The University of Southampton
University of Southampton Institutional Repository

Particle Swarm Optimization Assisted B-spline Neural Network Based Predistorter Design to Enable Transmit Precoding for Nonlinear MIMO Downlink

Particle Swarm Optimization Assisted B-spline Neural Network Based Predistorter Design to Enable Transmit Precoding for Nonlinear MIMO Downlink
Particle Swarm Optimization Assisted B-spline Neural Network Based Predistorter Design to Enable Transmit Precoding for Nonlinear MIMO Downlink
For the multiple-input multiple-output (MIMO) downlink employing high-order quadrature amplitude modulation signaling and with nonlinear high power amplifiers (HPAs) at base station transmitter, the existing precoding designs relying on the linear MIMO channel can no longer work. We propose an efficient and accurate predistorter design to enable transmit precoding for nonlinear MIMO downlink. Specifically, we obtain the closed-form least squares estimates of the nonlinear HPA's amplitude and phase response using two B-spline neural networks during training. The estimated HPA's phase response automatically yields the estimate of the predistorter's phase response. Based on the B-spline neural network estimate of the HPA's amplitude response, we construct a B-spline neural network model for the predistorter amplitude response, and we adopt a particle swarm optimization (PSO) algorithm to solve this highly nonlinear optimization problem. Using our accurate predistorter estimate to pre-compensate for the nonlinear distortions of the transmit HPAs, a standard full-digital transmit precoding design can readily be adopted to combat the MIMO channel interference. A simulation study is conducted to demonstrate the effectiveness of our proposed PSO assisted predistorter design.
0925-2312
336-348
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Khalaf, Emad
f8dad6b4-c9c8-4e20-b5be-62aec5625eba
Morfeq, Ali
c6be8aa2-aba9-4d2d-aae9-0056ff0ba742
Alotaibi, Naif
039035d6-edee-4e87-93b5-7c847f88e956
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Khalaf, Emad
f8dad6b4-c9c8-4e20-b5be-62aec5625eba
Morfeq, Ali
c6be8aa2-aba9-4d2d-aae9-0056ff0ba742
Alotaibi, Naif
039035d6-edee-4e87-93b5-7c847f88e956

Chen, Sheng, Ng, Soon Xin, Khalaf, Emad, Morfeq, Ali and Alotaibi, Naif (2021) Particle Swarm Optimization Assisted B-spline Neural Network Based Predistorter Design to Enable Transmit Precoding for Nonlinear MIMO Downlink. Neurocomputing, 458, 336-348.

Record type: Article

Abstract

For the multiple-input multiple-output (MIMO) downlink employing high-order quadrature amplitude modulation signaling and with nonlinear high power amplifiers (HPAs) at base station transmitter, the existing precoding designs relying on the linear MIMO channel can no longer work. We propose an efficient and accurate predistorter design to enable transmit precoding for nonlinear MIMO downlink. Specifically, we obtain the closed-form least squares estimates of the nonlinear HPA's amplitude and phase response using two B-spline neural networks during training. The estimated HPA's phase response automatically yields the estimate of the predistorter's phase response. Based on the B-spline neural network estimate of the HPA's amplitude response, we construct a B-spline neural network model for the predistorter amplitude response, and we adopt a particle swarm optimization (PSO) algorithm to solve this highly nonlinear optimization problem. Using our accurate predistorter estimate to pre-compensate for the nonlinear distortions of the transmit HPAs, a standard full-digital transmit precoding design can readily be adopted to combat the MIMO channel interference. A simulation study is conducted to demonstrate the effectiveness of our proposed PSO assisted predistorter design.

Text
NEUROC2021-Oct - Author's Original
Restricted to Repository staff only
Request a copy
Text
nmimodl - Accepted Manuscript
Restricted to Repository staff only until 4 June 2022.
Request a copy

More information

Accepted/In Press date: 4 June 2021
Published date: 11 October 2021

Identifiers

Local EPrints ID: 449715
URI: http://eprints.soton.ac.uk/id/eprint/449715
ISSN: 0925-2312
PURE UUID: 72a34b1f-c1b4-4c1e-a512-5d61f440a1c2
ORCID for Soon Xin Ng: ORCID iD orcid.org/0000-0002-0930-7194

Catalogue record

Date deposited: 11 Jun 2021 16:32
Last modified: 04 Sep 2021 01:37

Export record

Contributors

Author: Sheng Chen
Author: Soon Xin Ng ORCID iD
Author: Emad Khalaf
Author: Ali Morfeq
Author: Naif Alotaibi

University divisions

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.

×