Problem
Research questions and friction points this paper is trying to address.
MANETs
decentralized power allocation
multi-channel
throughput maximization
MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs
🤖 AI Summary
This study addresses the challenges of high latency and poor generalization in decentralized power allocation for multi-channel mobile ad hoc networks (MANETs) operating under dynamic, resource-constrained conditions. To overcome these limitations, this work proposes a unified learning-to-optimize framework that formulates a non-convex centralized problem as an unsupervised training objective. By leveraging message-passing graph neural networks (GNNs), the approach enables distributed, low-latency inference relying exclusively on local channel state information. The proposed method demonstrates strong generalization capabilities across varying network topologies, achieving performance comparable to centralized schemes. Furthermore, it exhibits robustness against channel uncertainties and scales effectively across different network sizes, offering a practical and efficient solution for decentralized resource management in MANETs.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Graph Neural Network
Decentralized Optimization
Power Allocation
MANET
Unsupervised Learning
🔎 Similar Papers
No similar papers found.
