用于恶劣天气条件下MIMO信道建模与合成的生成模型
Generative Models for Modeling and Synthesizing MIMO Channels in Adverse Weather Conditions
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中文总结 AI 辅助
该研究针对极端天气下MIMO信道测量受限问题，构建含多天气类型与强度的5G/6G场景数据集，训练天气条件扩散模型生成恶劣天气信道，评估后验证其可泛化用于恶劣环境信道建模。
中文摘要 AI 辅助
未来蜂窝网络扩大覆盖范围的需求依赖于可靠服务，但极端天气事件增多使这一目标愈发难以实现。在极端天气条件下，由于信道测量数据获取受限，我们难以评估覆盖范围。本文在低强度和中等强度天气条件下生成信道状态信息（CSI），以合成恶劣天气条件下的真实MIMO CSI。主要贡献包括：（1）合成包含三种天气类型、每种类型三个强度等级的MIMO信道数据集，该数据集可代表实际5G/6G场景；（2）基于低强度和中等强度天气下通过传统导频估计获得的信道样本，训练以天气为条件的扩散模型，随后利用该模型生成恶劣天气条件下的信道实现；（3）使用生成的信道评估下行链路误码率（BER）和中断概率指标。结果表明，基于扩散的生成模型为恶劣环境下的信道建模提供了一种可扩展、数据驱动的替代方案，且仅使用低强度和中等强度训练数据即可泛化到恶劣天气条件。
英文摘要
The push for broader coverage in future cellular networks depends on reliable service, yet this is increasingly harder to do as we encounter more instances of extreme weather conditions. In extreme weather conditions, we have difficulty evaluating coverage due to limited access to channel measurements. In this paper, we generate channel state information (CSI) in low and moderate weather conditions to synthesize realistic MIMO CSI under adverse weather conditions. Our primary contributions are to (1) synthesize MIMO channel datasets incorporating three weather types, each with three intensity levels, representative of practical 5G/6G scenarios; (2) train a diffusion model conditioned on weather using channel samples obtained through conventional pilot-based estimation under low and moderate weather intensities, and subsequently use it to generate channel realizations for severe weather conditions; and (3) evaluate the downlink Bit Error Rate (BER) and Outage Probability measures using the generated channels. The results show that diffusion-based generative models provide a scalable, data-driven alternative for channel modeling in harsh environments and can generalize to severe weather conditions using only low- and moderate-intensity training data.
发表机构
- The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。
