AI-Driven Channel State Information (CSI) Extrapolation for 6G: Current Situations, Challenges and Future Research
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908799547211776 |
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| author | Gao, Yuan Lu, Zichen Wu, Xinyi Yu, Wenjun Liu, Shengli Du, Jianbo Jin, Yanliang Zhang, Shunqing Chu, Xiaoli Xu, Shugong |
| author_facet | Gao, Yuan Lu, Zichen Wu, Xinyi Yu, Wenjun Liu, Shengli Du, Jianbo Jin, Yanliang Zhang, Shunqing Chu, Xiaoli Xu, Shugong |
| contents | CSI extrapolation is an effective method for acquiring channel state information (CSI), essential for optimizing performance of sixth-generation (6G) communication systems. Traditional channel estimation methods face scalability challenges due to the surging overhead in emerging high-mobility, extremely large-scale multiple-input multiple-output (EL-MIMO), and multi-band systems. CSI extrapolation techniques mitigate these challenges by using partial CSI to infer complete CSI, significantly reducing overhead. Despite growing interest, a comprehensive review of state-of-the-art (SOTA) CSI extrapolation techniques is lacking. This paper addresses this gap by comprehensively reviewing the current status, challenges, and future directions of CSI extrapolation for the first time. Firstly, we analyze the performance metrics specific to CSI extrapolation in 6G, including extrapolation accuracy, adaption to dynamic scenarios and algorithm costs. We then review both model-driven and artificial intelligence (AI)-driven approaches for time, frequency, antenna, and multi-domain CSI extrapolation. Key insights and takeaways from these methods are summarized. Given the promise of AI-driven methods in meeting performance requirements, we also examine the open-source channel datasets and simulators that could be used to train high-performance AI-driven CSI extrapolation models. Finally, we discuss the critical challenges of the existing research and propose perspective research opportunities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00159 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-Driven Channel State Information (CSI) Extrapolation for 6G: Current Situations, Challenges and Future Research Gao, Yuan Lu, Zichen Wu, Xinyi Yu, Wenjun Liu, Shengli Du, Jianbo Jin, Yanliang Zhang, Shunqing Chu, Xiaoli Xu, Shugong Signal Processing CSI extrapolation is an effective method for acquiring channel state information (CSI), essential for optimizing performance of sixth-generation (6G) communication systems. Traditional channel estimation methods face scalability challenges due to the surging overhead in emerging high-mobility, extremely large-scale multiple-input multiple-output (EL-MIMO), and multi-band systems. CSI extrapolation techniques mitigate these challenges by using partial CSI to infer complete CSI, significantly reducing overhead. Despite growing interest, a comprehensive review of state-of-the-art (SOTA) CSI extrapolation techniques is lacking. This paper addresses this gap by comprehensively reviewing the current status, challenges, and future directions of CSI extrapolation for the first time. Firstly, we analyze the performance metrics specific to CSI extrapolation in 6G, including extrapolation accuracy, adaption to dynamic scenarios and algorithm costs. We then review both model-driven and artificial intelligence (AI)-driven approaches for time, frequency, antenna, and multi-domain CSI extrapolation. Key insights and takeaways from these methods are summarized. Given the promise of AI-driven methods in meeting performance requirements, we also examine the open-source channel datasets and simulators that could be used to train high-performance AI-driven CSI extrapolation models. Finally, we discuss the critical challenges of the existing research and propose perspective research opportunities. |
| title | AI-Driven Channel State Information (CSI) Extrapolation for 6G: Current Situations, Challenges and Future Research |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.00159 |