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Optimization and Analysis of Deep Unfolding Based Double Loop Turbo Equalizers

Sahin, Serdar and Cipriano, Antonio and Poulliat, Charly Optimization and Analysis of Deep Unfolding Based Double Loop Turbo Equalizers. (2020) In: Journées scientifiques d'URSI-France - Workshop: Réseaux du futur : 5G et au-delà (URSI-France 2020), 11 March 2020 - 13 March 2020 (Palaiseau, France).

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Official URL: https://ursifr-2020.sciencesconf.org/307207/document

Abstract

This paper investigates the use of hybrid model-and-data-based deep learning on a recently proposed doubly-iterative turbo equalizer for handling inter-symbol interference (ISI) channel with single-carrier frequency domain equalization (SC-FDE). The receiver is obtained through a message-passing-based approximate Bayesian inference technique, known as expectation propagation (EP). Although this turbo-equalizer has been shown to behave asymptotically like maximum a posteriori (MAP) detection, finite-length numerical results suffer from drawbacks due to simplifying assumptions used during the modelling. Such limitations are partially mitigated by tuning heuristic hyper-parameters through robust learning algorithms. In this article, this strategy is further investigated with discussion on optimized parameters and with the use of an alternative loss function for training, or by adding further capabilities to adapt learned parameters to the channel state information.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-02950730
Audience (conference):National conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Thales (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
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Deposited On:16 Sep 2020 11:07

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