📝 SummarXiv

Abstract

The integration of Reconfigurable Intelligent Surfaces (RIS) and Integrated Sensing and Communication (ISAC) in vehicular networks enables dynamic spatial resource management and real-time adaptation to environmental changes. However, the coexistence of distinct vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) connectivity requirements, together with highly dynamic and heterogeneous network topologies, presents significant challenges for unified reliability modeling and resource optimization. To address these issues, we propose VariSAC, a graph neural network (GNN)-augmented deep reinforcement learning framework for assured, time-continuous connectivity in RIS-assisted, ISAC-enabled vehicle-to-everything (V2X) systems. Specifically, we introduce the Continuous Connectivity Ratio (CCR), a unified metric that characterizes the sustained temporal reliability of V2I connections and the probabilistic delivery guarantees of V2V links, thus unifying their continuous reliability semantics. Next, we employ a GNN with residual adapters to encode complex, high-dimensional system states, capturing spatial dependencies among vehicles, base stations (BS), and RIS nodes. These representations are then processed by a Soft Actor-Critic (SAC) agent, which jointly optimizes channel allocation, power control, and RIS configurations to maximize CCR-driven long-term rewards. Extensive experiments on real-world urban datasets demonstrate that VariSAC consistently outperforms existing baselines in terms of continuous V2I ISAC connectivity and V2V delivery reliability, enabling persistent connectivity in highly dynamic vehicular environments.