Artificial Intelligence-Enabled 5G Network Performance Evaluation With Fine Granularity and High Accuracy

Abstract

Network performance evaluation is crucial in ensuring the effective operation of 5G wireless networks, offering valuable insights into evaluating network status and user experience. However, the complexity of network conditions, characterized by high dynamics and diverse user requirements across various vertical applications, presents a significant challenge for generating accurate and detailed evaluation results using existing algorithms. To provide a feasible solution for this issue, an artificial intelligence-enabled 5G network performance evaluation scheme for private 5G networks is proposed. First, the network performance evaluation at different granularities is modeled with the deployment of network performance evaluation introduced. Furthermore, an intelligent network performance evaluation architecture based on residual networks with the attention mechanism is introduced, which can generate evaluation scores based on key performance indicators of reliability, accessibility, utilization, integrity, mobility and retainability. Additionally, the corresponding training strategies for the intelligent model, catering to different evaluation granularity, are thoroughly designed. Finally, to validate the effectiveness of the proposed scheme, comprehensive experiments are conducted using practical 5G network operation system data. The experimental results demonstrate the scheme’s ability to achieve highly accurate evaluations with fine spatial granularity. These findings establish the feasibility and efficacy of the proposed artificial intelligence-enabled scheme in enhancing 5G network performance evaluation.

Keywords

• Network performance evaluation
• 5G vertical applications
• artificial intelligence
• convolutional neural networks
• attention mechanism