Equalisation of non-linear time-varying channels using a pipelined decision feedback recurrent neural network filter in wireless communication systems

H.Q. Zhao; X.P. Zeng; J.S. Zhang; T.R. Li

Equalisation of non-linear time-varying channels using a pipelined decision feedback recurrent neural network filter in wireless communication systems

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Equalisation of non-linear time-varying channels using a pipelined decision feedback recurrent neural network filter in wireless communication systems

Author(s): H.Q. Zhao ; X.P. Zeng ; J.S. Zhang ; T.R. Li
DOI: 10.1049/iet-com.2010.0081

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Author(s): H.Q. Zhao ^{1, 2} ; X.P. Zeng ^{2, 3} ; J.S. Zhang ² ; T.R. Li ²
- Affiliations: 1: School of Electrical Engineering, Southwest Jiaotong University, Chengdu, People's Republic of China
  2: School of Information Science & Technology, Southwest Jiaotong University, Chengdu, People's Republic of China
  3: Center of Electronic Lab, Chengdu University of Information Technology, Chengdu, People's Republic of China
Source: Volume 5, Issue 3, 11 February 2011, p. 381 – 395
DOI: 10.1049/iet-com.2010.0081 , Print ISSN 1751-8628, Online ISSN 1751-8636

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To combat the linear and non-linear distortions for time-invariant and time-variant channels, a novel adaptive joint process equaliser based on a pipelined decision feedback recurrent neural network (JPDFRNN) is proposed in this paper. The JPDFRNN consists of a number of simple small-scale decision feedback recurrent neural network (DFRNN) modules and a linear combiner. The cascaded DFRNN provides pre-processing for the linear combiner. Moreover, each DFRNN can provide a local interpolation for M sample points; the final linear combiner presents a global interpolation with good localisation properties. Furthermore, since those modules of non-linear subsection can be performed simultaneously in a pipelined parallelism fashion, this would result in a significant improvement in the total computational efficiency. Simulation results show that the performance of the JPDFRNN using the modified real-time recurrent learning (RTRL) algorithm is superior to that of the DFRNN and RNN for the non-linear time-invariant and time-variant channels.

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Equalisation of non-linear time-varying channels using a pipelined decision feedback recurrent neural network filter in wireless communication systems

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