Demo Data from EDBT 2026 / reviewers in the wild / expert
Jing Cheng Liang
dblp:329/5784
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0002-2653-5113ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
2 papers |
Physical-layer communications · 75% Network performance modeling · 25% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Energy-efficient computing · 50% Reconfigurable computing and FPGAs · 50% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications
reconfigurable intelligent surface |
1.3 | 2 | 2024 | Reconfigurable Intelligent Surface: Power Consumption Modeling and Practical Measurement Validation · IEEE Trans. Commun. 2024 Reconfigurable Intelligent Surfaces: Simplified-Architecture Transmitters - From Theory to Implementations · Proc. IEEE 2022 |
Network performance modeling
power consumption modeling |
0.8 | 1 | 2024 | Reconfigurable Intelligent Surface: Power Consumption Modeling and Practical Measurement Validation · IEEE Trans. Commun. 2024 |
Physical-layer communications
wave manipulation |
0.6 | 1 | 2022 | Reconfigurable Intelligent Surfaces: Simplified-Architecture Transmitters - From Theory to Implementations · Proc. IEEE 2022 |
Energy-efficient computing
low-power design |
0.2 | 1 | 2024 | Reconfigurable Intelligent Surface: Power Consumption Modeling and Practical Measurement Validation · IEEE Trans. Commun. 2024 |
Reconfigurable computing and FPGAs
reconfigurable intelligent surface |
0.2 | 1 | 2024 | Reconfigurable Intelligent Surface: Power Consumption Modeling and Practical Measurement Validation · IEEE Trans. Commun. 2024 |
Physical-layer communications
modulation and signal design |
0.2 | 1 | 2022 | Reconfigurable Intelligent Surfaces: Simplified-Architecture Transmitters - From Theory to Implementations · Proc. IEEE 2022 |
Physical-layer communications › MIMO
space-time modulation |
0.2 | 1 | 2022 | Reconfigurable Intelligent Surfaces: Simplified-Architecture Transmitters - From Theory to Implementations · Proc. IEEE 2022 |
Methods — techniques the papers use, named apart from their topics
measurement validation · 1.5wavefront shaping · 0.6metasurface design · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Reconfigurable Intelligent Surface: Power Consumption Modeling and Practical Measurement ValidationabstractThe reconfigurable intelligent surface (RIS) has received a lot of interest because of its capacity to reconfigure the wireless communication environment in a cost- and energy-efficient way. However, the realistic power consumption modeling and measurement validation of RIS has received far too little attention. Therefore, in this work, we model the power consumption of RIS and conduct measurement validations using various RISs to fill this vacancy. Firstly, we propose a practical power consumption model of RIS. The RIS hardware is divided into three basic parts: the FPGA control board, the drive circuits, and the RIS unit cells. The power consumption of the first two parts is modeled asPstaticand that of the last part is modeled asPunits. Expressions ofPstaticandPunitsvary amongst different types of RISs. Secondly, we conduct measurements on various RISs to validate the proposed model. Five different RISs including the PIN diode, varactor diode, and RF switch types are measured, and measurement results validate the generality and applicability of the proposed power consumption model of RIS. Finally, we summarize the measurement results and discuss the approaches to achieve the low-power-consumption design of RIS-assisted wireless communication systems. Jinghe Wang, Wankai Tang, Jing Cheng Liang, Lei Zhang 0184, Jun Yan Dai 0001, Xiao Li 0001, Shi Jin 0002, Qiang Cheng 0002, Tiejun Cui |
IEEE Trans. Commun. | 3 |
| 2022 | Reconfigurable Intelligent Surfaces: Simplified-Architecture Transmitters - From Theory to ImplementationsabstractReconfigurable intelligent surfaces (RISs) offer an entirely new route to alter the propagation properties of electromagnetic waves and thus control their reflection, refraction, and scattering features in arbitrary manners. Such physical attributes are perceived to bring about fundamental influence on the modern wireless communication system due to the possibilities to establish artificial and controllable propagation environments for radio signals, which no longer rely on the complex encoding, decoding, and other signal processing techniques. Recent studies reveal that the wave manipulation is not the only skill of the RISs. With the rapid developments of space–time digital metasurface and information metasurface, there has been increasing attention focused on the information manipulation via these artificial surfaces. In this article, we provide an overview of the theoretical models of the space–time digital metasurface and information metasurface, the mechanisms of wavefront shaping, and the signal modulations in space and time domains during the wave–matter interactions. We will also address some practical issues during implementations of the reconfigurable intelligent metasurfaces and the associated hardware architectures at microwave frequencies to realize simplified radio frequency transmitters. Several modulation schemes and the corresponding demonstration systems are introduced to illustrate the powerful abilities of the reconfigurable intelligent metasurfaces. Potential research directions of this technique are briefly discussed for their potential applications in future wireless networks. Qiang Cheng 0002, Lei Zhang 0184, Jun Yan Dai 0001, Wankai Tang, Jun Chen Ke, Jing Cheng Liang, Shi Jin 0002, Tiejun Cui |
Proc. IEEE | 7 |