activity
20162023
most citedLow-complexity Sparse Array Synthesis Based on Off-grid Compressive Sensing

37 citations · 50 across the 14 of their papers we have counts for

collaborators

19 papers

cs.IT2023★ 1 cited

Near-Field Channel Estimation for Extremely Large-Scale Reconfigurable Intelligent Surface (XL-RIS)-Aided Wideband mmWave Systems

Songjie Yang, Chenfei Xie, Wanting Lyu +3

Near-field communications present new opportunities over near-field channels, however, the spherical wavefront propagation makes near-field signal processing challenging. In this c…

cs.IT2022

Active 3D Double-RIS-Aided Multi-User Communications: Two-Timescale-Based Separate Channel Estimation via Bayesian Learning

Songjie Yang, Wanting Lyu, Yue Xiu +2

Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided s…

eess.SP2022★ 2 cited

Energy-Efficient Cell-Free Network Assisted by Hybrid RISs

Wanting Lyu, Yue Xiu, Songjie Yang +2

In this letter, we investigate a cell-free network aided by hybrid reconfigurable intelligent surfaces (RISs), which consists of a mixture of passive and active elements that are c…

eess.SP2022★ 4 cited

Channel Estimation for Reconfigurable Intelligent Surface-Assisted Cell-Free Communications

Songjie Yang, Chenfei Xie, Mingwei Wang +1

Recent research has focused on reconfigurable intelligent surface (RIS)-assisted cell-free systems with the goal of enhancing coverage and lowering the cost of cell-free networks.…

eess.SP2022

Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems

Songjie Yang, Baojuan Liu, Zhiqin Hong +1

Due to the very narrow beam used in millimeter wave communication (mmWave), beam alignment (BA) is a critical issue. In this work, we investigate the issue of mmWave BA and present…

eess.SP2022★ 1 cited

Fast Compressive Channel Estimation for MmWave MIMO Hybrid Beamforming Systems

Songjie Yang, Chenfei Xie, Dongli Wang +1

Given the high degree of computational complexity of the channel estimation technique based on the conventional one-dimensional (1-D) compressive sensing (CS) framework employed in…