Skip to Main content Skip to Navigation
Conference papers

Neural Networks-Based Turbo Equalization of a Satellite Communication Channel

Abstract : This paper proposes neural networks-based turbo equalization (TEQ) applied to a non linear channel. Based on a Volterra model of the satellite non linear communication channel, we derive a soft input soft output (SISO) radial basis function (RBF) equalizer that can be used in an iterative equalization in order to improve the system performance. In particular, it is shown that the RBF-based TEQ is able to achieve its matched filter bound (MFB) within few iterations. The paper also proposes a blind implementation of the TEQ using a multilayer perceptron (MLP) as an adaptive model of the nonlinear channel. Asymptotic analysis as well as reduced complexity implementations are also presented and discussed.
Complete list of metadata

Cited literature [17 references]  Display  Hide  Download
Contributor : Open Archive Toulouse Archive Ouverte (OATAO) Connect in order to contact the contributor
Submitted on : Thursday, April 30, 2015 - 9:10:25 AM
Last modification on : Monday, July 4, 2022 - 9:39:58 AM
Long-term archiving on: : Monday, September 14, 2015 - 3:58:05 PM


Files produced by the author(s)


  • HAL Id : hal-01147225, version 1
  • OATAO : 13123


Hasan Abdulkader, Bouchra Benammar, Charly Poulliat, Marie-Laure Boucheret, Nathalie Thomas. Neural Networks-Based Turbo Equalization of a Satellite Communication Channel. 15th International Workshop on Signal Processing Advances in Wireless Communications - SPAWC 2014, Jun 2014, Toronto, Canada. pp. 494-498. ⟨hal-01147225⟩



Record views


Files downloads