Abstract
The deep integration of communication with intelligence and sensing, as a defining vision of 6G, renders environment-aware channel prediction a key enabling technology. As a representative 6G application, vehicular communications require accurate and forward-looking channel prediction under stringent reliability, latency, and adaptability demands. Traditional empirical and deterministic models remain limited in balancing accuracy, generalization, and deployability, while the growing availability of onboard and roadside sensing devices offers a promising source of environmental priors. This paper proposes an environment-aware channel prediction framework based on multimodal visual feature fusion. Using GPS data and vehicle-side panoramic RGB images, together with semantic segmentation and depth estimation, the framework extracts semantic, depth, and position features through a three-branch architecture and performs adaptive multimodal fusion via a squeeze-excitation attention gating module. For 360-dimensional angular power spectrum (APS) prediction, a dedicated regression head and a composite multi-constraint loss are further designed. As a result, joint prediction of path loss (PL), delay spread (DS), azimuth spread of arrival (ASA), azimuth spread of departure (ASD), and APS is achieved. Experiments on a synchronized urban V2I measurement dataset yield the best root mean square error (RMSE) of 3.26 dB for PL, RMSEs of 37.66 ns, 5.05 degrees, and 5.08 degrees for DS, ASA, and ASD, respectively, and mean/median APS cosine similarities of 0.9342/0.9571, demonstrating strong accuracy, generalization, and practical potential for intelligent channel prediction in 6G vehicular communications.
Abstract (translated)
通信与智能感知的深度融合作为6G的标志性愿景，使得环境感知信道预测成为一项关键使能技术。作为6G的典型应用，车联网通信在严苛的可靠性、低时延和自适应需求下，要求实现精准且具有前瞻性的信道预测。传统经验模型与确定性模型在精度、泛化能力与部署可行性之间难以兼顾，而车载与路侧感知设备的日益普及则为环境先验信息提供了极具潜力的数据来源。本文提出一种基于多模态视觉特征融合的环境感知信道预测框架。该框架利用GPS数据与车辆全景RGB图像，结合语义分割与深度估计，通过三分支架构提取语义、深度与位置特征，并借助挤压激励注意力门控模块实现自适应多模态融合。针对360维角度功率谱（APS）预测，进一步设计了专用回归头与复合多约束损失函数，从而实现了路径损耗（PL）、时延扩展（DS）、到达方位扩展（ASA）、离开方位扩展（ASD）及APS的联合预测。在同步采集的城市车对基础设施（V2I）测量数据集上的实验表明，PL预测取得了3.26 dB的最佳均方根误差（RMSE），DS、ASA、ASD的RMSE分别达到37.66纳秒、5.05度与5.08度，APS预测的平均/中值余弦相似度达0.9342/0.9571，验证了该方法在6G车联网通信智能信道预测中具备高精度、强泛化能力及实际应用潜力。
URL
https://arxiv.org/abs/2604.02396
