Green communication for OverGNN enabled heterogeneous ultra-dense networks

Abstract

With the development of wireless communications, heterogeneous ultra-dense networks (HUDNs) have emerged to meet the requirements of massive connectivity, high data rate, and low latency in the 5G era. Nevertheless, HUDN usually leads to a high-complexity and non-convex NP-hard energy-efficient resource allocation problem. Therefore, A novel heterogeneous Graph neural network (GNN) with high-dimensional computation structure (namely OverGNN) is proposed for the power allocation problem in this work. Particularly, OverGNN enabled nodes directly interact with high-order neighbours and extract abundant graph topological information, which can facilitate effective feature aggregation among nodes as well as alleviate the over-smoothing problem. Based on this fact, an efficient message passing scheme for user equipments under the same base station is developed to approximate the optimal power allocation strategy for maximizing system energy efficiency. In addition, an unsupervised approach is proposed to train the GNN model that can reduce the cost of dataset collection and enhance the scalability of the proposed method. Numerical results verify the effectiveness of the proposed OverGNN and demonstrate its advantages over the benchmarks.