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In order to achieve high data rate and ubiquitous connectivity in futurewireless networks, a key task is to efficiently manage the radio resource byjudicious beamforming and power allocation. Unfortunately, the iterative natureof the commonly applied optimization-based algorithms cannot meet the lowlatency requirements due to the high computational complexity. For real-timeimplementations, deep learning-based approaches, especially the graph neuralnetworks (GNNs), have been demonstrated with good scalability andgeneralization performance due to the permutation equivariance (PE) property.However, the current architectures are only equipped with the node-updatemechanism, which prohibits the applications to a more general setup, where theunknown variables are also defined on the graph edges. To fill this gap, wepropose an edge-update mechanism, which enables GNNs to handle both node andedge variables and prove its PE property with respect to both transmitters andreceivers. Simulation results on typical radio resource management problemsdemonstrate that the proposed method achieves higher sum rate but with muchshorter computation time than state-of-the-art methods and generalizes well ondifferent numbers of base stations and users, different noise variances,interference levels, and transmit power budgets.

ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks