5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information

Area of Science:

• Wireless Communication
• Mobile Computing
• Network Engineering

Background:

• Increasing mobile traffic necessitates ultra-broadband solutions like millimeter-wave (mmWave) for 5G.
• Limited global optical fiber penetration creates backhaul capacity constraints for mmWave networks.
• Multi-access edge computing (MEC) offers a solution to reduce latency by deploying resources closer to users.

Purpose of the Study:

• To address the backhaul bottleneck issue in mmWave heterogeneous networks.
• To propose a content prefetching algorithm integrated with MEC for enhanced data delivery.
• To improve the performance of mmWave access under limited backhaul capacity.

Main Methods:

• Integration of MEC into mmWave heterogeneous networks.
• Development of a context-aware content prefetching algorithm utilizing user information (destination, mobility, traffic tendency).
• Computer simulations employing realistic user mobility, traffic, and backhauling models for validation.

Main Results:

• The proposed framework effectively resolves backhaul bottlenecks in mmWave networks.
• The content prefetching algorithm successfully leverages MEC for data storage and delivery.
• Simulations demonstrated that the system achieved 95% of its potential capacity despite a 1 Gbps backhaul link constraint.

Conclusions:

• MEC integration and content prefetching are effective strategies for overcoming backhaul limitations in mmWave networks.
• The proposed approach enables mmWave technology to achieve its full potential even with constrained backhaul.
• This framework offers a viable solution for enhancing 5G cellular network performance and user experience.