Search Results for author:
Found 2 papers, 0 papers with code
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks
Without the need to retrain, DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x larger in the number of tags than those used for training, breaking the scalability limitations of the optimal scheduler and reducing carrier utilization by up to 50% compared to the state-of-the-art heuristic.
Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
