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Single-carrier frequency domain equalization for Hammerstein communication systems using complex-valued neural networks

Single-carrier frequency domain equalization for Hammerstein communication systems using complex-valued neural networks
Single-carrier frequency domain equalization for Hammerstein communication systems using complex-valued neural networks
Single-carrier (SC) block transmission with frequency-domain equalization (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high-bandwidth-efficiency and high-power-efficiency systems, the channel can generally be modeled by the Hammerstein system, which includes the nonlinear distortion effects of the high-power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. This paper advocates a complex-valued (CV) B-spline neural-network-based nonlinear SC-FDE scheme for Hammerstein channels. Specifically, we model the nonlinear HPA, which represents the CV static nonlinearity of the Hammerstein channel, by a CV B-spline neural network, and we develop two efficient alternating least squares schemes for estimating the parameters of the Hammerstein channel, including both the channel impulse response coefficients and the parameters of the CV B-spline model. We also use another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard least-squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Equalization of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalization in the frequency domain as well as the inverse B-spline neural network model obtained in the time domain. Extensive simulation results are included to demonstrate the effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels.
1053-587X
4467-4478
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Khalaf, Emad F.
22a91cd6-a364-464f-90a7-c9b31d846f61
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Khalaf, Emad F.
22a91cd6-a364-464f-90a7-c9b31d846f61

Hong, Xia, Chen, Sheng, Harris, Chris J. and Khalaf, Emad F. (2014) Single-carrier frequency domain equalization for Hammerstein communication systems using complex-valued neural networks. IEEE Transactions on Signal Processing, 62 (17), 4467-4478. (doi:10.1109/TSP.2014.2333555).

Record type: Article

Abstract

Single-carrier (SC) block transmission with frequency-domain equalization (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high-bandwidth-efficiency and high-power-efficiency systems, the channel can generally be modeled by the Hammerstein system, which includes the nonlinear distortion effects of the high-power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. This paper advocates a complex-valued (CV) B-spline neural-network-based nonlinear SC-FDE scheme for Hammerstein channels. Specifically, we model the nonlinear HPA, which represents the CV static nonlinearity of the Hammerstein channel, by a CV B-spline neural network, and we develop two efficient alternating least squares schemes for estimating the parameters of the Hammerstein channel, including both the channel impulse response coefficients and the parameters of the CV B-spline model. We also use another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard least-squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Equalization of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalization in the frequency domain as well as the inverse B-spline neural network model obtained in the time domain. Extensive simulation results are included to demonstrate the effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels.

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Accepted/In Press date: 22 June 2014
e-pub ahead of print date: 2 July 2014
Published date: 1 September 2014
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 367925
URI: http://eprints.soton.ac.uk/id/eprint/367925
ISSN: 1053-587X
PURE UUID: b6b030ea-e802-4612-9906-4006a72ad8dc

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Date deposited: 21 Aug 2014 13:29
Last modified: 06 Aug 2019 18:44

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