用扩散模型生成恶劣天气下的5G/6G MIMO信道数据
Generative Models for Modeling and Synthesizing MIMO Channels in Adverse Weather Conditions
- 基于低中强度天气数据训练扩散模型，生成恶劣天气信道
- 合成三类天气、每类三强度的完整MIMO信道数据集
- 适合无线通信系统在极端环境下的仿真与性能评估
未来蜂窝网络要实现广覆盖，依赖于可靠服务，但极端天气频发使这一目标愈发困难。在极端天气下，因缺乏信道测量数据，难以评估覆盖性能。本文通过低、中等天气条件下的信道状态信息（CSI）生成，合成恶劣天气下的真实感MIMO CSI。主要贡献包括：（1）构建包含三类天气、每类三强度的实用5G/6G场景MIMO信道数据集；（2）使用传统导频估计获取的低中强度信道样本，训练条件扩散模型，并用于生成严重天气下的信道实现实例；（3）基于生成信道评估下行链路误码率（BER）和中断概率。结果表明，基于扩散的生成模型可作为恶劣环境中可扩展、数据驱动的信道建模方案，仅需低中强度数据即可泛化至严酷天气。
原文摘要 · Abstract (English)
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.
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