528 citations · 1.4k across the 16 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…