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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2017

04.08.2016

PAPR Reduction in OFDM Signals: An Adaptive-Network-Based Fuzzy Inference Approach

verfasst von: Amit Mishra, Zaheeruddin

Erschienen in: Wireless Personal Communications | Ausgabe 2/2017

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Abstract

In this paper, an adaptive-network-based fuzzy inference system (ANFIS) based scheme is analyzed and proposed for reducing the peak-to-average power ratio (PAPR) in multicarrier signals under additive white Gaussian noise and multipath fading (Raleigh) channel environment. This scheme involves training of ANFIS structure in time domain using Orthogonal Frequency Division Multiplexing signals with low PAPR, such as those obtained by approximate gradient project–null subcarrier switching (AGP–NCS) method. Once the ANFIS module is trained, the proposed scheme approximately offers similar reduction in PAPR as compared to AGP–NCS method, with significantly less convergence time and computational complexity. he results show that proposed approach is not only less complex but also maintains the data rate and bit error rate performance compared with other conventional schemes.
Vorheriger Artikel Multi-objective Resource Allocation for LTE/LTE-A Femtocell/HeNB Networks Using Ant Colony Optimization
Nächster Artikel Increasing Lifetime and Fault Tolerance Capability in Wireless Sensor Networks by Providing a Novel Management Framework

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Metadaten
Titel
PAPR Reduction in OFDM Signals: An Adaptive-Network-Based Fuzzy Inference Approach
verfasst von
Amit Mishra
Zaheeruddin
Publikationsdatum
04.08.2016
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-016-3558-4

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