Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Genya Ishigaki
Second Advisor
Fabio Di Troia
Third Advisor
William Andreopoulos
Keywords
Elastic Network Slicing, Dynamic Pricing, Revenue Maximization, Deep Reinforcement Learning
Abstract
Network slicing is a concept that enables a diverse array of network applications with lots of unique service requirements. For the scope of this research, we delve into elastic network slicing, which has a potential benefit for both the providers and users through cost-effective resource utilization. Dynamic pricing of these slice resources is a method for operators to realize the balance of different types of network slices by implicitly communicating the current network state to slice users. We have designed a custom pricing scheme using Deep Reinforcement Learning for elastic network slices that maximizes the revenue of slice providers while meeting the users’ slice service requirements. Our experiment results indicate that a balanced distribution of different slice types, which is realized by our dynamic pricing, increases the total revenue of a slice provider without violating the service level agreement with slice users.
Recommended Citation
Shah, Naisheel, "Dynamic Pricing for Revenue Maximization in 5G Networks with Elastic Network Slicing and Reinforcement Learning" (2025). Master's Projects. 1543.
DOI: https://doi.org/10.31979/etd.65ft-w5v7
https://scholarworks.sjsu.edu/etd_projects/1543
