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Related Experiment Video

Updated: Oct 13, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning.

Merkebu Girmay1, Vasilis Maglogiannis1, Dries Naudts1

  • 1IDLab, Department of Information Technology, IMEC, Ghent University, Technologiepark Zwijnaarde 15, B-9052 Ghent, Belgium.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

New Q-learning techniques improve Long-Term Evolution-Unlicensed (LTE-U) and WiFi coexistence in unlicensed spectrum. Reward selective Experience Replay (RER) offers faster convergence and enhanced throughput and fairness.

Keywords:
IEEE802.11LTE-UQ-learningcoexistenceexperience replay

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Area of Science:

  • Wireless communication networks
  • Spectrum sharing technologies
  • Machine learning applications in networking

Background:

  • Increasing demand for broadband applications necessitates efficient spectrum utilization.
  • Long-Term Evolution-Unlicensed (LTE-U) technology aims to offload traffic to unlicensed spectrum.
  • Coexistence of LTE-U and WiFi in unlicensed bands presents significant challenges due to interference.

Purpose of the Study:

  • To propose novel Q-learning based coexistence schemes for uncoordinated LTE-U and WiFi networks.
  • To address the complexity and overhead associated with centralized coordination methods.
  • To enhance the performance of LTE-U and WiFi coexistence using machine learning.

Main Methods:

  • Implementation of Experience Replay (ER) and Reward selective Experience Replay (RER) Q-learning algorithms.
  • Development of a WiFi saturation sensing model for LTE-U traffic demand estimation.
  • Performance comparison with existing rule-based and standard Q-learning coexistence schemes.

Main Results:

  • The Reward selective Experience Replay (RER) Q-learning scheme demonstrates faster convergence compared to the ER scheme.
  • RER Q-learning achieved a 19.1% and 5.2% enhancement in aggregated throughput over rule-based and Q-learning schemes, respectively.
  • RER Q-learning provided a 16.4% and 10.9% improvement in fairness compared to rule-based and Q-learning schemes, respectively.

Conclusions:

  • Proposed ER and RER Q-learning techniques offer effective solutions for uncoordinated LTE-U and WiFi coexistence.
  • The RER Q-learning scheme significantly outperforms existing methods in terms of convergence speed, throughput, and fairness.
  • Machine learning, particularly RER Q-learning, presents a promising approach for optimizing shared spectrum utilization.