528 citations · 1.4k across the 16 of their papers we have counts for
16 papers
Near/Far-Field Channel Estimation For Terahertz Systems With ELAAs: A Block-Sparse-Aware Approach
Hongwei Wang, Jun Fang, Huiping Duan +1
Millimeter wave/Terahertz (mmWave/THz) communication with extremely large-scale antenna arrays (ELAAs) offers a promising solution to meet the escalating demand for high data rates…
Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems
Hongwei Wang, Jun Fang, Huiping Duan +1
We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems. Spat…
A Variational Bayesian Inference-Inspired Unrolled Deep Network for MIMO Detection
Qian Wan, Jun Fang, Yinsen Huang +2
The great success of deep learning (DL) has inspired researchers to develop more accurate and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL-ba…
Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
Peilan Wang, Jun Fang, Huiping Duan +1
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 transm…
Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design
Peilan Wang, Jun Fang, Xiaojun Yuan +3
Millimeter wave (MmWave) communications is capable of supporting multi-gigabit wireless access thanks to its abundant spectrum resource. However, the severe path loss and high dire…
Fast Compressed Power Spectrum Estimation: Towards A Practical Solution for Wideband Spectrum Sensing
Linxiao Yang, Jun Fang, Huiping Duan +1
There has been a growing interest in wideband spectrum sensing due to its applications in cognitive radios and electronic surveillance. To overcome the sampling rate bottleneck for…
Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach
Xingjian Li, Jun Fang, Huiping Duan +2
In this paper, we studied the problem of beam alignment for millimeter wave (mmWave) communications, in which we assume a hybrid analog and digital beamforming structure is employe…
Proximal-Free ADMM for Decentralized Composite Optimization via Graph Simplification
Bin Wang, Jun Fang, Huiping Duan +1
We consider the problem of decentralized composite optimization over a symmetric connected graph, in which each node holds its own agent-specific private convex functions, and comm…
Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach
Xingjian Li, Jun Fang, Wen Cheng +3
We consider the problem of spectrum sharing in a cognitive radio system consisting of a primary user and a secondary user. The primary user and the secondary user work in a non-coo…
Phased Array-Based Sub-Nyquist Sampling for Joint Wideband Spectrum Sensing and Direction-of-Arrival Estimation
Feiyu Wang, Jun Fang, Huiping Duan +1
In this paper, we study the problem of joint wideband spectrum sensing and direction-of-arrival (DoA) estimation in a sub-Nyquist sampling framework. Specifically, considering a sc…
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models
Linxiao Yang, Jun Fang, Huiping Duan +2
The problem of low rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior mod…
Robust Bayesian Compressed sensing
Qian Wan, Huiping Duan, Jun Fang +1
We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers. A new sparse…
Real-Valued Khatri-Rao Subspace Approaches on the ULA and a New Nested Array
Huiping Duan, Tiantian Tuo, Jun Fang +1
In underdetermined direction-of-arrival (DOA) estimation using the covariance-based signal models, the computational complexity turns into a noticeable issue because of the high di…
Computationally Efficient Sparse Bayesian Learning via Generalized Approximate Message Passing
Fuwei Li, Jun Fang, Huiping Duan +2
The sparse Beyesian learning (also referred to as Bayesian compressed sensing) algorithm is one of the most popular approaches for sparse signal recovery, and has demonstrated supe…
Super-Resolution Compressed Sensing: A Generalized Iterative Reweighted L2 Approach
Jun Fang, Huiping Duan, Jing Li +2
Conventional compressed sensing theory assumes signals have sparse representations in a known, finite dictionary. Nevertheless, in many practical applications such as direction-of-…
One-Bit Quantization Design and Adaptive Methods for Compressed Sensing
Jun Fang, Yanning Shen, Hongbin Li
There have been a number of studies on sparse signal recovery from one-bit quantized measurements. Nevertheless, little attention has been paid to the choice of the quantization th…