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arXiv · 2304.00070

HybridCVLNet: A Hybrid CSI Feedback System and its Domain Adaptation

Haozhen Li·Xinyu Gu·Boyuan Zhang·Dongliang Li·Zhenyu Liu·Lin Zhang
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Abstract

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.

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Haozhen Li, Xinyu Gu, Boyuan Zhang, Dongliang Li, Zhenyu Liu, Lin Zhang. 2023-03-30. HybridCVLNet: A Hybrid CSI Feedback System and its Domain Adaptation. https://arxiv.org/abs/2304.00070

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