Edge Convolution Graph Neural Network Assisted Power Allocation for Wireless IoT Networks

Abstract

We propose a novel power control technique called PC-ECGNN, which uses edge convolution to optimize power allocation in wireless IoT networks. PC-ECGNN leverages interference link distances as edge features and desired link channel gains as initial vertex features, iteratively updating vertex features based on neighbors and edge features. PC-ECGNN is the first technique to incorporate edge convolution into power control and has been customized for the considered scenario, optimizing the neural network structure to provide fast convergence and high performance simultaneously. Experimental results show that PC-ECGNN outperformed the state-of-the-art PC-MPGNN, achieving a 4% increase in average spectral efficiency and a 4dBm reduction in average transmit power compared to PC-MPGNN. Furthermore, our technique demonstrates advantages over existing methods in dynamic environmental changes. The proposed model, trained in a fixed environment, showed minimal performance degradation across various test environments different from the training setting, outperforming traditional models trained in individual environments. When applying meta-learning, the proposed model achieved better performance in each test environment after additional fine-tuning with only 1% of the pre-training epochs, compared to models trained with the full number of epochs in each individual test environment.

Keywords

• Interference management
• power control
• graph neural networks
• edge convolution