Optimizing 5G network performance with dynamic resource allocation, robust encryption and Quality of Service (QoS)...

Area of Science:

• Telecommunications Engineering
• Network Security
• Data Traffic Management

Background:

• Global mobile data traffic is projected to increase significantly between 2024 and 2030, driven by high-bandwidth applications.
• Existing 5G network models face challenges in efficiently managing this surge in data and ensuring Quality of Service (QoS).

Purpose of the Study:

• To introduce a novel Maximum Capacity Model (MCM) designed to optimize resource allocation, encryption, and QoS in 5G networks.
• To address the growing demand for seamless high-bandwidth services like Voice over Internet Protocol (VoIP) and video streaming.

Main Methods:

• Development of a new Maximum Capacity Model (MCM) focusing on dynamic resource allocation.
• Implementation of prioritized traffic management and robust end-to-end encryption techniques, including header ciphering and payload parameter alteration.
• Comparative analysis of the MCM against conventional models.

Main Results:

• The MCM model demonstrated superior performance in key areas including QoS packet planning and scheduling, standard packet selection, and traffic management.
• Significant improvements were observed in maximum data rate and bandwidth utilization compared to traditional models.
• The encryption method ensures data confidentiality, with only sender and receiver possessing knowledge of the final outcome.

Conclusions:

• The proposed MCM effectively enhances 5G network efficiency and security.
• The model provides a robust solution for managing escalating mobile data traffic and supporting demanding applications.
• MCM offers a significant advancement over conventional models for 5G network performance optimization.