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arXiv · 2608.29838

KDGen-BF: A Generative Site-Specific Multi-User Beamforming Approach

Ruihang Jiang·Zhaolin Wang·Yuanwei Liu
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Abstract

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.

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Ruihang Jiang, Zhaolin Wang, Yuanwei Liu. 2026-08-30. KDGen-BF: A Generative Site-Specific Multi-User Beamforming Approach. https://arxiv.org/abs/2608.29838

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