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77
Spectrum Sensing for Cognitive Radio  Stateoftheart and recent advances
 IEEE SIGNAL PROCESSING MAGAZINE (PRINT), (29), 3, 101116.
, 2012
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Blind Estimation of Direct Sequence Spread Spectrum Signals in Multipath
 IEEE Transactions on Signal Processing
, 1997
"... Selfrecovering receivers for directsequence spreadspectrum signals with unknown spreading codes are discussed in this paper. Applications include signal interception, jamming, and low probability of intercept (LPI) communications. A multirate /multichannel, discretetime model of the spread spectr ..."
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Cited by 34 (2 self)
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Selfrecovering receivers for directsequence spreadspectrum signals with unknown spreading codes are discussed in this paper. Applications include signal interception, jamming, and low probability of intercept (LPI) communications. A multirate /multichannel, discretetime model of the spread spectrum signal is introduced, which establishes links with array processing techniques. Borrowing blind channel estimation ideas, which were originally developed in the context of fractionally spaced equalizers or receivers with multiple antennas, linear solutions are obtained that are independent of the input distribution. The signal interception problem is further studied, and a zeroforcing (ZF) receiver/equalizer is proposed to recover the transmitted data. Its performance is analyzed, and some illustrative simulations are presented.
Spectrum Sensing in Cognitive Radios Based on Multiple Cyclic Frequencies
 in Proc. 2nd Int. Conf. on Cognitive Radio Oriented Wireless Networks and Communications
"... c © 2007 IEEE. Reprinted with permission. ..."
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Asymptotic Analysis of Blind Cyclic CorrelationBased SymbolRate Estimators
, 2000
"... This paper considers the problem of blind symbol rate estimation of signals linearly modulated by a sequence of unknown symbols. Oversampling the received signal generates cyclostationary statistics that are exploited to devise symbolrate estimators by maximizing in the cyclic domain a (possibly we ..."
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Cited by 24 (5 self)
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This paper considers the problem of blind symbol rate estimation of signals linearly modulated by a sequence of unknown symbols. Oversampling the received signal generates cyclostationary statistics that are exploited to devise symbolrate estimators by maximizing in the cyclic domain a (possibly weighted) sum of modulus squares of cyclic correlation estimates. Although quite natural, the asymptotic (large sample) performance of this estimator has not been studied rigorously. The consistency and asymptotic normality of this symbolrate estimator is established when the number of samples converges to infinity. It is shown that this estimator exhibits a fast convergence rate (proportional to 3 2 ), and it admits a simple closedform expression for its asymptotic variance. This asymptotic expression enables performance analysis of the rate estimator as a function of the number of estimated cyclic correlation coefficients and the weighting matrix. A justification for the high performance of the unweighted estimator in high signaltonoise scenarios is also provided.
Optimal and SubOptimal Spectrum Sensing of OFDM Signals
 in Known and Unknown Noise Variance. Ieee Journal on Selected Areas in Communications,
, 2011
"... AbstractWe consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely known noise and signal powers, we set up a vectormatrix model for an OFDM signal with a cyclic prefix and derive the optimal NeymanPearson detector from first principles. The optimal detector explo ..."
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Cited by 15 (3 self)
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AbstractWe consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely known noise and signal powers, we set up a vectormatrix model for an OFDM signal with a cyclic prefix and derive the optimal NeymanPearson detector from first principles. The optimal detector exploits the inherent correlation of the OFDM signal incurred by the repetition of data in the cyclic prefix, using knowledge of the length of the cyclic prefix and the length of the OFDM symbol. We compare the optimal detector to the energy detector numerically. We show that the energy detector is nearoptimal (within 1 dB SNR) when the noise variance is known. Thus, when the noise power is known, no substantial gain can be achieved by using any other detector than the energy detector. For the case of completely unknown noise and signal powers, we derive a generalized likelihood ratio test (GLRT) based on empirical secondorder statistics of the received data. The proposed GLRT detector exploits the nonstationary correlation structure of the OFDM signal and does not require any knowledge of the noise power or the signal power. The GLRT detector is compared to stateoftheart OFDM signal detectors, and shown to improve the detection performance with 5 dB SNR in relevant cases.
Blind spectrum sensing for cognitive radio based on signal space dimension estimation
 IEEE Proc. ICC
, 2009
"... Abstract — Cognitive radio devices will be able to seek and dynamically use frequency bands for network access. This will be done by autonomous detection of vacant subbands in the radio spectrum. In this paper 1, we propose a new method for blind detection of vacant subbands over the spectrum band ..."
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Cited by 12 (4 self)
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Abstract — Cognitive radio devices will be able to seek and dynamically use frequency bands for network access. This will be done by autonomous detection of vacant subbands in the radio spectrum. In this paper 1, we propose a new method for blind detection of vacant subbands over the spectrum band. The proposed method exploits model selection tools like Akaike information criterion (AIC) and Akaike weights to sense holes in the spectrum band. Specifically, we assume that the noise of the radio spectrum band can still be adequately modeled using Gaussian distribution. We then compute and analyze Akaike weights in order to decide if the distribution of the received signal fits the noise distribution or not. Our theoretical result are validated using experimental measurements captured by Eurécom RF Agile Platform. Simulations show promising performance results of the proposed technique in terms of sensing vacant subbands in the spectrum.
Blind spectrum sensing by information theoretic criteria for cognitive radios
 IEEE Trans. Veh. Technol
, 2010
"... Abstract—Spectrum sensing is a fundamental and critical issue for opportunistic spectrum access in cognitive radio networks. Among the many spectrumsensing methods, the information theoretic criteria (ITC)based method is a promising blind method that can reliably detect the primary users while re ..."
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Cited by 9 (2 self)
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Abstract—Spectrum sensing is a fundamental and critical issue for opportunistic spectrum access in cognitive radio networks. Among the many spectrumsensing methods, the information theoretic criteria (ITC)based method is a promising blind method that can reliably detect the primary users while requiring little prior information. In this paper, we provide an intensive treatment on the ITC sensing method. To this end, we first introduce a new overdetermined channel model constructed by applying multiple antennas or oversampling at the secondary user to make ITC applicable. Then, a simplified ITC sensing algorithm is introduced, which needs to compute and compare only two decision values. Compared with the original ITC sensing algorithm, the simplified algorithm significantly reduces the computational complexity with no loss in performance. Applying the recent advances in random matrix theory, we then derive closedform expressions to tightly approximate both the probability of false alarm and the probability of detection. Based on the insight derived from the analytical study, we further present a generalized ITC sensing algorithm that can provide a flexible tradeoff between the probability of detection and the probability of false alarm. Finally, comprehensive simulations are carried out to evaluate the performance of the proposed ITC sensing algorithms. Results show that they considerably outperform other blind spectrumsensing methods in certain cases. Index Terms—Cognitive radio (CR) networks, information theoretic criteria (ITC), random matrix theory, spectrum sensing. I.
Blind carrier frequency offset estimation for noncircular constellation based transmission
 IEEE Trans. Signal Processing
, 2003
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