用生成模型加速5G信道仿真，保持用户位置相关性。
Physics-Aware Conditional SetGAN for Spatially Consistent Multi-User TR 38.901 Channel Generation
- 分离大尺度功率与小尺度衰落，结合主成分分析压缩特征
- 生成信道与参考模型的接收功率误差仅0.41 dB，空间一致性偏差低于0.03
- 生成速度提升3.45倍，计算成本降低6.15倍，适合多用户系统评估
基于TR 38.901的信道模型（如Sionna）虽可靠，但生成大量多用户信道实例仍耗时。本文提出一种物理感知、几何条件约束的SetGAN，基于Sionna参考数据训练。方法将大尺度接收功率与归一化小尺度衰落分离，对后者采用主成分分析压缩，并在潜在空间中学习条件化信道分布，同时保留由用户几何结构决定的相关性。在UMa/NLoS基准测试中，生成信道的接收功率分布与参考值接近，Wasserstein距离约为0.41 dB；空间一致性曲线在中位数随距离变化上的均方偏差低于0.03。此外，在固定用户位置的CPU-对-CPU基准下，生成耗时减少3.45倍，总CPU成本降低6.15倍。结果表明，训练后的生成模型可在不破坏多用户系统评估所需空间一致性的前提下，显著加速TR 38.901信道生成。
原文摘要 · Abstract (English)
TR 38.901-based channel models such as Sionna are reliable, but generating many multi-user channel realizations remains expensive. This paper asks a practical question: can a trained generative model produce multi-user TR 38.901 channels faster than Sionna without losing the spatial correlations imposed by user geometry? To answer this question, we propose a physics-aware, geometry-conditioned SetGAN trained on Sionna reference data. The method separates large-scale received power from normalized small-scale fading, compresses the latter with principal component analysis, and learns the conditional channel distribution in a latent space while preserving geometry-dependent correlations. On the UMa/NLoS benchmark, the model keeps the received-power distributions close to the reference, with about 0.41 dB Wasserstein distance, and reproduces spatial-consistency profiles with mean deviations below 0.03 on median curves versus distance. In addition, it reduces elapsed generation time by a factor of 3.45 and CPU-total cost by a factor of 6.15 relative to Sionna under matched user positions in the fixed-position CPU-vs-CPU benchmark. These results show that a trained generative model can substantially accelerate TR 38.901 channel generation without breaking the spatial consistency needed to evaluate multi-user systems.
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