  Deep Learning (DL)-based channel state information (CSI) feedback is a
promising technique for the transmitter to accurately acquire the CSI of
massive multiple-input multiple-output (MIMO) systems. As critical concerns
about DL-based physical layer applications, the intra-domain generalizability
affected by dataset bias and inter-domain robustness in data drift remain
challenging. Therefore, we build on a Hybrid Complex-Valued Lightweight
framework, namely the HybridCVLNet, capable of overcoming the dataset bias with
regularized hybrid structure and codeword. Meanwhile, a corresponding
transductive-based hybrid domain adaptation scheme is proposed to tackle the
inter-domain data drift. The experiment verifies that HybridCVLNet achieves
stable generalizability and performance gain over the state-of-the-art (SOTA)
feedback schemes in an intra-domain heterogeneous dataset. In addition, its
transductive-based hybrid domain adaptation scheme is more efficient and
superior to the inductive-based transfer learning methods under two
inter-domain online re-optimization settings.
