A Cost-Efficient 5G Non-Public Network Architectural Approach: Key Concepts and Enablers, Building Blocks and...

Area of Science:

• Telecommunications Engineering
• Computer Networking
• Wireless Communication Systems

Background:

• The demand for high data rates and low latency in mobile services fuels the evolution to fifth-generation (5G) networks.
• Private companies are increasingly interested in deploying 5G non-public networks (NPNs) for production monitoring and critical applications.
• Existing network designs often lack the dynamic adaptability required for diverse and evolving service demands.

Purpose of the Study:

• To introduce a novel 5G NPN architectural approach tailored for private enterprise needs.
• To integrate key enabling technologies like cell densification, disaggregated RAN, edge computing, and AI/ML.
• To analyze the applicability and scalability of the proposed architecture in real-world industrial scenarios.

Main Methods:

• Adoption of a dynamic network design approach with topology configured based on service demands.
• Integration of enabling technologies: cell densification, disaggregated Radio Access Network (RAN) with open interfaces, edge computing.
• Application of Artificial Intelligence/Machine Learning (AI/ML) for network optimization.

Main Results:

• The proposed 5G NPN architecture effectively supports critical applications and advanced analytics like computer vision.
• Demonstrated feasibility of integrating diverse technologies for enhanced network performance.
• Analysis of scalability challenges and a framework for Industry 4.0 deployment.

Conclusions:

• The presented 5G NPN architecture offers a flexible and robust solution for private networks.
• The dynamic and AI/ML-driven approach enhances network efficiency and supports demanding applications.
• The architecture is scalable and applicable to industrial settings like smart manufacturing.