Demo Data from EDBT 2026 / reviewers in the wild / expert
Chuting Lin
dblp:215/7827
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Computer networks · 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
1 paper |
Network optimization and economics · 77% Physical-layer communications · 23% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network optimization and economics › energy efficiency optimization
energy efficiency maximization |
0.6 | 1 | 2022 | Energy Efficiency Optimization for PSOAM Mode-Groups Based MIMO-NOMA Systems · IEEE Trans. Commun. 2022 |
Network optimization and economics
resource allocation |
0.6 | 1 | 2022 | Energy Efficiency Optimization for PSOAM Mode-Groups Based MIMO-NOMA Systems · IEEE Trans. Commun. 2022 |
Physical-layer communications › multiple access › non-orthogonal multiple access
MIMO-NOMA |
0.2 | 1 | 2022 | Energy Efficiency Optimization for PSOAM Mode-Groups Based MIMO-NOMA Systems · IEEE Trans. Commun. 2022 |
Physical-layer communications
orbital angular momentum |
0.2 | 1 | 2022 | Energy Efficiency Optimization for PSOAM Mode-Groups Based MIMO-NOMA Systems · IEEE Trans. Commun. 2022 |
Methods — techniques the papers use, named apart from their topics
dual layer optimization · 0.6deep belief network · 0.6bisection method · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Ensemble Learning Prediction Based on Comprehensive Factors for Portfolio Optimization
Chuting Lin, Yumeng Qian, Hanlun Wu, Yongxuan Lai |
ICONIP (6) | 1 |
| 2022 | Energy Efficiency Optimization for PSOAM Mode-Groups Based MIMO-NOMA SystemsabstractPlane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE. Jie Tang 0002, Chuting Lin, Wanmei Feng, Zhen Chen 0010, Daniel K. C. So, Kai-Kit Wong |
IEEE Trans. Commun. | 3 |