用于空间一致多用户TR 38.901信道生成的物理感知条件集生成对抗网络

中文摘要 AI 辅助

基于TR 38.901的信道模型如Sionna虽可靠，但生成多用户信道实现成本高。本文提出问题：训练好的生成模型能否比Sionna更快生成多用户TR 38.901信道且不丢失用户几何结构带来的空间相关性？为此提出物理感知、几何条件的SetGAN并在Sionna参考数据上训练。该方法分离大尺度接收功率与归一化小尺度衰落，用主成分分析压缩后者，在潜在空间学习条件信道分布并保留几何相关相关性。在UMa/NLoS基准测试中，模型保持接收功率分布与参考接近，再现空间一致性曲线。此外，相比Sionna，生成时间缩短3.45倍，CPU总成本降低6.15倍。结果表明训练好的生成模型可大幅加速TR 38.901信道生成且不破坏评估多用户系统所需的空间一致性。

英文摘要

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.

发表机构

• Institute of Telecommunications and Multimedia Applications (iTEAM), Universitat Polit\`ecnica de Val\`encia (UPV), Spain（电信与多媒体应用研究所）
• Beihang Valencia Polytechnic Institute (BVPI), China（北京航空航天大学瓦大理工大学）
• Huawei Technologies, France（华为技术）

机构由 AI 辅助整理，请以论文原文为准。

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Submitted to IEEE GLOBECOM 2026