Random Forest Algorithm-Based Lightweight Comprehensive Evaluation for Wireless User Perception

• Main Authors: Kaixuan Zhang, Juan Wang, Wei Zhang, Ke Wang, Jun Zeng, Guanghui Fan, Guan Gui
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Comprehensive evaluation methods; wireless user perception; indicator selection; random forest (RF)
• Online Access: https://ieeexplore.ieee.org/document/8915775/

The quality of wireless user perception for cells in a particular scenario is reflected on a set of indicators. Comprehensive evaluation of those cells is the base of network optimization for operators. Traditional methods use weighted sum of all indicators as the evaluation result. However, these indicators include some ineffective ones, which leads to an unconvincing evaluation result. To achieve a convincing and accurate result, we propose a lightweight comprehensive evaluation method. Firstly, indicator selection is implemented via random forest algorithm. Secondly, those selected indicators are weighted via entropy method. Finally, we compute the score of all cells with the weights. Experiment results are given to show that the cells with higher score perform better on all indicators, which is coincide with the actual situation. Hence, our proposed method is not only lightweight but also obtain more accurate result.