Uplink User Signal Separation for OFDMA-Based Cognitive Radios

<p/> <p>Spectrum awareness of orthogonal frequency division multiple access- (OFDMA-) based cognitive radios (CRs) can be improved by enabling them to separate the primary user signals in the uplink (UL). Assuming availability of information about the basic parameters of the primary syst...

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Main Authors: Guvenc Ismail, &#350;ahin MustafaE, Arslan H&#252;seyin
Format: Article
Language:English
Published: SpringerOpen 2010-01-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://asp.eurasipjournals.com/content/2010/502369
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spelling doaj-073f9706dc83447ca675044a40954aa72020-11-25T00:37:40ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-0120101502369Uplink User Signal Separation for OFDMA-Based Cognitive RadiosGuvenc Ismail&#350;ahin MustafaEArslan H&#252;seyin<p/> <p>Spectrum awareness of orthogonal frequency division multiple access- (OFDMA-) based cognitive radios (CRs) can be improved by enabling them to separate the primary user signals in the uplink (UL). Assuming availability of information about the basic parameters of the primary system as well as time synchronization to the first arriving user signal, two algorithms are proposed in this paper. The first one targets estimating the size of the frequency allocation block of the primary system. The performance of this algorithm is compared with the results of a Gaussian approximation-based approach that aims to determine the probability of correct block size estimation theoretically. The second one is a semiblind user separation algorithm, which estimates the carrier frequency offsets and time delays of each block by exploiting the cross-correlations over pilot subcarriers. A two-dimensional clustering method is then employed to group the estimates, where each group belongs to a different user. It is shown that the proposed algorithms can improve the spectrum opportunity detection of cognitive radios. Feasibility of the algorithms is proved through practical simulations.</p>http://asp.eurasipjournals.com/content/2010/502369
collection DOAJ
language English
format Article
sources DOAJ
author Guvenc Ismail
&#350;ahin MustafaE
Arslan H&#252;seyin
spellingShingle Guvenc Ismail
&#350;ahin MustafaE
Arslan H&#252;seyin
Uplink User Signal Separation for OFDMA-Based Cognitive Radios
EURASIP Journal on Advances in Signal Processing
author_facet Guvenc Ismail
&#350;ahin MustafaE
Arslan H&#252;seyin
author_sort Guvenc Ismail
title Uplink User Signal Separation for OFDMA-Based Cognitive Radios
title_short Uplink User Signal Separation for OFDMA-Based Cognitive Radios
title_full Uplink User Signal Separation for OFDMA-Based Cognitive Radios
title_fullStr Uplink User Signal Separation for OFDMA-Based Cognitive Radios
title_full_unstemmed Uplink User Signal Separation for OFDMA-Based Cognitive Radios
title_sort uplink user signal separation for ofdma-based cognitive radios
publisher SpringerOpen
series EURASIP Journal on Advances in Signal Processing
issn 1687-6172
1687-6180
publishDate 2010-01-01
description <p/> <p>Spectrum awareness of orthogonal frequency division multiple access- (OFDMA-) based cognitive radios (CRs) can be improved by enabling them to separate the primary user signals in the uplink (UL). Assuming availability of information about the basic parameters of the primary system as well as time synchronization to the first arriving user signal, two algorithms are proposed in this paper. The first one targets estimating the size of the frequency allocation block of the primary system. The performance of this algorithm is compared with the results of a Gaussian approximation-based approach that aims to determine the probability of correct block size estimation theoretically. The second one is a semiblind user separation algorithm, which estimates the carrier frequency offsets and time delays of each block by exploiting the cross-correlations over pilot subcarriers. A two-dimensional clustering method is then employed to group the estimates, where each group belongs to a different user. It is shown that the proposed algorithms can improve the spectrum opportunity detection of cognitive radios. Feasibility of the algorithms is proved through practical simulations.</p>
url http://asp.eurasipjournals.com/content/2010/502369
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AT 350ahinmustafae uplinkusersignalseparationforofdmabasedcognitiveradios
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