Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.15468 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914398546690048 |
|---|---|
| author | Kopnina, Irina Artemasov, Dmitry Matveev, Sergey |
| author_facet | Kopnina, Irina Artemasov, Dmitry Matveev, Sergey |
| contents | Accurate channel state information (CSI) prediction is crucial for next-generation multiple-input multiple-output (MIMO) communication systems. Classical prediction methods often become inefficient for high-dimensional and rapidly time-varying channels. To improve prediction efficiency, it is essential to exploit the inherent low-rank tensor structure of the MIMO channel. Motivated by this observation, we propose a dynamic mode decomposition (DMD)-based prediction framework operating on the low-dimensional core tensors obtained via a Tucker decomposition. The proposed method predicts reduced-order channel cores, significantly lowering computational complexity. Simulation results demonstrate that the proposed approach preserves the dominant channel dynamics and achieves high prediction accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15468 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DMD Prediction of MIMO Channel Using Tucker Decomposition Kopnina, Irina Artemasov, Dmitry Matveev, Sergey Information Theory Signal Processing Accurate channel state information (CSI) prediction is crucial for next-generation multiple-input multiple-output (MIMO) communication systems. Classical prediction methods often become inefficient for high-dimensional and rapidly time-varying channels. To improve prediction efficiency, it is essential to exploit the inherent low-rank tensor structure of the MIMO channel. Motivated by this observation, we propose a dynamic mode decomposition (DMD)-based prediction framework operating on the low-dimensional core tensors obtained via a Tucker decomposition. The proposed method predicts reduced-order channel cores, significantly lowering computational complexity. Simulation results demonstrate that the proposed approach preserves the dominant channel dynamics and achieves high prediction accuracy. |
| title | DMD Prediction of MIMO Channel Using Tucker Decomposition |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2603.15468 |