Conformal Robust Beamforming via Generative Channel Models
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910968432295936 |
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| author | Su, Xin Hou, Qiushuo He, Ruisi Simeone, Osvaldo |
| author_facet | Su, Xin Hou, Qiushuo He, Ruisi Simeone, Osvaldo |
| contents | Traditional approaches to outage-constrained beamforming optimization rely on statistical assumptions about channel distributions and estimation errors. However, the resulting outage probability guarantees are only valid when these assumptions accurately reflect reality. This paper tackles the fundamental challenge of providing outage probability guarantees that remain robust regardless of specific channel or estimation error models. To achieve this, we propose a two-stage framework: (i) construction of a channel uncertainty set using a generative channel model combined with conformal prediction, and (ii) robust beamforming via the solution of a min-max optimization problem. The proposed method separates the modeling and optimization tasks, enabling principled uncertainty quantification and robust decision-making. Simulation results confirm the effectiveness and reliability of the framework in achieving model-agnostic outage guarantees. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06934 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conformal Robust Beamforming via Generative Channel Models Su, Xin Hou, Qiushuo He, Ruisi Simeone, Osvaldo Signal Processing Traditional approaches to outage-constrained beamforming optimization rely on statistical assumptions about channel distributions and estimation errors. However, the resulting outage probability guarantees are only valid when these assumptions accurately reflect reality. This paper tackles the fundamental challenge of providing outage probability guarantees that remain robust regardless of specific channel or estimation error models. To achieve this, we propose a two-stage framework: (i) construction of a channel uncertainty set using a generative channel model combined with conformal prediction, and (ii) robust beamforming via the solution of a min-max optimization problem. The proposed method separates the modeling and optimization tasks, enabling principled uncertainty quantification and robust decision-making. Simulation results confirm the effectiveness and reliability of the framework in achieving model-agnostic outage guarantees. |
| title | Conformal Robust Beamforming via Generative Channel Models |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.06934 |