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:灵活的双面网络允许用户在没有静态时间安排的情况下动态地使用上链路和下链路,从而有效利用网络资源。 这项工作调查了灵活双面网络的总和最大化。 特别是, 我们考虑建立一个有双向固定通信链接的网络。 相应的组合优化是一种非决定性的多式(NP)硬体,没有封闭式解决方案。 在这方面, 现有的超文本化带来了高计算复杂性, 增加了大型网络的可缩缩缩问题。 受图形神经网络(GNN)最近在解决NP- 硬无线资源管理问题方面取得的成功驱动, 我们提议建立一个新型的GNN架构, 名为FLex- Net, 以共同优化通信方向和传输能力。 拟议的GNN( NN) 生成了接近最佳的性能, 与最常用的技术相比, 保持低的计算复杂性。 此外, 我们的数字结果揭示了在样本复杂性、 可缩放性和普及能力方面使用GNNN的优势。