Abstract:
In this paper, we aim to design an end-to-end deep learning architecture for a broadcast MIMO system with precoding at the transmitter. The objective is to transmit inter...Show MoreMetadata
Abstract:
In this paper, we aim to design an end-to-end deep learning architecture for a broadcast MIMO system with precoding at the transmitter. The objective is to transmit interference-free data streams to multiple users over a wireless channel. We propose end-to-end learning of communication systems modelled as a Deep autoencoder network with a novel cost function to achieve this goal. This architecture enables optimization of the transmitter and receiver network weights jointly over a wireless channel. We also introduce a way to precode the transmitter embeddings before transmission. An end-to-end training of the autoencoder framework of transmitter-receiver pairs is employed while training the proposed transmit-precoded MIMO system model. Several numerical evaluations over Rayleigh block-fading (RBF) channels with slow fading are presented to prove this approach. Specific training methods are suggested to improve performance over RBF channels in this paper.
Published in: 2020 IEEE 3rd 5G World Forum (5GWF)
Date of Conference: 10-12 September 2020
Date Added to IEEE Xplore: 13 October 2020
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