This article proposes generative site-specific beamforming (GenSSBF) for next-generation spatial intelligence in wireless networks. Site-specific beamforming (SSBF) has emerged as a promising paradigm to mitigate the channel acquisition bottleneck in multiantenna systems by exploiting environmental priors. However, classical SSBF based on discriminative deep learning struggles: 1) to properly represent the inherent multimodality of wireless propagation and 2) to effectively capture the structural features of beamformers. In contrast, by leveraging conditional generative models, GenSSBF addresses these issues via learning a conditional distribution over feasible beamformers. By doing so, the synthesis of diverse and high-fidelity beam candidates from coarse channel sensing measurements can be guaranteed. This article presents the fundamentals, system designs, and implementation methods of GenSSBF. Case studies in both indoor and outdoor scenarios show that GenSSBF attains near-optimal beamforming gain with ultra-low channel acquisition overhead. Finally, several open research problems are highlighted.

翻译：本文提出面向下一代无线网络空间智能的生成式站点专用波束赋形（GenSSBF）。站点专用波束赋形（SSBF）通过利用环境先验知识来缓解多天线系统中的信道获取瓶颈，已成为一种有前景的范式。然而，基于判别式深度学习的经典SSBF存在以下不足：1）难以恰当表征无线传播固有的多模态特性；2）无法有效捕捉波束赋形器的结构特征。相比之下，GenSSBF通过利用条件生成模型，学习可行波束赋形器上的条件分布，从而解决了这些问题。通过这种方式，可以保证从粗略的信道感知测量中合成多样化且高保真的波束候选方案。本文阐述了GenSSBF的基本原理、系统设计及实现方法。室内和室外场景的案例研究表明，GenSSBF能以极低的信道获取开销实现接近最优的波束赋形增益。最后，本文重点指出了若干有待研究的开放性问题。