.png){kind=icon}
.png){kind=icon}
Return
MultiAgentNetSim: Empowering Next-Generation Network Modeling with Multi-Agent Simulation
.png){kind=icon}
PRE
AI
DOI：10.1109/MCOM.002.2400363.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The increasing complexity of next-generation networks necessitates advanced simulation techniques, with multi-agent simulation (MAS) emerging as a highly effective solution. MAS enables a thorough analysis of micro-level interactions and intricate interdependencies inherent in modern networks, along with their implications -- complexities that traditional simulation methods are increasingly unable to address effectively. Motivated by this potential, MultiAgentNetSim is proposed, founded on the principles of MAS. A key feature of MultiAgentNetSim is its capacity to facilitate realistic simulations of complex network scenarios, with network slicing serving as a prominent example explored within this article. Beyond serving as a simulation environment, MultiAgentNetSim functions as a decision-support tool, providing dynamic inputs for algorithm training, and a robust framework for evaluating algorithmic performance. A notable example is its integration with a dynamic pricing algorithm within the network slicing scenario. The simulation inputs deliver expressive and realistic data, enabling a more informed pricing strategy. This strategy, explored through the metric of operator profit, can be assessed across multiple metrics and continuously optimized using feedback from the platform, making it an invaluable asset in modern network management.
Keywords:
5G mobile communication
Data models
Analytical models
Pricing
Complexity theory
Vehicle dynamics
Load modeling
Heuristic algorithms
Next generation networking
Resource management
Journal
IF:
8.2
Papers:
6.9K
Citations:
2.2W