Optimizing power control in multi-cell cellular networks with deep learning enables such a non-convex problem to be implemented in real-time. When channels are time-varying, the deep neural networks (DNNs) need to be re-trained frequently, which calls for low training complexity. To reduce the number of training samples and the size of DNN required to achieve good performance, a promising approach is to embed the DNNs with priori knowledge. Since cellular networks can be modelled as a graph, it is natural to employ graph neural networks (GNNs) for learning, which exhibit permutation invariance (PI) and equivalence (PE) properties. Unlike the homogeneous GNNs that have been used for wireless problems, whose outputs are invariant or equivalent to arbitrary permutations of vertexes, heterogeneous GNNs (HetGNNs), which are more appropriate to model cellular networks, are only invariant or equivalent to some permutations. If the PI or PE properties of the HetGNN do not match the property of the task to be learned, the performance degrades dramatically. In this paper, we show that the power control policy has a combination of different PI and PE properties, and existing HetGNN does not satisfy these properties. We then design a parameter sharing scheme for HetGNN such that the learned relationship satisfies the desired properties. Simulation results show that the sample complexity and the size of designed GNN for learning the optimal power control policy in multi-user multi-cell networks are much lower than the existing DNNs, when achieving the same sum rate loss from the numerically obtained solutions.
CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images
Morgridge Institute for Research（莫里格研究所）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Institute for Molecular Medicine Finland (FIMM)（芬兰分子医学研究所）；University of Helsinki（赫尔辛基大学）；Boston University（波士顿大学）；Institute of Computational Biology, Helmholtz Munich（海德堡医学院计算生物学研究所）；Massachusetts Institute of Technology（麻省理工学院）；Broad Institute of MIT and Harvard（MIT和哈佛大学博德研究所）
Automatic Programming of Cellular Automata and Artificial Neural Networks Guided by Philosophy
Institute for Information Systems, FHNW University of Applied Sciences and Arts Northwestern Switzerland（瑞士西北应用科学与艺术大学信息系统研究所）；Chair for Philosophy, ETH Zurich（苏黎世联邦理工学院哲学教席）
