利用多模态数据进行NR网络质量优化的研究

Research on Quality Optimization of NR Networks Using Multimodal Data

5GNR网络的发展为用户带来了前所未有的高速率和低时延体验,但随着网络复杂性的增加以及业务范围的扩展,如何确保网络质量成为一个挑战。多模态数据为优化网络质量提供了新的视角和方法。该文研究了如何利用多模态数据捕获与预处理技术,并融入机器学习和深度学习模型来提升NR网络的性能,文中介绍了多模态数据的概念以及它在网络优化中的重要性,然后详细描述了数据捕获、预处理、建模,定义了优化目标与效用函数,最后回顾了研究成果并展望了未来的研究方向。

The development of 5G NR networks has brought unprecedented high-speed and low latency experiences to users,but with the increase in network complexity and the expansion of business scope,ensuring network quality has become a challenge.Multimodal data provides new perspectives and methods for optimizing network quality.This article explores how to utilize multimodal data capture and preprocessing techniques,and integrate machine learning and deep learning models to improve the performance of NR networks.Firstly,the concept of multimodal data and its importance in network optimization were introduced.Then,the data capture,preprocessing,and modeling were described in detail,until the optimization objective and utility function were defined.Finally,the research results were reviewed and future research directions were discussed.