The following paper by former WiMNet MS student Trevor Gordon, former WiMNet postdoc Prof Igor Kadota and two of Igor’s students Pedro Botelho, Yubo Zhang received the Best Student Paper Award in the 23rd International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt’25).

Y. Zhang, P. Botelho, T. Gordon, G. Zussman, and I. Kadota, “Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning,”  in Proc. WiOpt’25, 2025.

The paper considers a decentralized wireless network with several source-destination pairs sharing a limited number of orthogonal frequency bands. Sources learn to adapt their transmissions (specifically, their band selection strategy) over time, in a decentralized manner, without sharing information with each other. Sources can only observe the outcome of their own transmissions (i.e., success or collision), having no prior knowledge of the network size or of the transmission strategy of other sources. The goal of each source is to maximize their own throughput while striving for network-wide fairness. The paper proposes a novel fully decentralized Reinforcement Learning (RL)-based solution that achieves fairness without coordination.

The paper includes results obtained as part of an NSF NRDZ project.