Search Results for author: Ming Xiao

Found 17 papers, 0 papers with code

RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

no code implementations25 Mar 2024 Xiaojie Li, Songyang Zhang, Hang Li, Xiaoyang Li, Lexi Xu, Haigao Xu, Hui Mei, Guangxu Zhu, Nan Qi, Ming Xiao

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning.

Graph Attention

Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation

no code implementations22 Mar 2024 Chengxi Li, Ming Xiao, Mikael Skoglund

In ACFL, before the training, each device uploads a coded local dataset with additive noise to the central server to generate a global coded dataset under privacy preservation requirements.

Federated Learning

Design of Reconfigurable Intelligent Surface-Aided Cross-Media Communications

no code implementations5 Nov 2022 Mingming Wu, Yue Xiao, Yulan Gao, Ming Xiao

A novel reconfigurable intelligent surface (RIS)-aided hybrid reflection/transmitter design is proposed for achieving information exchange in cross-media communications.

Management

Clinicopathological correlation of p40/TTF1 co-expression in NSCLC and review of related literature

no code implementations14 Sep 2022 LiAn Yang, Ming Xiao, Xian Li, Ya-lan Wang

Mutations in STK11/LKB1 and NF1 genes have been found in ADC and SQC and are often associated with drug resistance and poor prognosis, but STK11/NF1 co-mutation has not been reported and more cases are needed to reveal the association.

Specificity

Vertical GaN Diode BV Maximization through Rapid TCAD Simulation and ML-enabled Surrogate Model

no code implementations18 Jul 2022 Albert Lu, Jordan Marshall, Yifan Wang, Ming Xiao, Yuhao Zhang, Hiu Yung Wong

In this paper, two methodologies are used to speed up the maximization of the breakdown volt-age (BV) of a vertical GaN diode that has a theoretical maximum BV of ~2100V.

Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning

no code implementations7 Feb 2022 Hao Chen, Yu Ye, Ming Xiao, Mikael Skoglund

This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices and the learning algorithm run on-device, with the aim of relaxing the burden at a central entity/server.

BIG-bench Machine Learning

Satellite Based Computing Networks with Federated Learning

no code implementations20 Nov 2021 Hao Chen, Ming Xiao, Zhibo Pang

Driven by the ever-increasing penetration and proliferation of data-driven applications, a new generation of wireless communication, the sixth-generation (6G) mobile system enhanced by artificial intelligence (AI), has attracted substantial research interests.

Federated Learning

Federated Learning over Wireless IoT Networks with Optimized Communication and Resources

no code implementations22 Oct 2021 Hao Chen, Shaocheng Huang, Deyou Zhang, Ming Xiao, Mikael Skoglund, H. Vincent Poor

Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of things (IoT) networks.

Federated Learning Scheduling

Regularized Sequential Latent Variable Models with Adversarial Neural Networks

no code implementations10 Aug 2021 Jin Huang, Ming Xiao

The recurrent neural networks (RNN) with richly distributed internal states and flexible non-linear transition functions, have overtaken the dynamic Bayesian networks such as the hidden Markov models (HMMs) in the task of modeling highly structured sequential data.

Variational Inference

Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT

no code implementations30 Jun 2021 Wanlu Lei, Yu Ye, Ming Xiao, Mikael Skoglund, Zhu Han

Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation, and has shown faster convergence than gradient descent based methods.

Decision Making Edge-computing +2

Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing

no code implementations2 Oct 2020 Hao Chen, Yu Ye, Ming Xiao, Mikael Skoglund, H. Vincent Poor

A class of mini-batch stochastic alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.

Edge-computing

Fully Decentralized Federated Learning Based Beamforming Design for UAV Communications

no code implementations27 Jul 2020 Yue Xiao, Yu Ye, Shaocheng Huang, Li Hao, Zheng Ma, Ming Xiao, Shahid Mumtaz

To handle the data explosion in the era of internet of things (IoT), it is of interest to investigate the decentralized network, with the aim at relaxing the burden to central server along with keeping data privacy.

Signal Processing

Decentralized Beamforming Design for Intelligent Reflecting Surface-enhanced Cell-free Networks

no code implementations22 Jun 2020 Shaocheng Huang, Yu Ye, Ming Xiao, H. Vincent Poor, Mikael Skoglund

Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems.

Learning Based Hybrid Beamforming for Millimeter Wave Multi-User MIMO Systems

no code implementations27 Apr 2020 Shaocheng Huang, Yu Ye, Ming Xiao

Hybrid beamforming (HBF) design is a crucial stage in millimeter wave (mmWave) multi-user multi-input multi-output (MU-MIMO) systems.

Learning Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems

no code implementations16 Apr 2020 Shaocheng Huang, Yu Ye, Ming Xiao

We propose two learning schemes to design HBF for FD mmWave systems, i. e., extreme learning machine based HBF (ELM-HBF) and convolutional neural networks based HBF (CNN-HBF).

Mobility-aware Content Preference Learning in Decentralized Caching Networks

no code implementations22 Aug 2019 Yu Ye, Ming Xiao, Mikael Skoglund

To determine the caching scheme for decentralized caching networks, the content preference learning problem based on mobility prediction is studied.

Multi-Task Learning

Decentralized Multi-Task Learning Based on Extreme Learning Machines

no code implementations25 Apr 2019 Yu Ye, Ming Xiao, Mikael Skoglund

We first present the ELM based MTL problem in the centralized setting, which is solved by the proposed MTL-ELM algorithm.

Multi-Task Learning

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