Yifei Shen

TL;DR: In this paper, a message passing graph neural network (MPGNN) was proposed to solve large-scale radio resource management problems in wireless networks, which can solve the beamforming problem in an interference channel with 1000 transceiver pairs within 6 milliseconds on a single GPU.

TL;DR: This paper demonstrates that radio resource management problems can be formulated as graph optimization problems that enjoy a universal permutation equivariance property, and identifies a family of neural networks, named message passing graph neural networks (MPGNNs), which can generalize to large-scale problems, while enjoying a high computational efficiency.

TL;DR: Li et al. as mentioned in this paper proposed a framework of Learning to Optimize for Resource Management (LORM), which learns the optimal pruning policy in the branch-and-bound algorithm for mixed-integer nonlinear programming (MINLPs) via a sampleefficient method, namely, imitation learning.

TL;DR: In this paper, an interference graph convolutional neural network (IGCNet) is proposed to learn the optimal power control in an unsupervised manner, which is a universal approximation to continuous set functions.

TL;DR: A transfer learning method via self-imitation in LORM can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples, and achieves comparable performance with the model trained from scratch with sufficient labeled samples.