In this paper, the problem of joint communication and sensing is studied in the context of terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles (SPVs) provide either communication service or sensing service to target vehicles, where it is essential to determine 1) the service mode (i.e., providing either communication or sensing service) for each SPV and 2) the subset of target vehicles that each SPV will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of the communication target vehicles, while satisfying the sensing service requirements of the sensing target vehicles, by determining the service mode and the target vehicle association for each SPV. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using this extracted graph information, a joint service mode selection and target vehicle association strategy is then determined to adapt to the dynamic vehicle topology with various vehicle types (e.g., target vehicles and service provider vehicles). Simulation results show that the proposed GNN-based scheme can achieve 93.66% of the sum rate achieved by the optimal solution, and yield up to 3.16% and 31.86% improvements in sum rate, respectively, over a homogeneous GNN-based algorithm and a conventional optimization algorithm without using GNNs.

翻译：本文研究了太赫兹（THz）车载网络中联合通信与感知的优化问题。在所建立模型中，一组服务提供商车辆（SPVs）为目标车辆提供通信服务或感知服务，其核心决策包括：1）为每辆SPV确定服务模式（即提供通信服务还是感知服务）；2）确定每辆SPV所服务的子集目标车辆。该问题被建模为优化问题，通过确定每辆SPV的服务模式与目标车辆关联关系，在满足感知目标车辆的服务需求前提下，最大化通信目标车辆的数据速率总和。为求解该问题，本文提出了一种基于异构图表征的图神经网络（GNN）算法。该算法使中央控制器能够提取每辆车的位置、连接关系及通信干扰等图结构信息。基于提取的图信息，算法进一步确定联合服务模式选择与目标车辆关联策略，以适应包含多种车辆类型（例如目标车辆与服务提供商车辆）的动态车辆拓扑。仿真结果表明，本文提出的GNN方案可达到最优解总速率的93.66%，相较于同构GNN算法和未使用GNN的传统优化算法，分别实现了最高3.16%和31.86%的总速率提升。