AI 中文总结
针对超密集室内下一代网络的干扰问题，提出用生成式人工智能增强数字孪生框架，借助条件生成对抗网络等实现主动罕见事件信道合成，经模拟验证该方法能提升信干噪比、减少丢包并缩小与完美信道状态信息的差距。
AI 中文摘要
超密集室内下一代网络受到移动性引起的阻塞和局部多用户热点的严重干扰，传统数字孪生无法预测。我们提出了一种生成式人工智能增强的数字孪生框架，采用条件生成对抗网络，带有时空生成器和PatchGAN鉴别器，用于主动罕见事件信道合成。由蒙特卡罗合成轨迹驱动的最坏情况迫零波束形成器实现分布鲁棒预编码，控制信道开销限制在每10毫秒时隙约2.1 kB。基于Sionna的模拟证实，在2.8 - 4.1毫秒推理开销内，中位数信干噪比增益为5 - 8 dB，丢包减少60 - 70%，完美信道状态信息预言差距缩小60 - 85%。
英文摘要
Ultra-dense indoor next-generation networks suffer severe interference from mobility-induced blockages and localized multi-user hotspots that conventional digital twins~(DTs) cannot anticipate. We propose a generative AI~(GenAI)-enhanced DT framework employing a conditional generative adversarial network~(cGAN) with a spatio-temporal generator and PatchGAN discriminator for proactive rare-event channel synthesis. A worst-case zero-forcing~(WC-ZF) beamformer driven by Monte Carlo synthetic trajectories realizes distributionally robust precoding, with control-channel overhead bounded to $\approx$2.1\,kB per 10\,ms slot. Sionna-based simulations confirm a 5--8\,dB median signal-to-interference-plus-noise-ratio (SINR) gain, 60--70\% packet-loss reduction, and 60--85\% closure of the perfect channel state information (CSI) oracle gap within a 2.8--4.1\,ms inference overhead.
Comments6 pages, 4 figures
