In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based method can improve the number of successfully served vehicles by up to 17% and 28% compared to a GNN based algorithm with a fixed neural network model and a conventional optimization algorithm without using GNNs.

翻译：本文研究了太赫兹（THz）使能的车载网络联合感知与通信中的车辆服务模式选择（感知、通信或两者兼具）及车辆连接问题。所考虑的网络包含若干服务提供车辆（SPV），其可提供：1）仅感知服务，2）仅通信服务，以及3）两种服务，同时包含感知服务请求车辆和通信服务请求车辆。基于车辆网络拓扑结构及其服务可达性，SPV策略性地选择服务请求车辆以提供感知、通信或两者兼具的服务。该问题被建模为一个优化问题，旨在通过联合确定每个SPV的服务模式及其关联车辆，最大化成功服务的车辆数量。为解决此问题，我们提出一种动态图神经网络（GNN）模型，该模型根据车辆网络拓扑结构选择适当的图信息聚合函数，相较于使用固定聚合函数的传统静态GNN（针对不同车辆网络拓扑），能够提取更多车辆网络信息。利用提取的车辆网络信息，将确定每个SPV的服务模式及其所服务的服务请求车辆。仿真结果表明，与采用固定神经网络模型的基于GNN的算法以及未使用GNN的传统优化算法相比，所提出的基于动态GNN的方法可将成功服务的车辆数量分别提高高达17%和28%。