Abstract
The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN)-based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds.
Highlights
Device-to-device (D2D) communication is considered as a key enabling technology for the Internet of Things (IoT) ecosystem, where the devices communicate with each other directly without the essential interventions of the central agents such as base stations (BSs) and access points (APs) [1]
This feature is favorable in modeling the transmitters and receivers in wireless IoT networks as the geometrical information can be embedded in the graph features
Since edges can model the interactions between nodes, the beneficial and harmful links are separated by modeling the wireless communication system as a directed graph, where the communication link between a transceiver pair can be treated as a node, and the interference link between two nodes can be treated as an edge
Summary
Device-to-device (D2D) communication is considered as a key enabling technology for the Internet of Things (IoT) ecosystem, where the devices communicate with each other directly without the essential interventions of the central agents such as base stations (BSs) and access points (APs) [1]. A random edge graph neural network was proposed in [24] to solve the power optimization problems in wireless ad-hoc networks and cellular networks Their proposed designs [22] - [24] are limited to homogeneous wireless systems and may not be compatible with heterogeneous IoT systems. Inspired by the previous works, a GNN based framework is proposed to tackle the resource allocation problems in wireless IoT networks in a supervised manner in this paper. The proposed GNN based framework for resource allocations in wireless networks is presented, which includes a CE based algorithm for training samples generation, a graph modeling of wireless networks, and a GNN that is operated in a supervised manner.
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