Research on Prediction and Optimization Strategy of University Wi-Fi Network Access Quality Based on Machine Learning

Mingjun Lu

doi:10.23977/acss.2026.100106

Research on Prediction and Optimization Strategy of University Wi-Fi Network Access Quality Based on Machine Learning

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DOI: 10.23977/acss.2026.100106 | Downloads: 1 | Views: 42

Author(s)

Mingjun Lu ¹

Affiliation(s)

¹ School of Digital Technology, Guangxi University of Foreign Languages, Nanning, Guangxi, 530200, China

Corresponding Author

Mingjun Lu

ABSTRACT

University Wi-Fi networks are characterized by high user density, diverse service types, and uneven spatiotemporal distribution of access behaviors. Traditional threshold-based and experience-driven network operation methods struggle to achieve precise prediction and dynamic optimization of access quality. This study collects multi-dimensional data from a university campus Wi-Fi network, employing a hybrid machine learning prediction method combining XGBoost and LSTM to accurately forecast QoE metrics including Wi-Fi access rate, latency, packet loss rate, and connection success rates. Additionally, a closed-loop optimization scheme of "prediction-scheduling-evaluation" is proposed, encompassing AP power adaptation, automatic channel configuration, load balancing, and service tiered scheduling. Experimental results demonstrate that the proposed prediction model achieves an average MAE below 5%. The optimization strategy enhances average network access rate by 28.3%, reduces latency by 34.7%, and improves connection success rates to over 99.5%, providing a practical solution for intelligent operation and quality improvement of university Wi-Fi networks.

KEYWORDS

university Wi-Fi; access quality prediction; machine learning

CITE THIS PAPER

Mingjun Lu. Research on Prediction and Optimization Strategy of University Wi-Fi Network Access Quality Based on Machine Learning. Advances in Computer, Signals and Systems (2026). Vol. 10, No. 1, 47-53. DOI: http://dx.doi.org/10.23977/acss.2026.100106.

REFERENCES

[1] Lu Qingsong, Qiu Yinghui. Vertical handover algorithm for heterogeneous wireless networks based on fuzzy neural network [J]. Computer Engineering and Science, 2025,47(12):2160-2168.
[2] Lü Qifen, Huang Zhaowen, Gao Yutian, et al. Development and Large-Scale Application of an Integrated 5G Smart Cybersecurity Service Platform [J]. Communication Enterprise Management, 2025, (11):57-61.
[3] Li Yanfei, Fan Lingmeng, Wu Xinqiao, et al. Research on adaptive selection methods for power wireless networks in heterogeneous networks [J]. Foreign Electronic Measurement Technology, 2025,44(09):100-106.

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Research on Prediction and Optimization Strategy of University Wi-Fi Network Access Quality Based on Machine Learning

Author(s)

Affiliation(s)

Corresponding Author

ABSTRACT

KEYWORDS

CITE THIS PAPER

REFERENCES

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