Search Results for author: Mengyuan Lee

Found 9 papers, 3 papers with code

Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments

no code implementations23 Jun 2023 Qiushuo Hou, Mengyuan Lee, Guanding Yu, Yunlong Cai

The proposed framework, consisting of an inner network and an outer network, aims to adapt to the dynamic wireless environment by achieving three important goals, i. e., seamlessness, quickness and continuity.

Image Classification Meta-Learning +2

Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions

no code implementations8 Dec 2022 Mengyuan Lee, Guanding Yu, Huaiyu Dai, Geoffrey Ye Li

As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communications, exhibiting good potential for the advancement of next-generation wireless communications.

Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks

no code implementations15 Aug 2022 Mengyuan Lee, Guanding Yu, Huaiyu Dai

As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems.

Efficient Neural Network Management +1

Decentralized Inference with Graph Neural Networks in Wireless Communication Systems

no code implementations19 Apr 2021 Mengyuan Lee, Guanding Yu, Huaiyu Dai

Different from other neural network models, GNN can be implemented in a decentralized manner with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems.

Efficient Neural Network Graph Neural Network

Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

no code implementations4 Jan 2021 Su Wang, Mengyuan Lee, Seyyedali Hosseinalipour, Roberto Morabito, Mung Chiang, Christopher G. Brinton

The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server.

Federated Learning Learning Theory

A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions

no code implementations29 Sep 2020 Mengyuan Lee, Seyyedali Hosseinalipour, Christopher G. Brinton, Guanding Yu, Huaiyu Dai

However, the problem of allocating items among the bidders to maximize the auctioneers" revenue, i. e., the winner determination problem (WDP), is NP-complete to solve and inapproximable.

Cloud Computing Graph Neural Network

Accelerating Generalized Benders Decomposition for Wireless Resource Allocation

1 code implementation3 Mar 2020 Mengyuan Lee, Ning Ma, Guanding Yu, Huaiyu Dai

Only useful cuts are added to the master problem and thus the complexity of the master problem is reduced.

Graph Embedding based Wireless Link Scheduling with Few Training Samples

1 code implementation7 Jun 2019 Mengyuan Lee, Guanding Yu, Geoffrey Ye Li

In this paper, we propose a novel graph embedding based method for link scheduling in D2D networks.

Graph Embedding Scheduling

Learning to Branch: Accelerating Resource Allocation in Wireless Networks

1 code implementation5 Mar 2019 Mengyuan Lee, Guanding Yu, Geoffrey Ye Li

Moreover, we develop a mixed training strategy to further reinforce the generalization ability and a deep neural network (DNN) with a novel loss function to achieve better dynamic control over optimality and computational complexity.

Information Theory Information Theory

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