Joint Admission and Power Control for Big Data Access Management Using GAT

The emerging artificial intelligence (AI) puts forward high requirement for big data acquisition, which is difficult to be met with the existing communication technologies in real time. In this paper, we investigate new graph learning based access management scheme for supporting the real-time big data acquisition in the sixth-generation mobile communication system (6G). We model the network scene with a mass of communication links as a fully connected graph which takes into account the accumulative interference of all links. Then, the joint admission and power control problem is formulated as a combinatorial optimization problem. We propose a graph attention network (GAT) based algorithm which can learn the system features by weighted aggregation of neighbor nodes. In addition, we construct a differentiable loss function that can accurately express the optimization objective and train the network by the change of loss. Based on the output of the GAT, we iteratively optimize the link admission and power to active more links. Simulation results demonstrate that the proposed algorithm is superior to the traditional convex optimization based algorithms and the nonmodified GAT based algorithms in the number of activated links. Moreover, the training of the constructed network is unsupervised with high computational efficiency, which makes them suitable for the big data access management.