2025/12/2 0:00:00 YANG Hui, DONG Dezun, XUN Peng, LIU Rulin, LI Junnan, TANG Zhu, LYU Gaofeng, QUAN Wei, ZHONG Jincheng, LI Tao 7true 2025/12/2 0:00:00 XU Jinbo, DONG Dezun, LI Baofeng, ZHANG Wei, XING Jianying, ZHANG Peng 6true K nearest neighbors algorithm) was developed to meet the requirements of base station network traffic prediction in ultra-dense 5G/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.]]> 2025/12/2 0:00:00 SUN Ning, LI Zhuoxuan, SHI Xinli, SUN Peichong, XU Mingjie, CAO Jinde 5true 2025/12/2 0:00:00 FU Siqing, LI Tiejun, WU Lizhou, ZHANG Chunyuan, MA Sheng, ZHANG Jianmin, REN Ruixuan 4true 2025/12/2 0:00:00 CAO Jijun, WU Zongming, TANG Qiang, LI Xiaoyu 3true 2025/12/2 0:00:00 ZHANG Jianfeng, XIE Dong, JIAN Songlei, LI Bao, WANG Xiaochuan, GUO Yong, YU Jie 2true 2025/12/2 0:00:00 WANG Xiangqian, SHEN Yuhao, JING Kun, LYU Yafei 1true