Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909038312161280 |
|---|---|
| author | Artemasov, Dmitry Shmatok, Alexander Andreev, Kirill Frolov, Alexey Hanawal, Manjesh K. Zlatanov, Nikola |
| author_facet | Artemasov, Dmitry Shmatok, Alexander Andreev, Kirill Frolov, Alexey Hanawal, Manjesh K. Zlatanov, Nikola |
| contents | The integration of terahertz communications and ultra-massive multiple-input multiple-output (UM-MIMO) systems in 6G networks is motivated by their ability to enable unprecedented data rates, mitigate spectrum congestion, and enhance overall network performance. However, the enlarged antenna apertures and higher carrier frequencies in these systems increase the Rayleigh distance, causing users to span both the near-field and conventional far-field regions. Accurate spatial precoding thus requires exact channel estimation at the base station - a task made more challenging by the hybrid coexistence of near- and far-field effects and the limited number of digital chains available in hybrid beamforming architectures. In this paper, we propose a block recurrent transformer model to address this challenge. We demonstrate that a single transformer block equipped with state memory can be trained once and then iteratively applied for hybrid-field channel estimation. Furthermore, we train the model such that it generalizes to wireless channels with varying scatterer distances, different numbers of propagation paths, and wideband operation. Simulation results show that the proposed method achieves performance gains of approximately 5 dB and 7.5 dB in normalized mean squared error (NMSE) over state-of-the-art solutions in narrowband and wideband scenarios, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12578 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO Artemasov, Dmitry Shmatok, Alexander Andreev, Kirill Frolov, Alexey Hanawal, Manjesh K. Zlatanov, Nikola Signal Processing Information Theory Machine Learning 94Axx, 68Txx I.2.6; C.2.1 The integration of terahertz communications and ultra-massive multiple-input multiple-output (UM-MIMO) systems in 6G networks is motivated by their ability to enable unprecedented data rates, mitigate spectrum congestion, and enhance overall network performance. However, the enlarged antenna apertures and higher carrier frequencies in these systems increase the Rayleigh distance, causing users to span both the near-field and conventional far-field regions. Accurate spatial precoding thus requires exact channel estimation at the base station - a task made more challenging by the hybrid coexistence of near- and far-field effects and the limited number of digital chains available in hybrid beamforming architectures. In this paper, we propose a block recurrent transformer model to address this challenge. We demonstrate that a single transformer block equipped with state memory can be trained once and then iteratively applied for hybrid-field channel estimation. Furthermore, we train the model such that it generalizes to wireless channels with varying scatterer distances, different numbers of propagation paths, and wideband operation. Simulation results show that the proposed method achieves performance gains of approximately 5 dB and 7.5 dB in normalized mean squared error (NMSE) over state-of-the-art solutions in narrowband and wideband scenarios, respectively. |
| title | Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO |
| topic | Signal Processing Information Theory Machine Learning 94Axx, 68Txx I.2.6; C.2.1 |
| url | https://arxiv.org/abs/2605.12578 |