多模态感知残差校正图神经网络用于毫米波路径损耗预测的机器合成感知
Multi-Modal Sensing Residual-Corrected GNN for mmWave Path Loss Prediction via Synesthesia of Machines
AI总结：
本文提出一种多模态感知残差校正图神经网络，用于毫米波路径损耗预测，结合拓扑感知图表示和细粒度视觉语义，实现高精度路径损耗估计。
摘要：
为支持第六代（6G）智能交通系统（ITSs），本文首次提出一种多模态感知残差校正图神经网络（MM-ResGNN）框架，用于车载通信中的毫米波（mmWave）路径损耗预测。传播环境被建模为一个环境感知路径损耗图（ESPL-Graph），其中节点代表发射端（Tx）和接收端（Rx）实体，边共同描述Tx-Rx传输链路和Rx-Rx空间相关链路。同时，引入了一种几何驱动的物理基线，以将确定性衰减趋势与随机残差变化解耦。构建了一个车载多模态路径损耗数据集（VMMPL），涵盖了三个代表性场景，包括城市宽车道、城市十字路口和郊区分叉道路环境，并实现了RGB图像与物理空间中的全局语义信息的精确对齐，以及电磁空间中的链路级射线追踪（RT）基于的路径损耗数据。在MM-ResGNN中，通过门控融合机制将拓扑感知的图表示和细粒度的视觉语义协同整合，以估计相对于物理基线的路径损耗残差。实验结果表明，MM-ResGNN在经验模型和传统数据驱动基线上取得了显著改进，其归一化均方误差（NMSE）为0.0098，均绝对误差（MAE）为5.7991~dB，均绝对百分比误差（MAPE）为5.0498%。此外，MM-ResGNN通过少量样本微调策略表现出强大的跨场景泛化能力，能够在有限标记数据的情况下实现未见车载环境中的准确路径损耗预测。
英文摘要：
To support sixth-generation (6G)-enabled intelligent transportation systems (ITSs), a multi-modal sensing residual-corrected graph neural network (MM-ResGNN) framework is proposed for millimeter-wave (mmWave) path loss prediction in vehicular communications for the first time. The propagation environment is formulated as an environment sensing path loss graph (ESPL-Graph), where nodes represent the transmitter (Tx) and receiver (Rx) entities and edges jointly describe Tx--Rx transmission links and Rx--Rx spatial correlation links. Meanwhile, a geometry-driven physical baseline is introduced to decouple deterministic attenuation trends from stochastic residual variations. A vehicular multi-modal path loss dataset (VMMPL) is constructed, which covers three representative scenarios, including the urban wide lane, urban crossroad, and suburban forking road environments, and achieves precise alignment between RGB images and global semantic information in the physical space, and link-level ray-tracing (RT)-based path loss data in the electromagnetic space. In MM-ResGNN, topology-aware graph representations and fine-grained visual semantics are synergistically integrated through a gated fusion mechanism to estimate the path loss residual relative to the physical baseline. Experimental results demonstrate that MM-ResGNN achieves significant improvements over empirical models and conventional data-driven baselines, with a normalized mean squared error (NMSE) of 0.0098, a mean absolute error (MAE) of 5.7991~dB, and a mean absolute percentage error (MAPE) of 5.0498\%. Furthermore, MM-ResGNN exhibits robust cross-scenario generalization through a few-shot fine-tuning strategy, enabling accurate path loss prediction in unseen vehicular environments with limited labeled data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
