A Comparative Study of Network Slicing Techniques for Effective Utilization of Channel for 5G and Beyond 5G Networks

Abstract

Radio Resource is a finite and crucial resource for any wireless communication. We have huge expectations from the 5G and beyond 5G wireless communication networks. If we want to meet this with huge expectations of Next Generation Wireless Network (NGWN) without compromising the Quality of Service (QoS), then we must have the Radio Resource Management (RRM) techniques for effective utilization of available and finite Radio Resource. The NGWN will be experiencing large number of connected devices which needs huge and on-demand bandwidth allocation without compromising the QoS. This on-demand bandwidth allocation is implemented using Network Slicing (NS) techniques to meet the requirements of the device-level service and application-level service. The objective of this work is to investigate various NS techniques for enhanced radio resource utilization in NGWN. It focuses on addressing the challenges of dynamic bandwidth allocation by employing advanced decision-making mechanisms to fulfill the ever-changing demands of next-generation applications. The study explores and compares static and dynamic NS techniques for application-level service provisioning. Dynamic NS is analyzed with an emphasis on integrating soft computing methods to handle decision-making processes for on-demand bandwidth allocation. Comparative simulations and evaluations of these techniques are conducted to assess their efficiency and adaptability. The major challenge in dynamic NS is making critical and right decisions to meet the dynamic requirements of NGWN applications. This challenge is addressed using the soft computing techniques in making decisions to cater to the dynamic on demand requirements of the NGWN applications. The key findings of this research work are to analyze the dynamic NS, when integrated with soft computing techniques and offers superior adaptability and decision-making capabilities, enabling effective bandwidth allocation for NGWN applications. The results demonstrate that these methods enhance channel utilization and cater to diverse service requirements, making them critical for the successful deployment of NGWN. This study provides insights into embedding robust NS techniques into NGWN to achieve optimal radio resource utilization while addressing the dynamic needs of next-generation wireless communication systems.

Keywords

• Joint radio resource management
• network slicing
• soft computing techniques
• next generation wireless networks
• application-level
• resource allocation