Year: 2024
Type: article
Abstract: This study examined the integrated benefits of 5G New Radio, network slicing, and reinforcement learning (RL) mechanisms in addressing the challenges associated with the increasing proliferation of intelligent objects in communication networks.This study proposed an innovative architecture that initially employed network slicing to efficiently segregate and manage various service types.Subsequently, this architecture was enhanced by applying RL to optimize the subchannel and power allocation strategies.This dual approach was proven through simulation studies conducted in a suburban setting, highlighting the effectiveness of the method for optimizing the use of available frequency bands.The results highlighted significant improvements in mitigating interference and adapting to the dynamic conditions of the network, thereby ensuring efficient dynamic resource allocation.Further, the application of an RL algorithm enabled the system to adjust resources adaptively based on real-time network conditions, thereby proving the flexibility and responsiveness of the scheme to changing network scenarios.
Institutions: Nahrain University, University of Baghdad
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403001224