6 papers
Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
Mengyuan Lee, Guanding Yu, Huaiyu Dai +1
As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communica…
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
Su Wang, Mengyuan Lee, Seyyedali Hosseinalipour +3
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated b…
A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions
Mengyuan Lee, Seyyedali Hosseinalipour, Christopher G. Brinton +2
The combinatorial auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. It can obtain high economic efficiency and user fle…
Accelerating Generalized Benders Decomposition for Wireless Resource Allocation
Mengyuan Lee, Ning Ma, Guanding Yu +1
Generalized Benders decomposition (GBD) is a globally optimal algorithm for mixed integer nonlinear programming (MINLP) problems, which are NP-hard and can be widely found in the a…
Graph Embedding based Wireless Link Scheduling with Few Training Samples
Mengyuan Lee, Guanding Yu, Geoffrey Ye Li
Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution…
Learning to Branch: Accelerating Resource Allocation in Wireless Networks
Mengyuan Lee, Guanding Yu, Geoffrey Ye Li
Resource allocation in wireless networks, such as device-to-device (D2D) communications, is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are ge…