Learning Decentralized Wireless Resource Allocations With Graph Neural Networks

• Published in: IEEE transactions on signal processing Vol. 70; pp. 1850 - 1863
• Main Authors: Wang, Zhiyang, Eisen, Mark, Ribeiro, Alejandro
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Constraints; decentralized; deep learning; Delays; Graph neural networks; Learning; Mathematical models; Neural networks; Optimization; Permutations; Receivers; Resource allocation; Resource management; State (computer science); Statistical learning; Training; Transmitters; Wireless networks
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2022.3163626

We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure. We develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process a sequence of delayed and potentially asynchronous graph aggregated state information obtained locally at each transmitter from multi-hop neighbors. We further utilize model-free primal-dual learning methods to optimize performance subject to constraints in the presence of delay and asynchrony inherent to decentralized networks. We demonstrate a permutation equivariance property of the resulting resource allocation policy that can be shown to facilitate transference to dynamic network configurations. The proposed framework is validated with numerical simulations that exhibit superior performance to baseline strategies.