Performance Investigation of Small-World Networks for 5G Mobile Communication Networks

• Published in: 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) pp. 1 - 5
• Main Authors: Srinivasan, Thiruvenkadam, Ra, In-Ho
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05.01.2023
• Subjects: 5G mobile communication; 5G network; Computational modeling; Graph neural networks; Measurement; Network slicing; Reinforcement learning; Resource management; scale-free network; small-world network
• DOI: 10.1109/ICAECT57570.2023.10117958

5G mobile networks require network slicing of physical infrastructure to change how service providers deliver and defend services. For node allocation and connection establishment, a fast and secure slicing technique is used, which requires many domain applications across the network. A large number of studies used a scale-free network (SFN) model for the core network, then used network slicing to allocate resources to various applications. The feasibility of using a small-world network (SWN) as an alternative to a scale-free network is investigated in this work. The service request network and the core network have both been modeled as small-world networks, and performance measurement has been carried out with regard to the allocation of resources. Performance metrics such as service revenue and acceptance ratio are used to assess the effectiveness of the proposed approach. The small-world network's performance advantage is compared to that of the scale-free network under different network settings.