用扩散模型生成波束概率先验，提升毫米波通信的对准效率。
Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks
- 将波束对准建模为生成任务，利用扩散模型学习概率先验
- 小预算下命中率显著提升（Hit@1达61%，较基线高180%）
- 适合追求低延迟、低功耗的毫米波/太赫兹系统部署
波束对准是定向毫米波和太赫兹系统中的关键挑战，窄波束需要高精度且低开销的训练。现有基于学习的方法通常仅预测单一波束，无法量化不确定性，限制了自适应波束扫描。本文将波束对准重定义为生成任务，提出一种条件扩散模型，从紧凑的几何与多径特征中学习概率波束先验。该先验指导top-k波束扫描，并捕捉因探测受限导致的信噪比损失。在使用8波束DFT码本的射线追踪DeepMIMO场景下，最优条件扩散模型实现优异排名性能（Hit@1 ≈ 0.61，Hit@3 ≈ 0.90，Hit@5 ≈ 0.97），同时在小扫描开销下保持较高信噪比。相比确定性分类器基线，扩散模型使Hit@1提升约180%。结果进一步表明信息性条件输入的重要性，以及扩散采样在精度与计算效率间灵活权衡的能力。所提框架在小k值下显著提升命中率，降低波束训练开销，实现低延迟、节能的毫米波与太赫兹波束对准，同时保持接收信噪比。
原文摘要 · Abstract (English)
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.
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