A Scalable Distributed Link Management Method for Massive IoT with Synchronous Message Passing Neural Network

The development of the next generation ubiquitous network has put forward higher requirements for the connection density of communication devices, which has led to a lot of research on link management. However, with the expansion of network scale, the weaknesses of the existing algorithms in computing efficiency, performance, and realizability have become prominent. The emerging graph neural network (GNN) provides a new way to solve this problem. In order to make full use of the broadcast feature of wireless communication, we design a cross-domain distributed GNN structure (named as synchronous message passing neural network (SynMPNN)) combining the measurable index of the actual scene with message passing mechanism. This new GNN structure and the additional input feature dimension (i.e., SINR) work together to provide more comprehensive information for network training. After the initial deployment of the power decision from SynMPNN, we select some links to shut down and others to reduce their transmit power to further improve the system performance and save energy. Simulation results show that our proposed method under distributed execution conditions reaches 83.1% performance of the centralized method. In addition, the discussion on scalability suggests that in order to save training cost, small-scale scenes with the same density can be selected for training in the application of large-scale scenes.