The increasing demand for mobile ad hoc networks (MANETs) calls for decentralized mechanisms that can allocate transmit power across nodes and channels under stringent resource constraints. Existing optimization-based approaches, however, do not account for expected settings where each link includes multiple channels (e.g., multi-band signaling). Motivated by recent advances in machine learning for distributed optimization, we propose MANET-GNN, a graph neural network (GNN)-based algorithm for decentralized power allocation in multi-channel MANETs. MANET-GNN explicitly exploits the network topology, scales efficiently with the number of nodes and frequency bands, generalizes across topologies and channel conditions, and enables near-instantaneous inference suitable for real-time deployment. Our design builds on a constrained optimization formulation and employs a dedicated GNN architecture inspired by message passing, trained via an unsupervised procedure that is robust to noisy channel state information. Numerical evaluations demonstrate that MANET-GNN achieves high-throughput multi-channel communication across diverse MANET scenarios.

翻译：移动自组网（MANET）日益增长的需求要求在严格资源约束下，能够跨节点和信道分配发射功率的去中心化机制。然而，现有的基于优化的方法并未考虑每条链路包含多个信道（例如多频段信号）的预期场景。受近期分布式优化机器学习进展的启发，我们提出MANET-GNN，一种基于图神经网络（GNN）的去中心化多信道MANET功率分配算法。MANET-GNN显式利用网络拓扑结构，随节点数和频段数高效扩展，能够跨拓扑结构和信道条件泛化，并实现适合实时部署的近乎即时的推理。我们的设计基于约束优化公式，并采用受消息传递启发的专用GNN架构，通过鲁棒抵抗噪声信道状态信息的无监督训练过程进行训练。数值评估表明，MANET-GNN在多种MANET场景下实现了高吞吐量的多信道通信。