DeepTx: Deep Learning Beamforming with Channel Prediction
This work addresses beamforming inefficiencies for wireless communication systems, but it is incremental as it builds on prior deep learning approaches for receiver processing.
The paper tackles the problem of beamforming in wireless communications by proposing a deep learning model that predicts downlink channel information from uplink estimates, demonstrating improved beamforming performance in numerical experiments.
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.