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Related Experiment Video

Updated: Sep 28, 2025

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5G Network Slicing: Methods to Support Blockchain and Reinforcement Learning.

Juan Hu1, Jianwei Wu2

  • 1School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China.

Computational Intelligence and Neuroscience
|April 4, 2022
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Summary

This study introduces a novel 5G network slicing method combining blockchain and reinforcement learning to enhance service efficiency. The proposed approach significantly reduces latency and improves reliability and resource utilization for diverse network services.

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Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Network Security

Background:

  • The 5G era presents challenges in network resource management due to increasing service demands and complexity.
  • Existing methods struggle to guarantee performance for all services across diverse applications beyond mobile phones and computers.
  • There is a need for advanced techniques to refine network offloading and support the development of 5G.

Purpose of the Study:

  • To propose and evaluate a 5G network slicing method integrating blockchain and reinforcement learning.
  • To enhance the efficiency, reliability, and resource utilization of 5G network services.
  • To reduce latency and improve Quality of Experience (QOE) for various applications.

Main Methods:

  • Implementation of 5G network slicing utilizing blockchain technology for security and distributed ledger capabilities.
  • Integration of reinforcement learning algorithms for dynamic resource allocation and decision-making within network slices.
  • Comparative analysis of the proposed method against traditional approaches through simulations and model testing.

Main Results:

  • The blockchain + reinforcement learning method demonstrated the lowest latency, achieving 15ms for 5G network slicing, significantly outperforming 4G, 3G, and 2G.
  • System reliability was highest with the proposed method (0.95), even as user numbers increased, due to minimized transmission delay.
  • Resource utilization exceeded 0.8 across all slices, with peak utilization reaching 1, and achieved the highest average receiving throughput (1450 kbps) and QOE (0.83).

Conclusions:

  • The integration of blockchain and reinforcement learning offers a superior solution for 5G network slicing.
  • This approach effectively addresses the challenges of latency, reliability, and resource management in complex 5G environments.
  • The proposed method significantly enhances network service efficiency and user experience, paving the way for advanced 5G applications.