Bayesian Learning-Aided Near-Field Channel Estimation for mmWave Hybrid MIMO systems employing Uniform Circular Array
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_ | 1866911657018523648 |
|---|---|
| author | Garg, Abhisha Gupta, Priya Srivastava, Suraj Jagannatham, Aditya |
| author_facet | Garg, Abhisha Gupta, Priya Srivastava, Suraj Jagannatham, Aditya |
| contents | This work conceives a Ring-Bayes channel learning framework that unifies Bayesian learning with near-field channel estimation in millimeter-wave (mmWave) hybrid MIMO systems. As the number of antennas scales up, users increasingly fall within the near-field region, rendering the conventional planar-wave assumption invalid. Moreover, the widely studied uniform linear arrays (ULAs) at the base station are impractical for large-scale deployment, whereas uniform circular arrays (UCAs) achieve superior beamforming gain and spatial directivity with the same antenna aperture. To exploit these advantages, we design a near-field concentric-ring codebook that captures channel features jointly in angular and distance domains. Leveraging this structure, the proposed Ring-Bayes framework enables highly accurate recovery of UCA near-field channels. Extensive simulations confirm that our approach delivers substantial improvements over existing methods, establishing Ring-Bayes as a powerful and scalable solution for next-generation mmWave communications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06039 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bayesian Learning-Aided Near-Field Channel Estimation for mmWave Hybrid MIMO systems employing Uniform Circular Array Garg, Abhisha Gupta, Priya Srivastava, Suraj Jagannatham, Aditya Signal Processing This work conceives a Ring-Bayes channel learning framework that unifies Bayesian learning with near-field channel estimation in millimeter-wave (mmWave) hybrid MIMO systems. As the number of antennas scales up, users increasingly fall within the near-field region, rendering the conventional planar-wave assumption invalid. Moreover, the widely studied uniform linear arrays (ULAs) at the base station are impractical for large-scale deployment, whereas uniform circular arrays (UCAs) achieve superior beamforming gain and spatial directivity with the same antenna aperture. To exploit these advantages, we design a near-field concentric-ring codebook that captures channel features jointly in angular and distance domains. Leveraging this structure, the proposed Ring-Bayes framework enables highly accurate recovery of UCA near-field channels. Extensive simulations confirm that our approach delivers substantial improvements over existing methods, establishing Ring-Bayes as a powerful and scalable solution for next-generation mmWave communications. |
| title | Bayesian Learning-Aided Near-Field Channel Estimation for mmWave Hybrid MIMO systems employing Uniform Circular Array |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.06039 |