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

Sensors (Basel, Switzerland)ยท2025
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Updated: Jan 9, 2026

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

Djamila Talbi1, Zoltan Gal1

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

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

Deep Q-Network Reinforcement Learning (DQN-RL) optimizes 6G mobile networks. Markov chain analysis shows 5-10 training episodes are sufficient for efficient policy convergence, enhancing performance and reducing energy use.

Keywords:
B5G/6GMarkov chaindeep Q-network reinforcement learningdynamic time warpinghigher-order Markov chainqueueing theoryretrial queueing systemspectral gap

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Area of Science:

  • Telecommunications Engineering
  • Artificial Intelligence
  • Queueing Theory

Background:

  • 6G mobile communication services face dynamic challenges in queueing systems.
  • Deep Q-Network Reinforcement Learning (DQN-RL) offers a potential solution for optimizing network behavior.
  • Understanding agent learning convergence is crucial for effective implementation.

Purpose of the Study:

  • To analyze the learning convergence of DQN-RL agents in 6G retrial queueing systems.
  • To quantify convergence characteristics using Markov chain methods and mixing time analysis.
  • To provide a foundation for optimizing 6G queueing strategies under uncertainty.

Main Methods:

  • Utilized first- and second-order Markov chain methods to analyze DQN-RL agent convergence.
  • Simulated temporal evolution of reward sequences as Markov chains.
  • Employed mixing time analysis and spectral gap properties of Markov models to assess convergence.

Main Results:

  • Markov chain analysis indicates 10 training episodes are sufficient for policy convergence in DQN-RL.
  • In some scenarios, as few as 5 episodes enhance mobile network performance with low energy consumption.
  • Mixing time calculations assessed learning stability and system responsiveness across 120 parameter combinations.

Conclusions:

  • DQN-RL convergence is sensitive to system parameters and retrial dynamics in 6G queueing.
  • Markov chain analysis provides a rigorous method for evaluating learning convergence.
  • The findings support optimizing 6G queueing strategies for enhanced efficiency and performance.