activity
20142024
most citedSuper-Resolution Compressed Sensing: A Generalized Iterative Reweighted L2 Approach

5 citations · 14 across the 12 of their papers we have counts for

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Showing cs.ITShow all

8 papers · 1 filter

cs.IT20241 cited

Joint Transceiver Optimization for MmWave/THz MU-MIMO ISAC Systems

Peilan Wang, Jun Fang, Xianlong Zeng +2

In this paper, we consider the problem of joint transceiver design for millimeter wave (mmWave)/Terahertz (THz) multi-user MIMO integrated sensing and communication (ISAC) systems.…

cs.IT20232 cited

Stacked Intelligent Metasurface-Aided MIMO Transceiver Design

Jiancheng An, Chau Yuen, Chao Xu +5

Next-generation wireless networks are expected to utilize the limited radio frequency (RF) resources more efficiently with the aid of intelligent transceivers. To this end, we prop…

cs.IT20232 cited

Fundamental Detection Probability vs. Achievable Rate Tradeoff in Integrated Sensing and Communication Systems

Jiancheng An, Hongbin Li, Derrick Wing Kwan Ng +1

Integrating sensing functionalities is envisioned as a distinguishing feature of next-generation mobile networks, which has given rise to the development of a novel enabling techno…

cs.IT20161 cited

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…

cs.IT2016

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…

cs.IT20152 cited

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…