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1. DeepTx: Deep Learning Beamforming
With Channel Prediction
Abstract
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 u
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
DeepTx: Deep Learning Beamforming
With Channel Prediction
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
erable 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
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.
DeepTx: Deep Learning Beamforming
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
erable 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
sed 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
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