论文标题
移动性，流量和无线电频道预测：5G及以后的应用程序
Mobility, traffic and radio channel prediction: 5G and beyond applications
论文作者
论文摘要
机器学习（ML）是在无线电访问网络（RANS）中启用自动化的重要组成部分。为RAN应用ML的工作已经开发了很多年，现在也引起了3GPP和Open-Ran标准化Fora的关注。在最近的3GPP规范工作中也强调了多个功能的关键组成部分，是使用移动性，流量和无线电通道预测。这些类型的预测构成了智能使得在当前和将来的无线网络中利用ML的电势。本文提供了利用此类智能推动因素的当前应用程序的评估结果概述，然后我们讨论这些支持者如何成为新兴6G用例（例如无线能量传输）的基石。
Machine learning (ML) is an important component for enabling automation in Radio Access Networks (RANs). The work on applying ML for RAN has been under development for many years and is now also drawing attention in 3GPP and Open-RAN standardization fora. A key component of multiple features, also highlighted in the recent 3GPP specification work, is the use of mobility, traffic and radio channel prediction. These types of predictions form the intelligence enablers to leverage the potentials for ML for RAN, both for current and future wireless networks. This paper provides an overview with evaluation results of current applications that utilize such intelligence enablers, we then discuss how those enablers likely will be a cornerstone for emerging 6G use cases such as wireless energy transmission.
