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Due to the discarding of downlink channel state information (CSI) amplitudeand the employing of iteration reconstruction algorithms, 1-bit compressedsensing (CS)-based superimposed CSI feedback is challenged by low recoveryaccuracy and large processing delay. To overcome these drawbacks, this letterproposes a fusion learning scheme by exploiting the bi-directional channelreciprocity. Specifically, a simplified version of the conventional downlinkCSI reconstruction is utilized to extract the initial feature of downlink CSI,and a single hidden layer-based amplitude-learning network (AMPL-NET) isdesigned to learn the auxiliary feature of the downlink CSI amplitude. Then,based on the extracted and learned amplitude features, a simple but effectiveamplitude-fusion network (AMPF-NET) is developed to perform the amplitudefusion of downlink CSI and thus improves the reconstruction accuracy for 1-bitCS-based superimposed CSI feedback while reducing the processing delay.Simulation results show the effectiveness of the proposed feedback scheme andthe robustness against parameter variations.

Fusion Learning for 1-Bit CS-based Superimposed CSI Feedback with Bi-Directional Channel Reciprocity