Dynamic 6G propagation modeling with reconfigurable intelligent surfaces and machine learning for urban rural highway and hilly mobility scenarios

Abstract Conventional propagation models struggle to accurately characterize the extreme mobility and complex environmental challenges anticipated in 6G networks. These include severe Doppler shifts on highways (120–500 km/h), profound urban non-line-of-sight (NLoS) blockages exceeding 140 dB at 200 m, rural energy constraints leading to 30–50% reductions in transmit power, and irregular hilly terrains with path loss exponents as high as 3.8. To address this gap, we propose an integrated propagation framework that combines classical physical modeling, Reconfigurable Intelligent Surface (RIS) enhancement, and AI-driven optimization. Our implementation utilizes the Close-In (CI) path loss model with environment-specific exponents and Rician fading with scenario-dependent K-factors. To mitigate high attenuation and fading, the framework incorporates 64-element RIS structures, which provide an SNR improvement of up to + 18 dB, and employs reinforcement learning for dynamic beamforming, adaptive phase management, and anticipatory resource allocation. Validation across four mobility scenarios demonstrates significant performance gains: without RIS, highway SNR remains above 10 dB up to 500 m, but urban SNR falls below 0 dB at ~ 100 m and hilly conditions degrade to − 5 dB at 300 m. With RIS, urban and hilly environments maintain an SNR > 10 dB up to 250 m, while highway and rural scenarios achieve over 15 dB SNR at 700 m. Throughput is enhanced from below 100 Mbps at distances beyond 100 m in baseline urban/hilly conditions to over 500 Mbps at 300 m. Furthermore, AI-powered beam alignment reduces handover failures by 40% under high-speed highway mobility.