Transmitter vehicles that broadcast 6G Cellular Vehicle-to-Everything (C-V2X)-based messages, e.g., Basic Safety Messages (BSMs), are prone to be impacted by PHY issues due to the lack of dynamic high-fidelity Radio Environment Map (REM) with dynamic location variation. This paper explores a lightweight diffusion-based generative approach, the Coordinate-Conditioned Denoising Diffusion Probabilistic Model (CCDDPM), that 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. The transmitter vehicle coordinate is encoded as a smooth Gaussian prior and fused with the Gaussian noise through a lightweight two-channel conditional U-Net architecture. We demonstrate that the predicted REM closely matches the statistics and structure of ground-truth REM while exhibiting the improved stability and over other widely applied generative AI approaches. The resulting predictor enables rapid and scenario-consistent REM with arbitrary transmitter coordinates, which thereby supports more efficient 6G C-V2X communications where transmitter vehicles are less likely to suffer from the PHY issues.

翻译：由于缺乏随动态位置变化的高保真动态无线电环境地图，广播基于6G蜂窝车联网（C-V2X）消息（例如基本安全消息）的发射车辆容易受到物理层问题的影响。本文探索了一种轻量级的基于扩散的生成方法——坐标条件去噪扩散概率模型（CCDDPM）。该方法利用特定区域内有限历史发射车辆基于信号强度的6G V2X无线电环境地图，来预测同一区域内任意坐标的发射车辆的无线电环境地图。发射车辆坐标被编码为平滑的高斯先验，并通过一个轻量级双通道条件U-Net架构与高斯噪声融合。我们证明，预测的无线电环境地图在统计和结构上与真实地图高度吻合，同时相较于其他广泛应用的生成式人工智能方法，表现出更高的稳定性。所得到的预测器能够快速生成与场景一致且适用于任意发射坐标的无线电环境地图，从而支持更高效的6G C-V2X通信，使发射车辆更少受到物理层问题的影响。