A Lightweight Link Scheduling Algorithm in IWNs Based on Hybrid Graph Representation Learning

In industrial wireless networks with resource-constrained and densely deployed devices, link scheduling is a challenging task. Traditional optimization methods have high computational complexity and low scalability. Graph learning offers a promising approach, yet it also comes with limitations of capturing multivariate relationships from interference, leading to ineffective link scheduling. In this article, hypergraphs are additionally introduced to model cumulative interference from concurrent transmissions. Considering the constructed comprehensive interference model, we propose a Lightweight link scheduling algorithm based on hybrid binary Graph and HyperGraph representation learning (L-GHG) in an unsupervised manner. Thereinto, the L-GHG algorithm extracts and fuses features from various interference relationships for link scheduling decisions. A graph partitioning based on channel state information is proposed to reduce redundant search space arising from cumulative interference. Simulations demonstrate that our proposed algorithm outperforms benchmark schemes regarding generalizability and scalability. We also validate the availability of the proposed algorithm in practical experiments.