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Yingsong Li
Researcher at Harbin Engineering University
Publications - 382
Citations - 5128
Yingsong Li is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Antenna (radio) & Antenna measurement. The author has an hindex of 33, co-authored 342 publications receiving 3357 citations. Previous affiliations of Yingsong Li include Chinese Academy of Sciences & Kochi University of Technology.
Papers
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Dual-Band Metasurface-Based Decoupling Method for Two Closely Packed Dual-Band Antennas
TL;DR: In this article, a metasurface-based decoupling method was proposed to reduce the mutual couplings at two independent bands of two coupled multiple-input-multiple-output (MIMO) antennas.
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Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation
TL;DR: The proposed RNA-LMS/F algorithm exhibits an improved performance in terms of the convergence speed and the steady-state error, which can provide a zero attractor to further exploit the sparsity of the channel by the use of the norm adaption penalty and the reweighting factor.
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Dual-Band Eight-Antenna Array Design for MIMO Applications in 5G Mobile Terminals
TL;DR: The proposed dual-band eight-antenna array for multiple-input and multiple-output (MIMO) applications in 5G mobile terminals can maintain acceptable radiation and MIMO performance in the presence of specific anthropomorphic mannequin head and human hands.
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A Reconfigurable Triple-Notch-Band Antenna Integrated with Defected Microstrip Structure Band-Stop Filter for Ultra-Wideband Cognitive Radio Applications
Yingsong Li,Wenxing Li,Qiubo Ye +2 more
TL;DR: In this article, a printed reconfigurable ultra-wideband (UWB) monopole antenna with triple narrow band-notched characteristics is proposed for cognitive radio applications, which can work at eight modes by controlling switches ON and OFF.
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Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation
TL;DR: The simulation results obtained from sparse channel estimation and echo cancelation demonstrate that the proposed sparse SM-NLMS algorithms are superior to the previously proposed NLMS, SM- NLMS as well as zero-attracting NLMS (ZA-NL MS) algorithms.