Link Scheduling using Graph Neural Networks

1 code implementation • 12 Sep 2021 • Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

Test results on medium-sized wireless networks show that our centralized heuristic can reach a near-optimal solution quickly, and our distributed heuristic based on a shallow GCN can reduce by nearly half the suboptimality gap of the distributed greedy solver with minimal increase in complexity.

Scheduling

Efficient power allocation using graph neural networks and deep algorithm unfolding

1 code implementation • 18 Nov 2020 • Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

We study the problem of optimal power allocation in a single-hop ad hoc wireless network.

Distributed Scheduling using Graph Neural Networks

1 code implementation • 18 Nov 2020 • Zhongyuan Zhao, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.

Scheduling

Adaptive Contention Window Design using Deep Q-learning

1 code implementation • 18 Nov 2020 • Abhishek Kumar, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

We study the problem of adaptive contention window (CW) design for random-access wireless networks.

Q-Learning Reinforcement Learning (RL)

Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation

1 code implementation • 22 Sep 2020 • Arindam Chowdhury, Gunjan Verma, Chirag Rao, Ananthram Swami, Santiago Segarra

We study the problem of optimal power allocation in a single-hop ad hoc wireless network.