  Channel state information (CSI) feedback is critical for achieving the
promised advantages of enhancing spectral and energy efficiencies in massive
multiple-input multiple-output (MIMO) wireless communication systems. Deep
learning (DL)-based methods have been proven effective in reducing the required
signaling overhead for CSI feedback. In practical dual-polarized MIMO
scenarios, channels in the vertical and horizontal polarization directions tend
to exhibit high polarization correlation. To fully exploit the inherent
propagation similarity within dual-polarized channels, we propose a
disentangled representation neural network (NN) for CSI feedback, referred to
as DiReNet. The proposed DiReNet disentangles dual-polarized CSI into three
components: polarization-shared information, vertical polarization-specific
information, and horizontal polarization-specific information. This
disentanglement of dual-polarized CSI enables the minimization of information
redundancy caused by the polarization correlation and improves the performance
of CSI compression and recovery. Additionally, flexible quantization and
network extension schemes are designed. Consequently, our method provides a
pragmatic solution for CSI feedback to harness the physical MIMO polarization
as a priori information. Our experimental results show that the performance of
our proposed DiReNet surpasses that of existing DL-based networks, while also
effectively reducing the number of network parameters by nearly one third.
