Artificial intelligent power prediction for efficient resource management of WCDMA mobile network

• 主要な著者: Tee Y.K., Tinng S.K., Koh J., David Y.
• その他の著者: 55031013900
• フォーマット: Conference Paper
• 出版事項: 2023
• 主題: Call admission control; Genetic algorithm; Quality of service; Support vector regression; Wideband code division multiple access; Access control; Code division multiple access; Electric power utilization; Genetic algorithms; Genetic engineering; Network management; Planning; Regression analysis; Resource allocation; Telecommunication services; Vectors; Wireless networks; Wireless telecommunication systems; Artificial intelligent; Dynamic resource allocations; Handover; Integrated supports; Mobile networks; New services; Optimal beams; Power Consumption; Prediction errors; Resource management; Support vector regressions; Quality control

This paper presents a method of predicting changes of power consumption at Node B of a wideband code division multiple access (WCDMA) mobile network due to dynamic resource allocation such as movement of unit equipment (UE), handover call from adjacent cell and accommodation of new service request....