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This study introduces multi-access edge computing (MEC) into millimeter-wave (mmWave) networks to overcome backhaul limitations. A novel content prefetching algorithm effectively resolves bottlenecks, achieving 95% system capacity.

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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.