On the Equalization of an OFDM-Based Radio-over-Fiber System Using Neural Networks

Loading...
Thumbnail Image
Date
2017-04
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Společnost pro radioelektronické inženýrství
Altmetrics
Abstract
In this study the impact of a Radio-over-Fiber (RoF) subsystem on the performance of Orthogonal Frequency Division Multiplexing (OFDM) system is evaluated. The study investigates the use of Multi-Layered Perceptron (MLP) and Radial Basis Function (RBF) neural networks to compensate for the optical subsystem nonlinearities in terms of bit error rate, error vector magnitude, and computational complexity. The Bit Error Rate (BER) and Error Vector Magnitude (EVM) results show that the performance of MLP neural network is superior to that of RBF neural network and time-multiplexed pilot-based equalizer especially in the case of highly nonlinear behavior of the RoF subsystem.
Description
Citation
Radioengineering. 2017 vol. 26, č. 1, s. 162-169. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2017/17_01_0162_0169.pdf
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
Creative Commons Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
Collections
Citace PRO