82 citations · 416 across the 34 of their papers we have counts for
34 papers
Matrix Factorization Based Blind Bayesian Receiver for Grant-Free Random Access in mmWave MIMO mMTC
Zhengdao Yuan, Fei Liu, Qinghua Guo +3
Grant-free random access is promising for massive connectivity with sporadic transmissions in massive machine type communications (mMTC), where the hand-shaking between the access…
A Unitary Transform Based Generalized Approximate Message Passing
Jiang Zhu, Xiangming Meng, Xupeng Lei +1
We consider the problem of recovering an unknown signal from general nonlinear measurements obtained through a generalized linear model (GLM), i.e.,…
Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks
Dawei Gao, Qinghua Guo, Guisheng Liao +4
In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in…
Unitary Approximate Message Passing for Matrix Factorization
Zhengdao Yuan, Qinghua Guo, Yonina C. Eldar +1
We consider matrix factorization (MF) with certain constraints, which finds wide applications in various areas. Leveraging variational inference (VI) and unitary approximate messag…
A Relaxed Energy Function Based Analog Neural Network Approach to Target Localization in Distributed MIMO Radar
Xiaoyu Zhao, Jun Li, Qinghua Guo
Analog neural networks are highly effective to solve some optimization problems, and they have been used for target localization in distributed multiple-input multiple-output (MIMO…
Efficient Channel Estimation for RIS-Aided MIMO Communications with Unitary Approximate Message Passing
Yabo Guo, Peng Sun, Zhengdao Yuan +4
Reconfigurable intelligent surface (RIS) is very promising for wireless networks to achieve high energy efficiency, extended coverage, improved capacity, massive connectivity, etc.…
Grant Free MIMO-NOMA with Differential Modulation for Machine Type Communications
Yuanyuan Zhang, Zhengdao Yuan, Qinghua Guo +4
This paper considers a challenging scenario of machine type communications, where we assume internet of things (IoT) devices send short packets sporadically to an access point (AP)…
Joint Channel Estimation and Signal Recovery for RIS-Empowered Multi-User Communications
Li Wei, Chongwen Huang, Qinghua Guo +5
Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and lowp…
Unitary Approximate Message Passing for Sparse Bayesian Learning
Man Luo, Qinghua Guo, Ming Jin +4
Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm. However, it does not work well for a generic measure…
Message Passing Based Structured Sparse Signal Recovery for Estimation of OTFS Channels with Fractional Doppler Shifts
Fei Liu, Zhengdao Yuan, Qinghua Guo +2
The orthogonal time frequency space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To enable efficient OTFS detection in…
Iterative Detection for Orthogonal Time Frequency Space Modulation with Unitary Approximate Message Passing
Zhengdao Yuan, Fei Liu, Weijie Yuan +3
The orthogonal-time-frequency-space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To harvest the time and frequency dive…
Massive MIMO As an Extreme Learning Machine
Dawei Gao, Qinghua Guo, Yonina C. Eldar
This work shows that a massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM).…
User Activity Detection and Channel Estimation for Grant-Free Random Access in LEO Satellite-Enabled Internet-of-Things
Zhaoji Zhang, Ying Li, Chongwen Huang +4
With recent advances on the dense low-earth orbit (LEO) constellation, LEO satellite network has become one promising solution to providing global coverage for Internet-of-Things (…
Approximate Message Passing with Unitary Transformation for Robust Bilinear Recovery
Zhengdao Yuan, Qinghua Guo, Man Luo
Recently, several promising approximate message passing (AMP) based algorithms have been developed for bilinear recovery with model $\boldsymbol{Y}=\sum_{k=1}^K b_k \boldsymbol{A}_…
Bayesian Receiver Design for Grant-Free NOMA with Message Passing Based Structured Signal Estimation
Yuanyuan Zhang, Zhengdao Yuan, Qinghua Guo +3
Grant-free non-orthogonal multiple access (NOMA) is promising to achieve low latency massive access in Internet of Things (IoT) applications. In grant-free NOMA, pilot signals are…
DNN-Aided Block Sparse Bayesian Learning for User Activity Detection and Channel Estimation in Grant-Free Non-Orthogonal Random Access
Zhaoji Zhang, Ying Li, Chongwen Huang +3
In the upcoming Internet-of-Things (IoT) era, the communication is often featured by massive connection, sporadic transmission, and small-sized data packets, which poses new requir…
DNN-Aided Message Passing Based Block Sparse Bayesian Learning for Joint User Activity Detection and Channel Estimation
Zhaoji Zhang, Ying Li, Chongwen Huang +3
Faced with the massive connection, sporadic transmission, and small-sized data packets in future cellular communication, a grant-free non-orthogonal random access (NORA) system is…
Sparse Bayesian Learning Using Approximate Message Passing with Unitary Transformation
Man Luo, Qinghua Guo
Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm. However, it is vulnerable to `difficult' measurement…
On the Performance of Massive MIMO Systems With Low-Resolution ADCs Over Rician Fading Channels
Tianle Liu, Jun Tong, Qinghua Guo +3
This paper considers uplink massive multiple-input multiple-output (MIMO) systems with lowresolution analog-to-digital converters (ADCs) over Rician fading channels. Maximum-ratio-…
Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
Rui Hu, Jun Tong, Jiangtao Xi +2
Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Chann…
Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications
Dawei Gao, Qinghua Guo, Jun Tong +3
This work concerns receiver design for light emitting diode (LED) communications where the LED nonlinearity can severely degrade the performance of communications. We propose extre…
Extreme Learning Machine-Based Receiver for MIMO LED Communications
Dawei Gao, Qinghua Guo
This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performan…
Linear Shrinkage Estimation of Covariance Matrices Using Low-Complexity Cross-Validation
Jun Tong, Rui Hu, Jiangtao Xi +3
Shrinkage can effectively improve the condition number and accuracy of covariance matrix estimation, especially for low-sample-support applications with the number of training samp…
Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments
Rui Hu, Jun Tong, Jiangtao Xi +2
Hybrid massive MIMO structures with reduced hardware complexity and power consumption have been widely studied as a potential candidate for millimeter wave (mmWave) communications.…