  The latest TypeII codebook selects partial strongest angular-delay ports for
the feedback of downlink channel state information (CSI), whereas its
performance is limited due to the deficiency of utilizing the correlations
among the port coefficients. To tackle this issue, we propose a tailored
autoencoder named TypeII-CsiNet to effectively integrate the TypeII codebook
with deep learning, wherein three novel designs are developed for sufficiently
boosting the sum rate performance. Firstly, a dedicated pre-processing module
is designed to sort the selected ports for reserving the correlations of their
corresponding coefficients. Secondly, a position-filling layer is developed in
the decoder to fill the feedback coefficients into their ports in the recovered
CSI matrix, so that the corresponding angular-delay-domain structure is
adequately leveraged to enhance the reconstruction accuracy. Thirdly, a
two-stage loss function is proposed to improve the sum rate performance while
avoiding the trapping in local optimums during model training. Simulation
results verify that our proposed TypeII-CsiNet outperforms the TypeII codebook
and existing deep learning benchmarks.
