The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
O Compressed Sensing (CS) é conhecido por fornecer melhor desempenho de estimativa de canal do que o método de mínimos quadrados (LS) para estimativa de canal. No entanto, os atrasos de multipercurso podem não ser resolvidos se abrangerem entre as grades. Este problema de grade do CS é um obstáculo para a estimativa de canais de super-resolução. Uma minimização da Norma Atômica (AN) é um dos métodos para estimar parâmetros contínuos. A minimização AN pode recuperar com sucesso um sinal espectralmente esparso de algumas amostras no domínio do tempo, mesmo que o dicionário seja contínuo. Existem estudos mostrando que o método de minimização de AN apresenta melhor resolução que os métodos convencionais de CS. Neste artigo, propomos um método de estimação de canal baseado na minimização de AN para sistemas Spread Spectrum (SS). A precisão da estimativa do canal proposta é comparada com o método LS convencional e o Seletor Dantzig (DS) do CS. Além da aplicação da estimativa de canal na comunicação sem fio, também mostramos que a minimização de AN pode ser aplicada ao Sistema de Posicionamento Global (GPS) utilizando a sequência de Ouro.
Dongshin YANG
The University of Kyushu
Yutaka JITSUMATSU
The University of Kyushu
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copiar
Dongshin YANG, Yutaka JITSUMATSU, "Super Resolution Channel Estimation by Using Spread Spectrum Signal and Atomic Norm Minimization" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 12, pp. 2141-2148, December 2018, doi: 10.1587/transfun.E101.A.2141.
Abstract: Compressed Sensing (CS) is known to provide better channel estimation performance than the Least Square (LS) method for channel estimation. However, multipath delays may not be resolved if they span between the grids. This grid problem of CS is an obstacle to super resolution channel estimation. An Atomic Norm (AN) minimization is one of the methods for estimating continuous parameters. The AN minimization can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. There are studies showing that the AN minimization method has better resolution than conventional CS methods. In this paper, we propose a channel estimation method based on the AN minimization for Spread Spectrum (SS) systems. The accuracy of the proposed channel estimation is compared with the conventional LS method and Dantzig Selector (DS) of the CS. In addition to the application of channel estimation in wireless communication, we also show that the AN minimization can be applied to Global Positioning System (GPS) using Gold sequence.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.2141/_p
Copiar
@ARTICLE{e101-a_12_2141,
author={Dongshin YANG, Yutaka JITSUMATSU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Super Resolution Channel Estimation by Using Spread Spectrum Signal and Atomic Norm Minimization},
year={2018},
volume={E101-A},
number={12},
pages={2141-2148},
abstract={Compressed Sensing (CS) is known to provide better channel estimation performance than the Least Square (LS) method for channel estimation. However, multipath delays may not be resolved if they span between the grids. This grid problem of CS is an obstacle to super resolution channel estimation. An Atomic Norm (AN) minimization is one of the methods for estimating continuous parameters. The AN minimization can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. There are studies showing that the AN minimization method has better resolution than conventional CS methods. In this paper, we propose a channel estimation method based on the AN minimization for Spread Spectrum (SS) systems. The accuracy of the proposed channel estimation is compared with the conventional LS method and Dantzig Selector (DS) of the CS. In addition to the application of channel estimation in wireless communication, we also show that the AN minimization can be applied to Global Positioning System (GPS) using Gold sequence.},
keywords={},
doi={10.1587/transfun.E101.A.2141},
ISSN={1745-1337},
month={December},}
Copiar
TY - JOUR
TI - Super Resolution Channel Estimation by Using Spread Spectrum Signal and Atomic Norm Minimization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2141
EP - 2148
AU - Dongshin YANG
AU - Yutaka JITSUMATSU
PY - 2018
DO - 10.1587/transfun.E101.A.2141
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2018
AB - Compressed Sensing (CS) is known to provide better channel estimation performance than the Least Square (LS) method for channel estimation. However, multipath delays may not be resolved if they span between the grids. This grid problem of CS is an obstacle to super resolution channel estimation. An Atomic Norm (AN) minimization is one of the methods for estimating continuous parameters. The AN minimization can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. There are studies showing that the AN minimization method has better resolution than conventional CS methods. In this paper, we propose a channel estimation method based on the AN minimization for Spread Spectrum (SS) systems. The accuracy of the proposed channel estimation is compared with the conventional LS method and Dantzig Selector (DS) of the CS. In addition to the application of channel estimation in wireless communication, we also show that the AN minimization can be applied to Global Positioning System (GPS) using Gold sequence.
ER -