Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913022127112192 |
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| author | Othman, Esraa Fahmy Bariah, Lina Debbah, Merouane |
| author_facet | Othman, Esraa Fahmy Bariah, Lina Debbah, Merouane |
| contents | Beam alignment is a key challenge in directional mmWave and THz systems, where narrow beams require accurate yet low-overhead training. Existing learning-based approaches typically predict a single beam and do not quantify uncertainty, limiting adaptive beam sweeping. We recast beam alignment as a generative task and propose a conditional diffusion model that learns a probabilistic beam prior from compact geometric and multipath features. The learned priors guide top-$k$ sweeps and capture the SNR loss induced by limited probing. Using a ray-traced DeepMIMO scenario with an 8-beam DFT codebook, our best conditional diffusion model achieves strong ranking performance (Hit@1 $\approx 0.61$, Hit@3 $\approx 0.90$, Hit@5 $\approx 0.97$) while preserving SNR at small sweep budgets. Compared with a deterministic classifier baseline, diffusion improves Hit@1 by about 180\%. Results further highlight the importance of informative conditioning and the ability of diffusion sampling to flexibly trade accuracy for computational efficiency. The proposed diffusion framework achieves substantial improvements in small-$k$ Hit rates, translating into reduced beam training overhead and enabling low-latency, energy-efficient beam alignment for mmWave and THz systems while preserving received SNR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09653 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks Othman, Esraa Fahmy Bariah, Lina Debbah, Merouane Signal Processing Artificial Intelligence Beam alignment is a key challenge in directional mmWave and THz systems, where narrow beams require accurate yet low-overhead training. Existing learning-based approaches typically predict a single beam and do not quantify uncertainty, limiting adaptive beam sweeping. We recast beam alignment as a generative task and propose a conditional diffusion model that learns a probabilistic beam prior from compact geometric and multipath features. The learned priors guide top-$k$ sweeps and capture the SNR loss induced by limited probing. Using a ray-traced DeepMIMO scenario with an 8-beam DFT codebook, our best conditional diffusion model achieves strong ranking performance (Hit@1 $\approx 0.61$, Hit@3 $\approx 0.90$, Hit@5 $\approx 0.97$) while preserving SNR at small sweep budgets. Compared with a deterministic classifier baseline, diffusion improves Hit@1 by about 180\%. Results further highlight the importance of informative conditioning and the ability of diffusion sampling to flexibly trade accuracy for computational efficiency. The proposed diffusion framework achieves substantial improvements in small-$k$ Hit rates, translating into reduced beam training overhead and enabling low-latency, energy-efficient beam alignment for mmWave and THz systems while preserving received SNR. |
| title | Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2604.09653 |