RainGaugeNet: CSI-Based Sub-6 GHz Rainfall Attenuation Measurement and Classification for ISAC Applications

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread, and slight Doppler effects were also observed following the onset of precipitation. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model in the sub-6GHz band that leverages multipath and temporal features. Two variants are developed: RainGaugeNet-R using ResNet1D and RainGaugeNet-T using a Transformer encoder. Using only 20 seconds of CSI data, RainGaugeNet-R achieves up to 95% an average classification accuracy in line-of-sight(LoS) scenarios and 85% in non-line-of-sight(NLoS) conditions. RainGaugeNet-T attains 90% accuracy in LoS and 99% in NLoS settings, demonstrating superior robustness. Both models significantly outperform state-of-the-art baselines while maintaining low computational complexity.