DeepTx: Deep Learning Beamforming with Channel Prediction
- URL: http://arxiv.org/abs/2202.07998v1
- Date: Wed, 16 Feb 2022 11:19:54 GMT
- Title: DeepTx: Deep Learning Beamforming with Channel Prediction
- Authors: Janne M.J. Huttunen, Dani Korpi, Mikko~Honkala
- Abstract summary: In this study, we focus on machine learning algorithms for the transmitter.
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 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.
- Score: 8.739166282613118
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 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.
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