Early results on deep unfolded conjugate gradient-based large-scale MIMO detection


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Ahmed Ouameur, M. et Massicotte, D. (2021). Early results on deep unfolded conjugate gradient-based large-scale MIMO detection. IET Communications, 15 (3). p. 435-444. ISSN 1751-8636 DOI 10.1049/cmu2.12076

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Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large-scale multiple-input multiple-output detection. The proposed technique combines the advantages of a model-driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugate gradient method. We derive the closed-form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions.

Type de document: Article
Mots-clés libres: Data flow analysis Data flow graphs Deep learning Graphic methods Learning systems MIMO systems
Date de dépôt: 09 mai 2022 15:17
Dernière modification: 09 mai 2022 15:17
Version du document déposé: Version officielle de l'éditeur
URI: https://depot-e.uqtr.ca/id/eprint/10126

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