Search Results for author:
Found 9 papers, 3 papers with code
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments
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.
Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
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
As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems.
Decentralized Inference with Graph Neural Networks in Wireless Communication Systems
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.
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
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.
A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions
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.
Accelerating Generalized Benders Decomposition for Wireless Resource Allocation
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
In this paper, we propose a novel graph embedding based method for link scheduling in D2D networks.
Learning to Branch: Accelerating Resource Allocation in Wireless Networks
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
