Decentralized GNN-based Power Allocation with Varying Network Density

The growing complexity and heterogeneity of industrial internet of things (IIoT) networks have driven research towards scalable and decentralized resource allocation strategies. Power control in such networks, particularly in interference-limited environments, is a key challenge due to varying network conditions and device capabilities. In this paper, we introduce a novel framework leveraging graph neural networks (GNNs) for decentralized power allocation in large-scale IIoT networks. By utilizing a centralized training / decentralized execution (CTDE) approach, our method applies deep deterministic policy gradient (DDPG) based unsupervised learning to optimize power allocation, with GNNs enabling localized decision-making through message passing among neighboring transmitters. We show the effectiveness of the proposed framework in both ad-hoc one-to-one and one-to-many network scenarios using 3rd generation partnership project (3GPP)-compliant simulations. Moreover, the generalization tests address the adaptability of the proposed model to unseen conditions, including wireless networks with varying densities and optimization constraints.