Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.
翻译:在无线通信领域,最近考虑了很多机器学习算法。以前,我们曾提议使用一个深层的全进化神经网络(CNN)处理接收器,并展示它来提供相当大的性能收益。在这项研究中,我们侧重于发射机的机器学习算法。特别是,我们考虑光束并提议一个CNN,用于给定的上行链路作为输入,输出下行链路信息用于波状。CNN是经过监督的培训,既考虑基于UE接收器性能的上行和下行链路传输,又考虑基于UE接收器性能的亏损功能。神经网络的主要任务是预测上行和下行链路槽之间的频道演变,但它也可以学会处理整个链条中的低效和错误,包括实际的波状阶段。所提供的数字实验显示改进的波状性能。