46 citations · 152 across the 31 of their papers we have counts for
7 papers · 1 filter
Delay-aware Backpressure Routing Using Graph Neural Networks
Zhongyuan Zhao, Bojan Radojicic, Gunjan Verma +2
We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Cl…
Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks
Zhongyuan Zhao, Ananthram Swami, Santiago Segarra
Distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, e…
Efficient power allocation using graph neural networks and deep algorithm unfolding
Arindam Chowdhury, Gunjan Verma, Chirag Rao +2
We study the problem of optimal power allocation in a single-hop ad hoc wireless network. In solving this problem, we propose a hybrid neural architecture inspired by the algorithm…
Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks
Gojko Cutura, Boning Li, Ananthram Swami +1
We study the temporal reconstruction of epidemics evolving over networks. Given partial or aggregated temporal information of the epidemic, our goal is to estimate the complete evo…
Adaptive Contention Window Design using Deep Q-learning
Abhishek Kumar, Gunjan Verma, Chirag Rao +2
We study the problem of adaptive contention window (CW) design for random-access wireless networks. More precisely, our goal is to design an intelligent node that can dynamically a…
Distributed Scheduling using Graph Neural Networks
Zhongyuan Zhao, Gunjan Verma, Chirag Rao +2
A fundamental problem in the design of wireless networks is to efficiently schedule transmission in a distributed manner. The main challenge stems from the fact that optimal link s…