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EdgeSAC: Graph Neural Soft Actor-Critic for Hierarchical IoV Resource Management
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DOI：10.1109/tmc.2026.3691088.png)
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Abstract
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Intelligent Transportation Systems (ITS) rely on the Internet of Vehicles (IoV) to sustain high data rates and low latency under dynamic and heterogeneous conditions. Joint power and spectrum control across macro and micro tiers remains challenging due to mobility, interference coupling, and large continuous action spaces. EdgeSAC is a graph-aware Soft Actor Critic (SAC) framework executed at the edge for power control in hierarchical Fifth-Generation New Radio (5G NR) Multiple-Input Multiple-Output (MIMO) networks. A permutation-equivariant Graph Neural Network (GNN) with edge updates encodes co-channel interference among Base Stations (BSs) and outputs node-level power fractions under tier budgets. An on-demand scheduler activates fixed-size channels and assigns at most one macro and one micro resource per user to realize dual connectivity. Signal-to-Interference-plus-Noise Ratio (SINR) is mapped to rate using a Shannon with gap model with rank adaptive MIMO, enabling tier aggregation without action discretization. In simulation with Third Generation Partnership Project (3GPP) TR 38.901 path loss and Manhattan mobility, EdgeSAC increases throughput over SAC and Proximal Policy Optimization (PPO) and reduces power relative to Twin Delayed Deep Deterministic Policy Gradient (TD3), which raises energy efficiency and fairness. The findings indicate that interference-aware graph embeddings combined with entropy regularized continuous control provide a scalable and power-efficient solution for hierarchical IoV resource management.
Keywords:
Internet of Vehicles (IoV)
resource control
reinforcement learning
Soft Actor-Critic (SAC)
Graph Neural Networks (GNNs)
Multiple-Input Multiple-Output (MIMO)
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