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Higher-Order Markov Model-Based Analysis of Reinforcement Learning in 6G Mobile Retrial Queueing Systems.

Sensors (Basel, Switzerland)ยท2025
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Updated: Sep 18, 2025

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Integrating Reinforcement Learning into M/M/1/K Retry Queueing Models for 6G Applications.

Djamila Talbi1, Zoltan Gal1

  • 1Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary

This study introduces a Deep Q-Networks (DQN) approach for intelligent queue management in 6G networks. The reinforcement learning model enhances resource allocation and service for mobile terminals, improving efficiency and fairness.

Keywords:
6Gdeep Q-networkhigh speed networksqueueing systemreinforcement learningsingular value decompositionterahertz frequency

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Area of Science:

  • Wireless Communication
  • Artificial Intelligence
  • Network Engineering

Background:

  • Next-generation wireless networks, particularly 6G operating at Terahertz frequencies, face challenges in sustainable, efficient, and fair resource allocation.
  • Existing queue management systems struggle to adapt to dynamic traffic conditions and high-speed demands.

Purpose of the Study:

  • To develop and evaluate an intelligent queue management system for 6G networks using reinforcement learning.
  • To minimize network delays, reduce energy consumption, and ensure equitable resource access.

Main Methods:

  • Integration of Deep Q-Networks (DQN), a reinforcement learning algorithm, into a 6G Retrial Queueing System (RQS).
  • Extensive simulations were conducted to analyze performance under varying arrival rates, queue sizes, and reward scaling factors.
  • Singular Value Decomposition (SVD) was used to analyze the agent's learning process and adaptation.

Main Results:

  • The proposed 6G-RQS model with DQN significantly enhances queue management effectiveness.
  • The system demonstrated improved performance by increasing the number of served mobile terminals, even under high traffic demands.
  • SVD analysis confirmed effective learning and adaptation by the reinforcement learning agent.

Conclusions:

  • Reinforcement learning-based queue management is a viable and promising solution for addressing 6G network challenges.
  • The intelligent 6G-RQS offers a dynamic and adaptive approach to optimize performance in high-speed communication networks.
  • This research paves the way for more efficient and equitable wireless network resource allocation.