论文标题
使用机器学习的大规模MIMO通道预测：域转换的力量
Massive MIMO Channel Prediction Using Machine Learning: Power of Domain Transformation
论文作者
论文摘要
为了补偿宽带大量多输入多输入（MIMO）系统中过时的通道状态信息的损失，可以通过利用无线通道的时间相关性来执行通道预测。最近设计了用于大规模MIMO系统的基于机器学习（ML）的通道预测指标。但是，收集大量培训数据的开销直接影响系统的延迟。在本文中，我们提出了一种新型的基于ML的通道预测技术，该技术可以通过将频道的域从子载波转换为宽带大型MIMO系统中的天线来减少开销，从而收集训练数据。数值结果表明，与没有域转换的基于ML的基于ML的通道预测技术相比，所提出的技术不仅可以减少开销的时间开销，而且还可以提供额外的性能增长。
To compensate the loss from outdated channel state information in wideband massive multiple-input multipleoutput (MIMO) systems, channel prediction can be performed by leveraging the temporal correlation of wireless channels. Machine learning (ML)-based channel predictors for massive MIMO systems were designed recently; however, the time overhead to collect a large amount of training data directly affects the latency of the system. In this paper, we propose a novel ML-based channel prediction technique, which can reduce the time overhead to collect the training data by transforming the domain of channels from subcarrier to antenna in wideband massive MIMO systems. Numerical results show that the proposed technique can not only reduce the time overhead but also give additional performance gain compared to the ML-based channel prediction techniques without the domain transformation.
