Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review

Fumlack Kingsley George; Jean Emmanuel Ntsama

doi:doi:10.11648/j.ajnc.20261502.12

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Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review

Fumlack Kingsley George^*

, Jean Emmanuel Ntsama

Published in American Journal of Networks and Communications (Volume 15, Issue 2)

Received: 27 August 2026 Accepted: 10 September 2026 Published: 28 September 2026

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Abstract

The rapid expansion of Wireless Local Area Networks (WLANs) has introduced significant performance challenges, particularly due to the increasing number of mobile and connected devices. Traditional static network management techniques are inadequate for handling the dynamic and complex nature of modern WLAN environments, often resulting in latency, congestion, and interference. This study systematically examines the potential of machine learning (ML) approaches, including advanced algorithms such as Q-learning and Support Vector Machines (SVM), for WLAN performance optimisation. By enabling predictive traffic analysis, adaptive configuration, and intelligent resource allocation, ML techniques offer opportunities to enhance throughput and minimise delay. The study aims to: (1) identify and categorise ML algorithms addressing key WLAN challenges such as latency reduction, interference mitigation, and load balancing; (2) analyze the performance metrics used across studies using standardised formulations; (3) evaluate the generalisability of simulation results to real-world deployments; and (4) identify computational, scalability, and dataset limitations affecting real-time implementation. Despite promising laboratory results, challenges persist due to the scarcity of large, high-quality, real-world datasets required for robust training. The paper highlights the critical role of data efficiency and advocates for open-source WLAN datasets and methods such as transfer learning and few-shot learning to reduce data dependence. Ultimately, this study emphasises that overcoming data constraints is key to realising adaptive, real-time, and scalable ML-driven WLAN optimisation for future wireless communication systems.

Published in	American Journal of Networks and Communications (Volume 15, Issue 2)
DOI	10.11648/j.ajnc.20261502.12
Page(s)	49-61
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Wireless Local Area Networks, Machine Learning, Network Optimization, Latency Reduction, Interference Mitigation, Transfer Learning

References

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APA Style

George, F. K., Ntsama, J. E. (2026). Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review. American Journal of Networks and Communications, 15(2), 49-61. https://doi.org/10.11648/j.ajnc.20261502.12

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ACS Style

George, F. K.; Ntsama, J. E. Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review. Am. J. Netw. Commun. 2026, 15(2), 49-61. doi: 10.11648/j.ajnc.20261502.12

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AMA Style

George FK, Ntsama JE. Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review. Am J Netw Commun. 2026;15(2):49-61. doi: 10.11648/j.ajnc.20261502.12

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@article{10.11648/j.ajnc.20261502.12,
  author = {Fumlack Kingsley George and Jean Emmanuel Ntsama},
  title = {Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review},
  journal = {American Journal of Networks and Communications},
  volume = {15},
  number = {2},
  pages = {49-61},
  doi = {10.11648/j.ajnc.20261502.12},
  url = {https://doi.org/10.11648/j.ajnc.20261502.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20261502.12},
  abstract = {The rapid expansion of Wireless Local Area Networks (WLANs) has introduced significant performance challenges, particularly due to the increasing number of mobile and connected devices. Traditional static network management techniques are inadequate for handling the dynamic and complex nature of modern WLAN environments, often resulting in latency, congestion, and interference. This study systematically examines the potential of machine learning (ML) approaches, including advanced algorithms such as Q-learning and Support Vector Machines (SVM), for WLAN performance optimisation. By enabling predictive traffic analysis, adaptive configuration, and intelligent resource allocation, ML techniques offer opportunities to enhance throughput and minimise delay. The study aims to: (1) identify and categorise ML algorithms addressing key WLAN challenges such as latency reduction, interference mitigation, and load balancing; (2) analyze the performance metrics used across studies using standardised formulations; (3) evaluate the generalisability of simulation results to real-world deployments; and (4) identify computational, scalability, and dataset limitations affecting real-time implementation. Despite promising laboratory results, challenges persist due to the scarcity of large, high-quality, real-world datasets required for robust training. The paper highlights the critical role of data efficiency and advocates for open-source WLAN datasets and methods such as transfer learning and few-shot learning to reduce data dependence. Ultimately, this study emphasises that overcoming data constraints is key to realising adaptive, real-time, and scalable ML-driven WLAN optimisation for future wireless communication systems.},
 year = {2026}
}

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TY  - JOUR
T1  - Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review
AU  - Fumlack Kingsley George
AU  - Jean Emmanuel Ntsama
Y1  - 2026/09/28
PY  - 2026
N1  - https://doi.org/10.11648/j.ajnc.20261502.12
DO  - 10.11648/j.ajnc.20261502.12
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 49
EP  - 61
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20261502.12
AB  - The rapid expansion of Wireless Local Area Networks (WLANs) has introduced significant performance challenges, particularly due to the increasing number of mobile and connected devices. Traditional static network management techniques are inadequate for handling the dynamic and complex nature of modern WLAN environments, often resulting in latency, congestion, and interference. This study systematically examines the potential of machine learning (ML) approaches, including advanced algorithms such as Q-learning and Support Vector Machines (SVM), for WLAN performance optimisation. By enabling predictive traffic analysis, adaptive configuration, and intelligent resource allocation, ML techniques offer opportunities to enhance throughput and minimise delay. The study aims to: (1) identify and categorise ML algorithms addressing key WLAN challenges such as latency reduction, interference mitigation, and load balancing; (2) analyze the performance metrics used across studies using standardised formulations; (3) evaluate the generalisability of simulation results to real-world deployments; and (4) identify computational, scalability, and dataset limitations affecting real-time implementation. Despite promising laboratory results, challenges persist due to the scarcity of large, high-quality, real-world datasets required for robust training. The paper highlights the critical role of data efficiency and advocates for open-source WLAN datasets and methods such as transfer learning and few-shot learning to reduce data dependence. Ultimately, this study emphasises that overcoming data constraints is key to realising adaptive, real-time, and scalable ML-driven WLAN optimisation for future wireless communication systems.
VL  - 15
IS  - 2
ER  -

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Author Information

Fumlack Kingsley George

Computer Science Department Yola, Modibbo Adama University of Technology, Yola, Nigeria

Contact Email

http://orcid.org/0009-0004-3946-2322
Jean Emmanuel Ntsama

Computer Science Department Yola, Modibbo Adama University of Technology, Yola, Nigeria

Contact Email

http://orcid.org/0000-0002-8348-9491

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APA Style

George, F. K., Ntsama, J. E. (2026). Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review. American Journal of Networks and Communications, 15(2), 49-61. https://doi.org/10.11648/j.ajnc.20261502.12

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ACS Style

George, F. K.; Ntsama, J. E. Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review. Am. J. Netw. Commun. 2026, 15(2), 49-61. doi: 10.11648/j.ajnc.20261502.12

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AMA Style

George FK, Ntsama JE. Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review. Am J Netw Commun. 2026;15(2):49-61. doi: 10.11648/j.ajnc.20261502.12

Copy | Download

@article{10.11648/j.ajnc.20261502.12,
  author = {Fumlack Kingsley George and Jean Emmanuel Ntsama},
  title = {Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review},
  journal = {American Journal of Networks and Communications},
  volume = {15},
  number = {2},
  pages = {49-61},
  doi = {10.11648/j.ajnc.20261502.12},
  url = {https://doi.org/10.11648/j.ajnc.20261502.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20261502.12},
  abstract = {The rapid expansion of Wireless Local Area Networks (WLANs) has introduced significant performance challenges, particularly due to the increasing number of mobile and connected devices. Traditional static network management techniques are inadequate for handling the dynamic and complex nature of modern WLAN environments, often resulting in latency, congestion, and interference. This study systematically examines the potential of machine learning (ML) approaches, including advanced algorithms such as Q-learning and Support Vector Machines (SVM), for WLAN performance optimisation. By enabling predictive traffic analysis, adaptive configuration, and intelligent resource allocation, ML techniques offer opportunities to enhance throughput and minimise delay. The study aims to: (1) identify and categorise ML algorithms addressing key WLAN challenges such as latency reduction, interference mitigation, and load balancing; (2) analyze the performance metrics used across studies using standardised formulations; (3) evaluate the generalisability of simulation results to real-world deployments; and (4) identify computational, scalability, and dataset limitations affecting real-time implementation. Despite promising laboratory results, challenges persist due to the scarcity of large, high-quality, real-world datasets required for robust training. The paper highlights the critical role of data efficiency and advocates for open-source WLAN datasets and methods such as transfer learning and few-shot learning to reduce data dependence. Ultimately, this study emphasises that overcoming data constraints is key to realising adaptive, real-time, and scalable ML-driven WLAN optimisation for future wireless communication systems.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review
AU  - Fumlack Kingsley George
AU  - Jean Emmanuel Ntsama
Y1  - 2026/09/28
PY  - 2026
N1  - https://doi.org/10.11648/j.ajnc.20261502.12
DO  - 10.11648/j.ajnc.20261502.12
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 49
EP  - 61
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20261502.12
AB  - The rapid expansion of Wireless Local Area Networks (WLANs) has introduced significant performance challenges, particularly due to the increasing number of mobile and connected devices. Traditional static network management techniques are inadequate for handling the dynamic and complex nature of modern WLAN environments, often resulting in latency, congestion, and interference. This study systematically examines the potential of machine learning (ML) approaches, including advanced algorithms such as Q-learning and Support Vector Machines (SVM), for WLAN performance optimisation. By enabling predictive traffic analysis, adaptive configuration, and intelligent resource allocation, ML techniques offer opportunities to enhance throughput and minimise delay. The study aims to: (1) identify and categorise ML algorithms addressing key WLAN challenges such as latency reduction, interference mitigation, and load balancing; (2) analyze the performance metrics used across studies using standardised formulations; (3) evaluate the generalisability of simulation results to real-world deployments; and (4) identify computational, scalability, and dataset limitations affecting real-time implementation. Despite promising laboratory results, challenges persist due to the scarcity of large, high-quality, real-world datasets required for robust training. The paper highlights the critical role of data efficiency and advocates for open-source WLAN datasets and methods such as transfer learning and few-shot learning to reduce data dependence. Ultimately, this study emphasises that overcoming data constraints is key to realising adaptive, real-time, and scalable ML-driven WLAN optimisation for future wireless communication systems.
VL  - 15
IS  - 2
ER  -

Copy | Download