MANET-GNN：多信道MANET中功率分配的学习型去中心化优化

中文摘要 AI 辅助

MANET（移动自组织网络）在动态且资源受限的环境中实现了灵活的无基础设施无线连接。随着现代MANET利用多个频率信道并支持异构流量模式，去中心化的发射功率分配变得越来越具有挑战性。我们开发了一个统一的学习优化框架，用于动态多跳、多信道MANET中的去中心化功率分配。我们构建了一个受约束的端到端吞吐量最大化问题，涵盖单播、多播、多商品、汇聚收集（convergecast）以及多对多通信。尽管该问题是集中式且非凸的，但它作为MANET-GNN的无监督训练目标，MANET-GNN是一种消息传递图神经网络（GNN），作为分布式学习优化器运行。MANET-GNN仅使用局部的、可能带有噪声的信道状态信息（CSI）以及规定次数的邻居消息交换，从而实现低延迟的去中心化推理，同时能够跨拓扑和网络规模进行泛化。数值结果表明，MANET-GNN在各种通信框架下实现了与集中式方法相当的性能，对信道不确定性具有鲁棒性，并能有效扩展到不同的MANET配置。

英文摘要

MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple frequency channels and support heterogeneous traffic patterns, decentralized transmit-power allocation becomes increasingly challenging. We develop a unified learned optimization framework for decentralized power allocation in dynamic multi-hop, multi-channel MANETs. We formulate a constrained end-to-end throughput maximization problem covering unicast, multicast, multicommodity, convergecast, and many-to-many communication. Although centralized and non-convex, this problem serves as an unsupervised training objective for MANET-GNN, a message-passing GNN that operates as a distributed learned optimizer. MANET-GNN uses only local, possibly noisy, CSI and a prescribed number of neighbor message exchanges, enabling low-latency decentralized inference while generalizing across topologies and network sizes. Numerical results show that MANET-GNN achieves centralized-competitive performance across communication frameworks, remains robust to channel uncertainty, and scales effectively across MANET configurations.

发表机构

• Ben-Gurion University of the Negev（内盖夫本-古里安大学）

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