A Synchronous Training Hypergraph Neural Network for Power Allocation in Multi-Cell Multi-User Networks

This letter proposes a novel approach for optimizing power allocation in multi-cell multi-user (MCMU) networks using a hypergraph neural network (HGNN). In MCMU networks, each base station (BS) serves multiple user equipments (UEs). This multivariate and implicit connection introduces computational overheads and is unsuitable for pairwise relationship modeling. To address this challenge, we first propose a hypergraph structure that represents BSs as hyperedges, capturing the complex interactions and multiple dependencies within the network. Second, a synchronous training loss is developed, which includes negative weighted sum rate and parameter regularity terms. The first term can learn the distribution without relying on labeled data. The second term avoids overfitting and improves scalability. Third, power constraints are embedded into the network architecture to ensure the feasibility of the power allocation. Extensive simulations demonstrate that our proposed HGNN achieves higher sum rate than the baselines and exhibits its excellent scalability with the increase of complexity in future networks.