Distributed Optimization of Multiuser MIMO Relay Network Using Backpropagation Algorithm

This paper studies a multiuser multi-input multi-output (MIMO) relay network, where the transmit nodes and the relay nodes are subject to the nonlinear instantaneous power constraints. We introduce a perspective of regarding a relay network as a so-termed quasi-neural network by drawing its striking...

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Bibliographic Details
Published inConference record - Asilomar Conference on Signals, Systems, & Computers pp. 1205 - 1209
Main Authors Wang, Rui, Jiang, Yi
Format Conference Proceeding
LanguageEnglish
Published IEEE 31.10.2021
Subjects
Artificial neural networks
Backpropagation
backpropagation algorithm
Interference
interference suppression
nonlinear amplify-and-forward
Numerical simulation
quasi-neural network
relay network
Relay networks (telecommunication)
Training
Transceivers
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Summary:This paper studies a multiuser multi-input multi-output (MIMO) relay network, where the transmit nodes and the relay nodes are subject to the nonlinear instantaneous power constraints. We introduce a perspective of regarding a relay network as a so-termed quasi-neural network by drawing its striking analogies to a (four-layer) artificial neural network (ANN) and propose a distributed scheme inspired by the backpropagation (BP) algorithm to optimize the transceivers. Using a crafty design of training sequences, the proposed scheme can be implemented with no channel state information (CSI) and no data exchange between the relay nodes. Numerical simulations verify the effectiveness of the proposed scheme. More important, it can suppress interferences from unknown directions.
ISSN:2576-2303
DOI:10.1109/IEEECONF53345.2021.9723157