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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2010-01-01
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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://asp.eurasipjournals.com/content/2010/502369 |
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doaj-073f9706dc83447ca675044a40954aa72020-11-25T00:37:40ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-0120101502369Uplink User Signal Separation for OFDMA-Based Cognitive RadiosGuvenc IsmailŞahin MustafaEArslan Hüseyin<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 Şahin MustafaE Arslan Hüseyin |
spellingShingle |
Guvenc Ismail Şahin MustafaE Arslan Hüseyin Uplink User Signal Separation for OFDMA-Based Cognitive Radios EURASIP Journal on Advances in Signal Processing |
author_facet |
Guvenc Ismail Şahin MustafaE Arslan Hüseyin |
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 |
work_keys_str_mv |
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1725300097037631488 |