Graph Neural Networks for Joint Communication and Sensing Optimization in Vehicular Networks

TL;DR

This paper introduces a GNN-based algorithm for optimizing joint communication and sensing in vehicular networks, effectively managing service modes and vehicle associations to maximize data rates amid dynamic topologies.

Contribution

It presents a novel GNN-based approach with heterogeneous graph modeling for joint communication and sensing optimization in THz vehicular networks.

Findings

Achieves 93.66% of the optimal sum rate.

Outperforms homogeneous GNN and conventional algorithms.

Enhances sum rate by up to 31.86%.

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.