KDGen-BF：一种针对特定站点的多用户波束成形生成方法
KDGen-BF: A Generative Site-Specific Multi-User Beamforming Approach
- The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出KDGen-BF框架，将多用户波束成形建模为条件生成问题，训练扩散变换器并采用多候选策略，在DeepMIMO场景下，该方法在有限探测预算、大探测预算及含噪RSRP观测时均优于多数基线方法。
AI中文摘要：
本文提出了知识蒸馏生成波束成形（KDGen-BF）框架，用于特定站点的多用户波束成形。KDGen-BF 可从低维参考信号接收功率（RSRP）观测值生成多用户波束成形权重，无需获取瞬时信道状态信息（CSI）。为解决有限 RSRP 观测值及干扰耦合导致的模糊性，KDGen-BF 将多用户波束成形建模为条件生成问题，直接输出有限码本之外的波束成形权重。该方法通过知识蒸馏和指数移动平均（KD-EMA）引导训练扩散变换器，并采用多候选策略用于在线部署。在多个 DeepMIMO 场景下的数值结果表明：1）在有限探测预算下，KDGen-BF 优于所有基线方法；2）在更大探测预算下，KDGen-BF 性能与离散傅里叶变换（DFT）码本的穷举搜索相当，且优于所有其他基线方法；3）在含噪 RSRP 观测值下，KDGen-BF 仍保持鲁棒性，优于所有对比基线方法。
英文摘要：
This paper proposes knowledge-distilled generative beamforming (KDGen-BF) framework for site-specific multi-user beamforming. KDGen-BF generates a multi-user beamforming weights from low-dimensional reference signal received power (RSRP) observations without acquiring instantaneous channel state information (CSI). To address the ambiguity caused by limited RSRP observations and interference coupling, KDGen-BF formulates multi-user beamforming as a conditional generation problem and directly outputs beamforming weights beyond a finite codebook. A diffusion transformer is trained through knowledge-distillation and exponential-moving-average (KD-EMA) guidance, and multi-candidate strategy is used for online deployment. Numerical results on multiple DeepMIMO scenarios demonstrate that: 1) under limited probing budgets, KDGen-BF outperforms all baselines; 2) with larger probing budgets, KDGen-BF achieves performance comparable to exhaustive search over the discrete Fourier transform (DFT) codebook and outperforms all other baselines; and 3) under noisy RSRP observations, KDGen-BF remains robust and outperforms all compared baselines.
