The Experts below are selected from a list of 55545 Experts worldwide ranked by ideXlab platform
Luc Vandendorpe - One of the best experts on this subject based on the ideXlab platform.
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wide band sensing and optimization for cognitive radio networks with Noise Variance uncertainty
IEEE Transactions on Communications, 2015Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper considers wide-band spectrum sensing and optimization for cognitive radio (CR) networks with Noise Variance uncertainty. It is assumed that the considered wide-band contains one or more white sub-bands. Under this assumption, we consider throughput maximization of the CR network while appropriately protecting the primary network. We address this problem as follows. First, we propose novel ratio based test statistics for detecting the edges of each sub-band. Second, we employ simple energy comparison approach to choose one reference white sub-band. Third, we propose novel generalized energy detector (GED) for examining each of the remaining sub-bands by exploiting the Noise information of the reference white sub-band. Finally, we optimize the sensing time $(T_{o} ) $ to maximize the CR network throughput using the detection and false alarm probabilities of the GED. The proposed GED does not suffer from signal to Noise ratio (SNR wall and outperforms the existing signal detectors. Moreover, the relationship between the proposed GED and conventional energy detector (CED is quantified analytically. We show that the optimal $T_{o} $ depends on the Noise Variance information. In particular, with 10TV bands, $\hbox{SNR}=-20\ \hbox{dB} $ and 2s frame duration, we found that the optimal $T_{o} $ is 28.5 ms (50.6 ms) with perfect (imperfect) Noise Variance scenario.
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Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty
IEEE Transactions on Wireless Communications, 2014Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper proposes novel spectrum sensing algorithms for cognitive radio networks. By assuming known transmitter pulse shaping filter, synchronous and asynchronous receiver scenarios have been considered. For each of these scenarios, the proposed algorithm is explained as follows: First, by introducing a combiner vector, an over-sampled signal of total duration equal to the symbol period is combined linearly. Second, for this combined signal, the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the signal energy corresponding to the maximum and minimum SNRs are proposed as a test statistics. For this test statistics, analytical probability of false alarm (Pf) and detection (Pd) expressions are derived for additive white Gaussian Noise (AWGN) channel. The proposed algorithms are robust against Noise Variance uncertainty. The generalization of the proposed algorithms for unknown transmitter pulse shaping filter has also been discussed. Simulation results demonstrate that the proposed algorithms achieve better Pd than that of the Eigenvalue decomposition and energy detection algorithms in AWGN and Rayleigh fading channels with Noise Variance uncertainty. The proposed algorithms also guarantee the desired Pf(Pd) in the presence of adjacent channel interference signals.
Tadilo Endeshaw Bogale - One of the best experts on this subject based on the ideXlab platform.
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wide band sensing and optimization for cognitive radio networks with Noise Variance uncertainty
IEEE Transactions on Communications, 2015Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper considers wide-band spectrum sensing and optimization for cognitive radio (CR) networks with Noise Variance uncertainty. It is assumed that the considered wide-band contains one or more white sub-bands. Under this assumption, we consider throughput maximization of the CR network while appropriately protecting the primary network. We address this problem as follows. First, we propose novel ratio based test statistics for detecting the edges of each sub-band. Second, we employ simple energy comparison approach to choose one reference white sub-band. Third, we propose novel generalized energy detector (GED) for examining each of the remaining sub-bands by exploiting the Noise information of the reference white sub-band. Finally, we optimize the sensing time $(T_{o} ) $ to maximize the CR network throughput using the detection and false alarm probabilities of the GED. The proposed GED does not suffer from signal to Noise ratio (SNR wall and outperforms the existing signal detectors. Moreover, the relationship between the proposed GED and conventional energy detector (CED is quantified analytically. We show that the optimal $T_{o} $ depends on the Noise Variance information. In particular, with 10TV bands, $\hbox{SNR}=-20\ \hbox{dB} $ and 2s frame duration, we found that the optimal $T_{o} $ is 28.5 ms (50.6 ms) with perfect (imperfect) Noise Variance scenario.
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Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty
IEEE Transactions on Wireless Communications, 2014Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper proposes novel spectrum sensing algorithms for cognitive radio networks. By assuming known transmitter pulse shaping filter, synchronous and asynchronous receiver scenarios have been considered. For each of these scenarios, the proposed algorithm is explained as follows: First, by introducing a combiner vector, an over-sampled signal of total duration equal to the symbol period is combined linearly. Second, for this combined signal, the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the signal energy corresponding to the maximum and minimum SNRs are proposed as a test statistics. For this test statistics, analytical probability of false alarm (Pf) and detection (Pd) expressions are derived for additive white Gaussian Noise (AWGN) channel. The proposed algorithms are robust against Noise Variance uncertainty. The generalization of the proposed algorithms for unknown transmitter pulse shaping filter has also been discussed. Simulation results demonstrate that the proposed algorithms achieve better Pd than that of the Eigenvalue decomposition and energy detection algorithms in AWGN and Rayleigh fading channels with Noise Variance uncertainty. The proposed algorithms also guarantee the desired Pf(Pd) in the presence of adjacent channel interference signals.
John Grosspietsch - One of the best experts on this subject based on the ideXlab platform.
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energy detection using estimated Noise Variance for spectrum sensing in cognitive radio networks
Wireless Communications and Networking Conference, 2008Co-Authors: Zhuan Ye, Gokhan Memik, John GrosspietschAbstract:In this paper, we analyze the performance of spectrum sensing based on energy detection. We do not assume the exact Noise Variance is known a priori. Instead, an estimated Noise Variance is used to calculate the threshold used in the spectrum sensing based on energy detection. We propose a new analytical model to evaluate the statistical performance of the energy detection. We claim some characteristics of this model, and analyze how these characteristics affect the performance of spectrum sensing. The analytical results are verified through numerical examples and simulations. Through these examples, we demonstrate the effectiveness of our analytical model: we show how it can be used to set the appropriate threshold such that more spectrum sharing can be facilitated, especially when combined with cooperative spectrum sensing method.
Saeed Gazor - One of the best experts on this subject based on the ideXlab platform.
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Multiple antenna spectrum sensing in cognitive radios
IEEE Transactions on Wireless Communications, 2010Co-Authors: Abbas Taherpour, Masoumeh Nasiri-kenari, Saeed GazorAbstract:In this paper, we consider the problem of spectrum sensing by using multiple antenna in cognitive radios when the Noise and the primary user signal are assumed as independent complex zero-mean Gaussian random signals. The optimal multiple antenna spectrum sensing detector needs to know the channel gains, Noise Variance, and primary user signal Variance. In practice some or all of these parameters may be unknown, so we derive the generalized likelihood ratio (GLR) detectors under these circumstances. The proposed GLR detector, in which all the parameters are unknown, is a blind and invariant detector with a low computational complexity. We also analytically compute the missed detection and false alarm probabilities for the proposed GLR detectors. The simulation results provide the available traded-off in using multiple antenna techniques for spectrum sensing and illustrates the robustness of the proposed GLR detectors compared to the traditional energy detector when there is some uncertainty in the given Noise Variance.
Poogyeon Park - One of the best experts on this subject based on the ideXlab platform.
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stabilization of a bias compensated normalized least mean square algorithm for noisy inputs
IEEE Transactions on Signal Processing, 2017Co-Authors: Sang Mok Jung, Poogyeon ParkAbstract:This paper proposes a stability-guaranteed bias-compensated normalized least-mean-square (BC-NLMS) algorithm for noisy inputs. The bias-compensated algorithms require the estimated input Noise Variance in the elimination process of the bias caused by noisy inputs. However, the conventional methods of estimating the input Noise Variance in those algorithms might cause the instability for a specific situation. This paper first analyzes the stability of the BC-NLMS algorithm by investigating the dynamics of both the mean deviation and the mean-square deviation in the BC-NLMS algorithm. Based on the analysis, the estimation of the input Noise Variance and the adjustment of the step size are carried out to perform a stabilization as well as a performance enhancement in terms of a steady-state error and a convergence rate. Simulations in system identification and acoustic echo cancellation scenarios with noisy inputs show that the proposed algorithm outperforms the existing bias-compensated algorithms in the aspect of the stability, the steady-state error, and the convergence rate.
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fast communication a band dependent variable step size sign subband adaptive filter
Signal Processing, 2014Co-Authors: Jinwoo Yoo, Jaewook Shin, Poogyeon ParkAbstract:This letter proposes a band-dependent variable step-size sign subband adaptive filter using the concept of mean-square deviation (MSD) minimization. Since it is difficult to obtain the value of the MSD accurately, the proposed step size is derived by minimizing the upper bound of the conditional MSD with given input. By assigning the different step size in each band, the filter performance can be improved. Moreover, we suggest the estimation method of the measurement-Noise Variance in an impulsive-Noise environment, because the proposed algorithm needs the measurement-Noise Variance to calculate the step size. The reset algorithm is also applied for maintaining the filter performance when a system change occurs suddenly. Simulation results demonstrate that the proposed algorithm performs better than the existing algorithms in aspects of the convergence rate and the steady-state estimation error.
