Message Passing Assisted Scalable Distributed Link Management for Ubiquitous Network

The development of the next generation ubiquitous network puts forward higher requirements for the connection density in the communication network, e.g., massive IoT and UAV swarm, which has led to a lot of research on link management. With the expansion of network scale, the weaknesses of existing algorithms in computing efficiency, performance, and realizability have become prominent. The emerging graph neural network (GNN) provides another way to solve this problem. In this paper, we design a cross-receptive distributed GNN structure from the perspective of communication system, combining measurable index of the actual scene with message passing frame. This new GNN structure and the additional input feature dimension work together to provide richer and more comprehensive information for network training. After the initial deployment of the power decision from GNN, we select some links to shut down and others to reduce their transmit power to further improve system performance and save energy. Simulation results show that our proposed method reaches 83.1% performance of the centralized mechanism. 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.