Flexible duplex networks allow users to dynamically employ uplink and downlink channels without static time scheduling, thereby utilizing the network resources efficiently. This work investigates the sum-rate maximization of flexible duplex networks. In particular, we consider a network with pairwise-fixed communication links. Corresponding combinatorial optimization is a non-deterministic polynomial (NP)-hard without a closed-form solution. In this respect, the existing heuristics entail high computational complexity, raising a scalability issue in large networks. Motivated by the recent success of Graph Neural Networks (GNNs) in solving NP-hard wireless resource management problems, we propose a novel GNN architecture, named Flex-Net, to jointly optimize the communication direction and transmission power. The proposed GNN produces near-optimal performance meanwhile maintaining a low computational complexity compared to the most commonly used techniques. Furthermore, our numerical results shed light on the advantages of using GNNs in terms of sample complexity, scalability, and generalization capability.

翻译：柔性双工网络允许用户在不依赖静态时间调度的条件下动态使用上行和下行信道，从而高效利用网络资源。本文研究柔性双工网络的和速率最大化问题。具体而言，我们考虑具有固定成对通信链路的网络。相应的组合优化问题为非确定性多项式（NP）难问题，且不存在闭式解。在此背景下，现有启发式算法计算复杂度高，导致大规模网络面临可扩展性问题。受图神经网络（GNN）在解决NP难无线资源管理问题方面取得的最新进展启发，我们提出一种名为Flex-Net的新型GNN架构，以联合优化通信方向与传输功率。与主流技术相比，所提出的GNN在保持低计算复杂度的同时实现了近优性能。此外，数值实验揭示了GNN在样本复杂度、可扩展性和泛化能力方面的优势。