The research on improved companding transformation for reducing PAPR in underwater acoustic OFDM communication system
type: Item
instance of: scholarly article
title: The research on improved companding transformation for reducing PAPR in underwater acoustic OFDM communication system
zbMATH Open document ID: 1417.90055
DOI: 10.1155/2016/3167483
author: Jinqiu Wu
author: Gang Qiao
author: Xiao-Fei Qi
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-19T00:00:00Z
review text: Summary: To solve the problem of the high peak-to-average power ratio (PAPR) in Orthogonal Frequency Division Multiplexing (OFDM) for the underwater acoustic communication system, the paper offers a method of reducing PAPR which combines the amplitude limiting and the improved nonlinear transformation. Traditional amplitude limiting technique can reduce PAPR in OFDM system effectively, at the cost of reducing the bit error rate (BER). However the companding transformation has far less computation complexity than SLM or PTS technologies and can improve the BER performance compared to the amplitude limiting technique simultaneously. The paper combines these two kinds of techniques, takes full use of advantages of the two method, and puts forward a low-complexity scheme choosing parameters that are more appropriate to the underwater acoustic field, with the result of improved BER performance even in lower SNR. Both simulation and experiment results show that the new method which combines clipping and companding transformation can effectively reduce the PAPR in the underwater acoustic OFDM communication system and improve the BER performance simultaneously.
Mathematics Subject Classification ID: 90B20
Mathematics Subject Classification ID: 90B06
zbMATH DE Number: 7025674
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/3167483
OpenAlex ID: W2339595823
Wikidata QID: Q59123442
cites work: Sparse Channel Estimation for Multicarrier Underwater Acoustic Communication: From Subspace Methods to Compressed Sensing
description: scientific article; zbMATH DE number 7025674