The University of Southampton
University of Southampton Institutional Repository

Nonlinear equalization of Hammerstein OFDM systems

Nonlinear equalization of Hammerstein OFDM systems
Nonlinear equalization of Hammerstein OFDM systems
A practical orthogonal frequency-division multiplexing (OFDM) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. In this contribution, we advocate a novel nonlinear equalization scheme for OFDM Hammerstein systems. We model the nonlinear HPA, which represents the static nonlinearity of the OFDM Hammerstein channel, by a B-spline neural network, and we develop a highly effective alternating least squares algorithm for estimating the parameters of the OFDM Hammerstein channel, including channel impulse response coefficients and the parameters of the B-spline model. Moreover, we also use another B-spline neural network to model the inversion of the HPA’s nonlinearity, 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 byproduct of the Hammerstein channel identification. Equalization of the OFDM Hammerstein channel can then be accomplished by the usual one-tap linear equalization as well as the inverse B-spline neural network model obtained. The effectiveness of our nonlinear equalization scheme for OFDM Hammerstein channels is demonstrated by simulation results.
b-spline neural networks, de boor algorithm, equalization, hammerstein channel, nonlinear high power amplifier, orthogonal frequency-division multiplexing
1053-587X
5629-5639
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Hong, Xia, Chen, Sheng, Gong, Yu and Harris, Chris J. (2014) Nonlinear equalization of Hammerstein OFDM systems. IEEE Transactions on Signal Processing, 62 (21), 5629-5639. (doi:10.1109/TSP.2014.2355773).

Record type: Article

Abstract

A practical orthogonal frequency-division multiplexing (OFDM) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. In this contribution, we advocate a novel nonlinear equalization scheme for OFDM Hammerstein systems. We model the nonlinear HPA, which represents the static nonlinearity of the OFDM Hammerstein channel, by a B-spline neural network, and we develop a highly effective alternating least squares algorithm for estimating the parameters of the OFDM Hammerstein channel, including channel impulse response coefficients and the parameters of the B-spline model. Moreover, we also use another B-spline neural network to model the inversion of the HPA’s nonlinearity, 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 byproduct of the Hammerstein channel identification. Equalization of the OFDM Hammerstein channel can then be accomplished by the usual one-tap linear equalization as well as the inverse B-spline neural network model obtained. The effectiveness of our nonlinear equalization scheme for OFDM Hammerstein channels is demonstrated by simulation results.

Text
TSP2014-11-1.pdf - Version of Record
Restricted to Repository staff only
Request a copy

More information

Published date: 1 November 2014
Keywords: b-spline neural networks, de boor algorithm, equalization, hammerstein channel, nonlinear high power amplifier, orthogonal frequency-division multiplexing
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 369926
URI: http://eprints.soton.ac.uk/id/eprint/369926
ISSN: 1053-587X
PURE UUID: 0486210a-6163-4590-acb5-4ab1192817b2

Catalogue record

Date deposited: 14 Oct 2014 09:56
Last modified: 25 Nov 2019 20:39

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×