Supervised Deep Learning for MIMO Precoding

Pathapati, Aravind Ganesh and Chakradhar, Nakka and Havish, P.N.V.S.S.K. and Somayajula, Sai Ashish and Amuru, Saidhiraj (2020) Supervised Deep Learning for MIMO Precoding. In: 3rd IEEE 5G World Forum, 5GWF 2020, 10 - 12 September 2020, Virtual, Bangalore.

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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 interferencefree 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. © 2020 IEEE.

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Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Autoencoders; Deep Learning; Joint training; MIMO; Precoding
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Nov 2022 12:21
Last Modified: 23 Nov 2022 12:21
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