5 citations · 9 across the 7 of their papers we have counts for
7 papers
On the Performance of X-Duplex Relaying
Shuai Li, Mingxin Zhou, Jianjun Wu +3
In this paper, we study a X-duplex relay system with one source, one amplify-and-forward (AF) relay and one destination, where the relay is equipped with a shared antenna and two r…
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems
Zhou Zhou, Jun Fang, Linxiao Yang +3
We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large ant…
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…
Low-Rank Covariance-Assisted Downlink Training and Channel Estimation for FDD Massive MIMO Systems
Jun Fang, Xingjian Li, Hongbin Li +1
We consider the problem of downlink training and channel estimation in frequency division duplex (FDD) massive MIMO systems, where the base station (BS) equipped with a large numbe…
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-…