Search Results for author: Jun Fang

Found 20 papers, 3 papers with code

Joint Active and Passive Beamforming for IRS-Assisted Radar

no code implementations9 Aug 2021 Fangzhou Wang, Hongbin Li, Jun Fang

Intelligent reflecting surface (IRS) is a promising technology being considered for future wireless communications due to its ability to control signal propagation.

Over-the-Air Federated Multi-Task Learning

no code implementations27 Jun 2021 Haoming Ma, Xiaojun Yuan, Dian Fan, Zhi Ding, Xin Wang, Jun Fang

In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES).

Federated Learning Multi-Task Learning

Recent Advances on Sub-Nyquist Sampling-Based Wideband Spectrum Sensing

no code implementations7 May 2021 Jun Fang, Bin Wang, Hongbin Li, Ying-Chang Liang

Cognitive radio (CR) is a promising technology enabling efficient utilization of the spectrum resource for future wireless systems.

Fast Beam Training and Alignment for IRS-Assisted Millimeter Wave/Terahertz Systems

no code implementations10 Mar 2021 Peilan Wang, Jun Fang, Wei zhang, Hongbin Li

Intelligent reflecting surface (IRS) has emerged as a competitive solution to address blockage issues in millimeter wave (mmWave) and Terahertz (THz) communications due to its capability of reshaping wireless transmission environments.

Session-based Recommendation with Self-Attention Networks

1 code implementation3 Feb 2021 Jun Fang

The self-attention networks (SAN) allow SR-SAN capture the global dependencies among all items of a session regardless of their distance.

Session-Based Recommendations

Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer

no code implementations7 Dec 2020 Huiyuan Yang, Xiaojun Yuan, Jun Fang, Ying-Chang Liang

By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.

Fairness Quantization

Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer

no code implementations2 Jun 2020 Huiyuan Yang, Xiaojun Yuan, Jun Fang, Ying-Chang Liang

By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.

Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems

no code implementations17 Nov 2019 Peilan Wang, Jun Fang, Huiping Duan, Hongbin Li

In this paper, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user.

Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design

no code implementations28 Aug 2019 Peilan Wang, Jun Fang, Xiaojun Yuan, Zhi Chen, Huiping Duan, Hongbin Li

In this framework, we study joint active and passive precoding design for IRS-assisted mmWave systems, where multiple IRSs are deployed to assist the data transmission from a base station (BS) to a single-antenna receiver.

Low-Rank Phase Retrieval via Variational Bayesian Learning

no code implementations5 Nov 2018 Kaihui Liu, Jiayi Wang, Zhengli Xing, Linxiao Yang, Jun Fang

We propose a hierarchical prior model for low-rank phase retrieval, in which a Gaussian-Wishart hierarchical prior is placed on the underlying low-rank matrix to promote the low-rankness of the matrix.

Simultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning

no code implementations6 Nov 2017 Hang Xiao, Zhengli Xing, Linxiao Yang, Jun Fang, Yanlun Wu

In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns.

Bayesian Compressive Sensing Using Normal Product Priors

no code implementations24 Aug 2017 Zhou Zhou, Kaihui Liu, Jun Fang

In this paper, we introduce a new sparsity-promoting prior, namely, the "normal product" prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework.

Compressive Sensing

Robust Bayesian Compressed sensing

no code implementations10 Oct 2016 Qian Wan, Huiping Duan, Jun Fang, Hongbin Li

We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers.

Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems

1 code implementation12 Sep 2016 Zhou Zhou, Jun Fang, Linxiao Yang, Hongbin Li, Zhi Chen, Rick S. Blum

Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems.

Information Theory Information Theory

An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors

no code implementations15 Nov 2015 Linxiao Yang, Jun Fang, Hongbin Li, Bing Zeng

In this paper, we focus on Tucker decomposition which represents an Nth-order tensor in terms of N factor matrices and a core tensor via multilinear operations.

Image Inpainting Recommendation Systems

Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection

no code implementations2 Jun 2015 Nan Zhou, Yangyang Xu, Hong Cheng, Jun Fang, Witold Pedrycz

In this paper, we propose a global and local structure preserving sparse subspace learning (GLoSS) model for unsupervised feature selection.

Feature Selection

Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model

no code implementations7 Mar 2015 Linxiao Yang, Jun Fang, Hong Cheng, Hongbin Li

In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation.

Bayesian Inference Dictionary Learning +1

Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals

no code implementations9 Nov 2013 Jun Fang, Yanning Shen, Hongbin Li, Pu Wang

In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns.

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