VariSAC: V2X Assured Connectivity in RIS-Aided ISAC via GNN-Augmented Reinforcement Learning

2025-09-08

AI Summary

To address the challenges of modeling continuous reliability and insufficient dynamic resource coordination in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) vehicular networks under hybrid V2I/V2V connectivity, this paper proposes a graph neural network (GNN)-enhanced Soft Actor-Critic reinforcement learning framework. We innovatively define the Continuous Connectivity Ratio (CCR) to jointly quantify V2I continuous reliability and V2V probabilistic message delivery. A residual adapter-equipped GNN is designed to encode dynamic spatial dependencies among vehicles and RISs. The framework jointly optimizes RIS beamforming, communication-sensing resource allocation, and power control. Evaluated on real-world urban vehicle trajectory datasets, the proposed method significantly improves V2I continuous connection rate and V2V message delivery reliability, achieving robust V2X connectivity performance in highly dynamic and heterogeneous environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Learning & Optimization for CV

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• Ensuring reliable connectivity in dynamic RIS-assisted vehicular networks
• Unifying V2I and V2V reliability modeling with heterogeneous requirements
• Optimizing resource allocation for continuous connectivity in ISAC systems

Innovation

• GNN-augmented reinforcement learning for V2X connectivity
• Unified CCR metric for continuous reliability semantics
• Joint optimization of channel, power, and RIS configurations