oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2015 ( 4 )

2014 ( 8 )

2013 ( 21 )

2012 ( 68 )

Custom range...

Search Results: 1 - 10 of 366 matches for " Cabric Danijela "
All listed articles are free for downloading (OA Articles)
Page 1 /366
Display every page Item
Cramer-Rao Bounds for Joint RSS/DoA-Based Primary-User Localization in Cognitive Radio Networks
Jun Wang,Jianshu Chen,Danijela Cabric
Mathematics , 2012,
Abstract: Knowledge about the location of licensed primary-users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we consider the achievable accuracy of PU localization algorithms that jointly utilize received-signal-strength (RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only localization algorithms separately and assume DoA estimation error variance is a fixed constant or rather independent of RSS. We derive the CRB for joint RSS/DoA-based PU localization algorithms based on the mathematical model of DoA estimation error variance as a function of RSS, for a given CR placement. The bound is compared with practical localization algorithms and the impact of several key parameters, such as number of nodes, number of antennas and samples, channel shadowing variance and correlation distance, on the achievable accuracy are thoroughly analyzed and discussed. We also derive the closed-form asymptotic CRB for uniform random CR placement, and perform theoretical and numerical studies on the required number of CRs such that the asymptotic CRB tightly approximates the numerical integration of the CRB for a given placement.
Covariance-Based OFDM Spectrum Sensing with Sub-Nyquist Samples
Alireza Razavi,Mikko Valkama,Danijela Cabric
Mathematics , 2015, DOI: 10.1016/j.sigpro.2014.11.017
Abstract: In this paper, we propose a feature-based method for spectrum sensing of OFDM signals from sub-Nyquist samples over a single band. We exploit the structure of the covariance matrix of OFDM signals to convert an underdetermined set of covariance-based equations to an overdetermined one. The statistical properties of sample covariance matrix are analyzed and then based on that an approximate Generalized Likelihood Ratio Test (GLRT) for detection of OFDM signals from sub-Nyquist samples is derived. The method is also extended to the frequency-selective channels.
Compressive Detection of Random Subspace Signals
Alireza Razavi,Mikko Valkama,Danijela Cabric
Mathematics , 2015,
Abstract: The problem of compressive detection of random subspace signals is studied. We consider signals modeled as $\mathbf{y} = \mathbf{H} \mathbf{x}$ where $\mathbf{H}$ is an $N \times K$ matrix with $K \le N$ and $\mathbf{x} \sim \mathcal{N}(0,\sigma_x^2 \mathbf{I}_K)$. We say that signal $\mathbf{y}$ lies in or leans toward a subspace if the condition number of $\mathbf{H} \mathbf{H}^T$ is strictly greater than one. We first design a measurement matrix $\mathbf{\Phi}=[\mathbf{\Phi}_s^T,\mathbf{\Phi}_o^T]^T$ comprising of two sub-matrices $\mathbf{\Phi}_s$ and $\mathbf{\Phi}_o$ where $\mathbf{\Phi}_s$ projects the signals to the strongest left-singular vectors, i.e., the left-singular vectors corresponding to the largest singular values, of subspace matrix $\mathbf{H}$ and $\mathbf{\Phi}_o$ projects it to the weakest left-singular vectors. We then propose two detectors which work based on the difference in energies of the samples measured by two sub-matrices $\mathbf{\Phi}_s$ and $\mathbf{\Phi}_o$. Simplified versions of the proposed detectors for the case when the variance of noise is known are also provided. Furthermore, we study the performance of the detector when measurements are imprecise and show how imprecision can be compensated by employing more measurement devices. The problem is then re-formulated for the case when the signal lies in the union of a finite number of linear subspaces instead of a single linear subspace. Finally, we study the performance of the proposed methods by simulation examples.
Compressive Identification of Active OFDM Subcarriers in Presence of Timing Offset
Alireza Razavi,Mikko Valkama,Danijela Cabric
Mathematics , 2015,
Abstract: In this paper we study the problem of identifying active subcarriers in an OFDM signal from compressive measurements sampled at sub-Nyquist rate. The problem is of importance in Cognitive Radio systems when secondary users (SUs) are looking for available spectrum opportunities to communicate over them while sensing at Nyquist rate sampling can be costly or even impractical in case of very wide bandwidth. We first study the effect of timing offset and derive the necessary and sufficient conditions for signal recovery in the oracle-assisted case when the true active sub-carriers are assumed known. Then we propose an Orthogonal Matching Pursuit (OMP)-based joint sparse recovery method for identifying active subcarriers when the timing offset is known. Finally we extend the problem to the case of unknown timing offset and develop a joint dictionary learning and sparse approximation algorithm, where in the dictionary learning phase the timing offset is estimated and in the sparse approximation phase active subcarriers are identified. The obtained results demonstrate that active subcarrier identification can be carried out reliably, by using the developed framework.
Optimal Discriminant Functions Based On Sampled Distribution Distance for Modulation Classification
Paulo Urriza,Eric Rebeiz,Danijela Cabric
Computer Science , 2013,
Abstract: In this letter, we derive the optimal discriminant functions for modulation classification based on the sampled distribution distance. The proposed method classifies various candidate constellations using a low complexity approach based on the distribution distance at specific testpoints along the cumulative distribution function. This method, based on the Bayesian decision criteria, asymptotically provides the minimum classification error possible given a set of testpoints. Testpoint locations are also optimized to improve classification performance. The method provides significant gains over existing approaches that also use the distribution of the signal features.
Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas
Paulo Urriza,Eric Rebeiz,Danijela Cabric
Computer Science , 2012,
Abstract: In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.
Multiple Antenna Cyclostationary Spectrum Sensing Based on the Cyclic Correlation Significance Test
Paulo Urriza,Eric Rebeiz,Danijela Cabric
Computer Science , 2012,
Abstract: In this paper, we propose and analyze a spectrum sensing method based on cyclostationarity specifically targeted for receivers with multiple antennas. This detection method is used for determining the presence or absence of primary users in cognitive radio networks based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. Analytical expressions for the probability of detection and probability of false-alarm under both spatially uncorrelated or spatially correlated noise are derived and verified by simulation. The detection performance in a Rayleigh flat-fading environment is found and verified through simulations. One of the advantages of the proposed method is that the detection threshold is shown to be independent of both the number of samples and the noise covariance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown to provide higher detection probability and better robustness to noise uncertainty than existing multiple-antenna cyclostationary-based spectrum sensing algorithms under both AWGN as well as a quasi-static Rayleigh fading channel.
Novel Radio Architectures for UWB, 60 GHz, and Cognitive Wireless Systems
Cabric Danijela,Chen Mike SW,Sobel David A,Wang Stanley
EURASIP Journal on Wireless Communications and Networking , 2006,
Abstract: There are several new radio systems which exploit novel strategies being made possible by the regulatory agencies to increase the availability of spectrum for wireless applications. Three of these that will be discussed are ultra-wideband (UWB), 60 GHz, and cognitive radios. The UWB approach attempts to share the spectrum with higher-priority users by transmitting at power levels that are so low that they do not cause interference. On the other hand, cognitive radios attempt to share spectra by introducing a spectrum sensing function, so that they are able to transmit in unused portions at a given time, place, and frequency. Another approach is to exploit the advances in CMOS technology to operate in frequency bands in the millimeter-wave region. 60 GHz operation is particularly attractive because of the 7 GHz of unlicensed spectrum that has been made available there. In this paper, we present an overview of novel radio architecture design approaches and address challenges dealing with high-frequencies, wide-bandwidths, and large dynamic-range signals encountered in these future wireless systems.
Dynamic Spectrum Access: From the Concept to the Implementation
Bourdoux André,Cabric Danijela,Ferrari Gianluigi,Horlin Fran?ois
EURASIP Journal on Wireless Communications and Networking , 2010,
Abstract:
Primary User Traffic Classification in Dynamic Spectrum Access Networks
Chun-Hao Liu,Przemys?aw Pawe?czak,Danijela Cabric
Computer Science , 2014,
Abstract: This paper focuses on analytical studies of the primary user (PU) traffic classification problem. Observing that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered here as the PU traffic descriptor. We investigate two PU traffic classifiers utilizing perfectly measured PU activity (busy) and inactivity (idle) periods: (i) maximum likelihood classifier (MLC) and (ii) multi-hypothesis sequential probability ratio test classifier (MSPRTC). Then, relaxing the assumption on perfect period measurement, we consider a PU traffic observation through channel sampling. For a special case of negligible probability of PU state change in between two samplings, we propose a minimum variance PU busy/idle period length estimator. Later, relaxing the assumption of the complete knowledge of the parameters of the PU period length distribution, we propose two PU traffic classification schemes: (i) estimate-then-classify (ETC), and (ii) average likelihood function (ALF) classifiers considering time domain fluctuation of the PU traffic parameters. Numerical results show that both MLC and MSPRTC are sensitive to the periods measurement errors when the distance among distribution hypotheses is small, and to the distribution parameter estimation errors when the distance among hypotheses is large. For PU traffic parameters with a partial prior knowledge of the distribution, the ETC outperforms ALF when the distance among hypotheses is small, while the opposite holds when the distance is large.
Page 1 /366
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.