Lightweight Diffusion for 6G C-V2X Radio Environment Maps
Published:Dec 27, 2025 09:38
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of dynamic Radio Environment Map (REM) generation for 6G Cellular Vehicle-to-Everything (C-V2X) communication. The core problem is the impact of physical layer (PHY) issues on transmitter vehicles due to the lack of high-fidelity REMs that can adapt to changing locations. The proposed Coordinate-Conditioned Denoising Diffusion Probabilistic Model (CCDDPM) offers a lightweight, generative approach to predict REMs based on limited historical data and transmitter vehicle coordinates. This is significant because it enables rapid and scenario-consistent REM generation, potentially improving the efficiency and reliability of 6G C-V2X communications by mitigating PHY issues.
Key Takeaways
- •Proposes a lightweight diffusion-based model (CCDDPM) for generating Radio Environment Maps (REMs) in 6G C-V2X.
- •Uses transmitter vehicle coordinates to condition the REM generation.
- •Aims to improve the efficiency and reliability of 6G C-V2X communications by mitigating PHY issues.
- •Demonstrates improved stability and performance compared to other generative AI approaches.
Reference
“The CCDDPM leverages the signal intensity-based 6G V2X Radio Environment Map (REM) from limited historical transmitter vehicles in a specific region, to predict the REMs for a transmitter vehicle with arbitrary coordinates across the same region.”