently applied to channel state information (CSI) compression
feedback in massive multiple-input multiple-output (MIMO) systems. Existing
DL-based CSI compression methods are usually effective in extracting a certain
type of features in the CSI. However, the CSI usually contains two types of
propagation features, i.g., non-line-of-sight (NLOS) propagation-path feature
and dominant propagation-path feature, especially in channel environments with
rich scatterers. To fully extract the both propagation features and learn a
dual-feature representation for CSI, this paper proposes a dual-feature-fusion
neural network (NN), referred to as DuffinNet. The proposed DuffinNet adopts a
parallel structure with a convolutional neural network (CNN) and an
attention-empowered neural network (ANN) to respectively extract different
features in the CSI, and then explores their interplay by a fusion NN. Built
upon this proposed DuffinNet, a new encoder-decoder framework is developed,
referred to as Duffin-CsiNet, for improving the end-to-end performance of CSI
compression and reconstruction. To facilitate the application of Duffin-CsiNet
in practice, this paper also presents a two-stage approach for codeword
quantization of the CSI feedback. Besides, a transfer learning-based strategy
is introduced to improve the generalization of Duffin-CsiNet, which enables the
network to be applied to new propagation environments. Simulation results
illustrate that the proposed Duffin-CsiNet noticeably outperforms the existing
DL-based methods in terms of reconstruction performance, encoder complexity,
and network convergence, validating the effectiveness of the proposed
dual-feature fusion design.
