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A model for base station network traffic prediction using an enhanced random ensemble-based mixed kernel K-nearest neighbor algorithm
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Ning SUN1, 2, Zhuoxuan LI3, Xinli SHI1, 3, *, Peichong SUN4, Mingjie XU1, 5, Jinde CAO3
Journal of National Niversity of Defense Technology | 2025, 47(6) : 24 - 35
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Journal of National Niversity of Defense Technology | 2025, 47(6): 24-35
• Computer System and technology •
A model for base station network traffic prediction using an enhanced random ensemble-based mixed kernel K-nearest neighbor algorithm
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Ning SUN1, 2, Zhuoxuan LI3, Xinli SHI1, 3, *, Peichong SUN4, Mingjie XU1, 5, Jinde CAO3
Affiliations
  • 1.School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
  • 2.China United Network Communications Corporation Guangzhou Branch, Guangzhou 510630, China
  • 3.School of Mathematics, Southeast University, Nanjing 211189, China
  • 4.Information and Network Engineering Research Center, South China University of Technology, Guangzhou 510641, China
  • 5.NARI Technology Co., Ltd., Nanjing 211106, China
Published: 2025-12-28 doi: 10.11887/j.issn.1001-2486.25060006
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An ER-MKKNN(enhanced random mixed kernel K-nearest neighbors algorithm)was developed to meet the requirements of base station network traffic prediction in ultra-dense 5 G/6G environments.A hybrid kernel function was formed by combining a radial basis function kernel with a white-noise kernel, thereby overcoming the trade-off between nonlinear relationship modeling and noise suppression that plagues single-kernel methods.Dual random subsampling of both samples and features, together with a randomized hyperparameter-interval strategy, was employed to bolster generalization stability in high-dimensional, sparse settings.A dynamic weight-allocation mechanism based on inversion of out-of-bag errors was introduced to improve robustness against abrupt traffic fluctuations.Finally, a multi-level parallel architecture was implemented to deliver a scalable prediction framework for ultra-dense network topologies.Experimental evaluations show that ER-MKKNN outperformed deep-learning models in root mean square error, mean absolute percentage error and mean absolute error, respectively, establishing a new technical pathway for intelligent network operations and maintenance.

base station network traffic prediction  /  mixed kernel K-nearest neighbor algorithm  /  enhanced random integration  /  multi-layer parallel architecture
Ning SUN, Zhuoxuan LI, Xinli SHI, Peichong SUN, Mingjie XU, Jinde CAO. A model for base station network traffic prediction using an enhanced random ensemble-based mixed kernel K-nearest neighbor algorithm[J]. Journal of National Niversity of Defense Technology, 2025 , 47 (6) : 24 -35 . DOI: 10.11887/j.issn.1001-2486.25060006
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doi: 10.11887/j.issn.1001-2486.25060006
  • Receive Date：2025-06-05
  • Online Date：2026-04-16
  • Published：2025-12-28
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  • Received：2025-06-05
Affiliations
    1.School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
    2.China United Network Communications Corporation Guangzhou Branch, Guangzhou 510630, China
    3.School of Mathematics, Southeast University, Nanjing 211189, China
    4.Information and Network Engineering Research Center, South China University of Technology, Guangzhou 510641, China
    5.NARI Technology Co., Ltd., Nanjing 211106, China
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