30 citations · 79 across the 10 of their papers we have counts for
10 papers
Hierarchically Block-Sparse Recovery With Prior Support Information
Liyang Lu, Haochen Wu, Wenbo Xu +2
We provide new recovery bounds for hierarchical compressed sensing (HCS) based on prior support information (PSI). A detailed PSI-enabled reconstruction model is formulated using v…
New Paradigm for Unified Near-Field and Far-Field Wireless Communications
Zhaocheng Wang, Haochen Wu, Yuanbin Chen +1
Current Type I and Type II codebooks in fifth generation (5G) wireless communications are limited in supporting the coexistence of far-field and near-field user equipments, as they…
Near-Field Channel Estimation in Dual-Band XL-MIMO with Side Information-Assisted Compressed Sensing
Haochen Wu, Liyang Lu, Zhaocheng Wang
Near-field communication comes to be an indispensable part of the future sixth generation (6G) communications at the arrival of the forth-coming deployment of extremely large-scale…
Near-Field Communications with Block-Dominant Compressed Sensing: Fundamentals, Approaches, and Future Directions
Liyang Lu, Ke Ma, Yue Wang +1
In the context of extremely large-scale antenna arrays deployed in sixth-generation (6G) mobile networks, near-field (NF) communications have gained considerable attention. Unlike…
Block-Sparse Tensor Recovery
Liyang Lu, Zhaocheng Wang, Zhen Gao +2
This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment. We present a generalized model of block-spar…
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO
An Chen, Wenbo Xu, Liyang Lu +1
Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas…
Joint Block-Sparse Recovery Using Simultaneous BOMP/BOLS
Liyang Lu, Zhaocheng Wang, Sheng Chen
We consider the greedy algorithms for the joint recovery of high-dimensional sparse signals based on the block multiple measurement vector (BMMV) model in compressed sensing (CS).…
Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit
Liyang Lu, Wenbo Xu, Yue Wang +1
This paper proposes two novel schemes of wideband compressive spectrum sensing (CSS) via block orthogonal matching pursuit (BOMP) algorithm, for achieving high sensing accuracy in…
Blind Orthogonal Least Squares based Compressive Spectrum Sensing
Liyang Lu, Wenbo Xu, Yue Wang +1
As an enabling technique of cognitive radio (CR), compressive spectrum sensing (CSS) based on compressive sensing (CS) can detect the spectrum opportunities from wide frequency ban…
Recovery Conditions of Sparse Signals Using Orthogonal Least Squares-Type Algorithms
L. Lu, W. Xu, Y. Wang +1
Orthogonal least squares (OLS)-type algorithms are efficient in reconstructing sparse signals, which include the well-known OLS, multiple OLS (MOLS) and block OLS (BOLS). In this p…