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Chuting Lin

dblp:215/7827 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Network optimization and economics › energy efficiency optimization
energy efficiency maximization
0.612022
Energy Efficiency Optimization for PSOAM Mode-Groups Based MIMO-NOMA Systems · IEEE Trans. Commun. 2022
Network optimization and economics
resource allocation
0.612022
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
0.212022
Energy Efficiency Optimization for PSOAM Mode-Groups Based MIMO-NOMA Systems · IEEE Trans. Commun. 2022
Physical-layer communications
orbital angular momentum
0.212022
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
YearPublicationVenuePosition
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 Systems
abstract
Plane 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