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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.22840 |
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| _version_ | 1866911426777448448 |
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| author | Wang, Haoyu Sun, Zhi Han, Shuangfeng Wang, Xiaoyun Wang, Zhaocheng |
| author_facet | Wang, Haoyu Sun, Zhi Han, Shuangfeng Wang, Xiaoyun Wang, Zhaocheng |
| contents | Deep learning is promising to enhance the accuracy and reduce the overhead of channel state information (CSI) feedback, which can boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Nevertheless, the generalizability of current deep learning-based CSI feedback algorithms cannot be guaranteed in unseen environments, which induces a high deployment cost. In this paper, the generalizability of deep learning-based CSI feedback is promoted with physics interpretation. Firstly, the distribution shift of the cluster-based channel is modeled, which comprises the multi-cluster structure and single-cluster response. Secondly, the physics-based distribution alignment is proposed to effectively address the distribution shift of the cluster-based channel, which comprises multi-cluster decoupling and fine-grained alignment. Thirdly, the efficiency and robustness of physics-based distribution alignment are enhanced. Explicitly, an efficient multi-cluster decoupling algorithm is proposed based on the Eckart-Young-Mirsky (EYM) theorem to support real-time CSI feedback. Meanwhile, a hybrid criterion to estimate the number of decoupled clusters is designed, which enhances the robustness against channel estimation error. Fourthly, environment-generalizable neural network for CSI feedback (EG-CsiNet) is proposed as a novel learning framework with physics-based distribution alignment. Based on extensive simulations and sim-to-real experiments in various conditions, the proposed EG-CsiNet can robustly reduce the generalization error by more than 3 dB compared to the state-of-the-arts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22840 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalizable Learning for Massive MIMO CSI Feedback in Unseen Environments Wang, Haoyu Sun, Zhi Han, Shuangfeng Wang, Xiaoyun Wang, Zhaocheng Signal Processing Deep learning is promising to enhance the accuracy and reduce the overhead of channel state information (CSI) feedback, which can boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Nevertheless, the generalizability of current deep learning-based CSI feedback algorithms cannot be guaranteed in unseen environments, which induces a high deployment cost. In this paper, the generalizability of deep learning-based CSI feedback is promoted with physics interpretation. Firstly, the distribution shift of the cluster-based channel is modeled, which comprises the multi-cluster structure and single-cluster response. Secondly, the physics-based distribution alignment is proposed to effectively address the distribution shift of the cluster-based channel, which comprises multi-cluster decoupling and fine-grained alignment. Thirdly, the efficiency and robustness of physics-based distribution alignment are enhanced. Explicitly, an efficient multi-cluster decoupling algorithm is proposed based on the Eckart-Young-Mirsky (EYM) theorem to support real-time CSI feedback. Meanwhile, a hybrid criterion to estimate the number of decoupled clusters is designed, which enhances the robustness against channel estimation error. Fourthly, environment-generalizable neural network for CSI feedback (EG-CsiNet) is proposed as a novel learning framework with physics-based distribution alignment. Based on extensive simulations and sim-to-real experiments in various conditions, the proposed EG-CsiNet can robustly reduce the generalization error by more than 3 dB compared to the state-of-the-arts. |
| title | Generalizable Learning for Massive MIMO CSI Feedback in Unseen Environments |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2512.22840 |