Goal-Oriented Uplink Scheduling Requests in Wireless Networks via Graph Neural Networks

In modern industrial internet of things (IIoT) networks, efficient management of communications resources is crucial to match stringent application requirements. Differently from traditional resource allocation policies, goal-oriented communications is an emerging paradigm that aims at better optimizing network resource usage by prioritizing the transmission of information that is most relevant to a given task. Moreover, recent trends show increasing interest in distributed machine learning-based optimization to enhance network performance while reducing the reliance on centralized control. In this context, graph neural networks (GNNs) have emerged as a powerful tool for learning distributed policies among nodes facilitating their cooperation. In this paper, we introduce a goal-oriented, distributed framework to optimize uplink scheduling requests for coordinated message transmission to a remote server, while minimizing communication overhead. Our approach employs GNN-based distributed unsupervised learning framework that does not require a centralized controller. Extensive simulations in a 3GPP-compliant industrial scenario demonstrate that the proposed solution effectively reduces redundant uplink scheduling requests while achieving efficient and scalable coordination across multiple networks. Our findings highlight the potential of GNNs for learning distributed policies that enhance communication efficiency in wireless industrial IoT systems.