VLDB 2026 Research / reviewers in the wild / expert
Liyuan Luo
dblp:372/8980
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer 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 · 60% Edge and fog computing · 20% Cellular and mobile networks · 20% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network optimization and economics › pricing
dynamic pricing |
0.8 | 1 | 2024 | Competitive Pricing for Resource Trading in Sliced Mobile Networks: A Multi-Agent Reinforcement Learning Approach · IEEE Trans. Mob. Comput. 2024 |
Cellular and mobile networks
network slicing |
0.8 | 1 | 2024 | Competitive Pricing for Resource Trading in Sliced Mobile Networks: A Multi-Agent Reinforcement Learning Approach · IEEE Trans. Mob. Comput. 2024 |
Network optimization and economics › pricing › game-theoretic pricing
price competition |
0.8 | 1 | 2024 | Competitive Pricing for Resource Trading in Sliced Mobile Networks: A Multi-Agent Reinforcement Learning Approach · IEEE Trans. Mob. Comput. 2024 |
Network optimization and economics
resource sharing |
0.8 | 1 | 2024 | Competitive Pricing for Resource Trading in Sliced Mobile Networks: A Multi-Agent Reinforcement Learning Approach · IEEE Trans. Mob. Comput. 2024 |
Edge and fog computing
resource trading |
0.8 | 1 | 2024 | Competitive Pricing for Resource Trading in Sliced Mobile Networks: A Multi-Agent Reinforcement Learning Approach · IEEE Trans. Mob. Comput. 2024 |
Methods — techniques the papers use, named apart from their topics
stackelberg game · 0.8multi-agent deep q-network · 0.8markov decision process · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Competitive Pricing for Resource Trading in Sliced Mobile Networks: A Multi-Agent Reinforcement Learning ApproachabstractThe emergence of network slicing as a flagship technology in 5G networks has not only enhanced network expansion and flexibility in resource management for service continuity, but also provided an avenue for establishing a viable market for resource sharing. To optimize the network's resource usage, stakeholders are encouraged to take pragmatic steps toward dynamic resource sharing. This paper designs a techno-economic model for the strategic interactions among multiple competing mobile virtual network operators (MVNOs) and their users in a trading marketplace. We formulate the dynamic pricing problem as a two-stage Stackelberg game, where the MVNOs are leaders, and the users are followers. In the first stage, the MVNOs compete to set their differentiated unit prices using a negotiation mechanism while considering system-level network load. Then, the users decide their purchasing volumes to match the prices of the MVNOs. We transform the game-based optimization problem into a stochastic Markov decision process (MDP) problem and propose a multi-agent deep Q-network (MADQN) method that obtains an optimal solution for the formulated game. Simulation results and analysis reveal that the proposed algorithm achieves convergence under the competitive pricing scheme (CPS) and independent pricing scheme (IPS) while enhancing MVNOs and users’ utilities at acceptable levels. Guolin Sun, Gordon Owusu Boateng, Liyuan Luo, Daniel Ayepah-Mensah, Guisong Liu |
IEEE Trans. Mob. Comput. | 3 |