Generative Models for Modeling and Synthesizing MIMO Channels in Adverse Weather Conditions

2026-07-31

AI Summary

This work addresses the scarcity of MIMO channel measurement data under extreme weather conditions, which hinders reliable coverage assessment for 5G/6G networks. To overcome this limitation, the authors propose a conditional diffusion model that, for the first time, incorporates both weather type and intensity as conditioning inputs. Leveraging only pilot-based channel state information (CSI) estimates collected under mild weather, the model generates realistic MIMO channels across three distinct weather types and multiple intensity levels. The synthesized channels demonstrate strong performance in key metrics such as downlink bit error rate and outage probability, confirming the model’s generalization capability and scalability in harsh environments. This approach offers an effective solution for channel modeling in scenarios where empirical measurements under adverse weather are unavailable.

Abstract

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.

Problem

• MIMO channels
• adverse weather conditions
• channel state information
• generative models
• 5G/6G networks

Innovation

• diffusion model
• MIMO channel synthesis
• adverse weather conditions
• channel state information
• generative modeling