Abstract
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.