Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks
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 $\approx 0.61$,...
| Authors | Esraa Fahmy Othman, Lina Bariah, Merouane Debbah |
|---|---|
| Primary category | eess.SP |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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