The Experts below are selected from a list of 11574 Experts worldwide ranked by ideXlab platform
R.m. Buehrer - One of the best experts on this subject based on the ideXlab platform.
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equal ber performance in linear successive interference cancellation for cdma systems
IEEE Transactions on Communications, 2001Co-Authors: R.m. BuehrerAbstract:In this contribution, we calculate the received power distribution required to obtain equal bit-error rate (BER) performance for all links in a code-division multiple-access system employing a linear successive interference cancellation (SIC) receiver at the base station for an additive white Gaussian noise channel. We show that the variance of the Decision Statistic of the linear SIC receiver can be formulated in a nonrecursive manner that allows calculation of the power profile necessary to obtain equal signal-to-noise-plus-interference ratios for all received signals when cancellation order is determined based on average power. When equal BER performance is required, this formulation allows capacity limits to be determined for a required signal-to-interference-plus-noise ratio (SINR) or an SINR limitation to be calculated for a given capacity. We also show that the power profiles required are significantly larger than those obtained when perfect cancellation is assumed, highlighting the inadequacy of such an assumption.
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comments on partial parallel interference cancellation for cdma
IEEE Transactions on Communications, 1999Co-Authors: R.m. Buehrer, S P NicolosoAbstract:We comment on partial parallel interference cancellation as discussed in the paper by Divsalar et al. (see ibid. vol.46, p.258-68, 1998). The aforementioned work showed that by multiplying the symbol estimates by a factor less than unity in the early stages of cancellation, the performance of parallel cancellation can be improved relative to full ("brute force") cancellation. In this paper we analyze the improvement of parallel cancellation when using partial cancellation, and provide additional insight into the gains. Specifically, we show that the Decision Statistic is biased when linear (soft) estimates of the symbol or channel are used for cancellation. Partial cancellation improves the performance in this case by reducing the Decision Statistic bias.
Jose C Principe - One of the best experts on this subject based on the ideXlab platform.
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information theoretic shape matching
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014Co-Authors: Erion Hasanbelliu, Luis Gonzalo Sanchez Giraldo, Jose C PrincipeAbstract:In this paper, we describe two related algorithms that provide both rigid and non-rigid point set registration with different computational complexity and accuracy. The first algorithm utilizes a nonlinear similarity measure known as correntropy. The measure combines second and high order moments in its Decision Statistic showing improvements especially in the presence of impulsive noise. The algorithm assumes that the correspondence between the point sets is known, which is determined with the surprise metric. The second algorithm mitigates the need to establish a correspondence by representing the point sets as probability density functions (PDF). The registration problem is then treated as a distribution alignment. The method utilizes the Cauchy-Schwarz divergence to measure the similarity/distance between the point sets and recover the spatial transformation function needed to register them. Both algorithms utilize information theoretic descriptors; however, correntropy works at the realizations level, whereas Cauchy-Schwarz divergence works at the PDF level. This allows correntropy to be less computationally expensive, and for correct correspondence, more accurate. The two algorithms are robust against noise and outliers and perform well under varying levels of distortion. They outperform several well-known and state-of-the-art methods for point set registration.
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correntropy based matched filtering for classification in sidescan sonar imagery
Systems Man and Cybernetics, 2009Co-Authors: Erio Hasanbelliu, Jose C Principe, Cli SlattoAbstract:This paper presents an automated way of classifying mines in sidescan sonar imagery. A nonlinear extension to the matched filter is introduced using a new metric called correntropy. This method features high order moments in the Decision Statistic showing improvements in classification especially in the presence of noise. Templates have been designed using prior knowledge about the objects in the dataset. During classification, these templates are linearly transformed to accommodate for the shape variability in the observation. The template resulting in the largest correntropy cost function is chosen as the object category. The method is tested on real sonar images producing promising results considering the low number of images required to design the templates.
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Correntropy Based Matched Filtering
2005 IEEE Workshop on Machine Learning for Signal Processing, 2005Co-Authors: Puskal Prasad Pokharel, Rati Agrawal, Jose C PrincipeAbstract:In this paper a non-linear extension to the matched filter is proposed and applied to signal detection. The Decision Statistic used in this novel method is derived from ideas on kernel-based learning theory and in fact, is a generalization of the correlation Statistic used in the matched filter. The optimality of the matched filter is merely based on second order Statistics and hence leaves room for improvement, especially when the assumption of Gaussianity is no longer valid. The proposed method incorporates higher order moments in the Decision Statistic and shows different behavior than the matched filter and improvement in the detection rate for non-Gaussian noise. Moreover, unlike kernel based approaches, this method is still computationally tractable and can easily be implemented in real-time
Rausley A A De Souza - One of the best experts on this subject based on the ideXlab platform.
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circular folding cooperative power spectral density split cancellation algorithm for spectrum sensing
IEEE Communications Letters, 2017Co-Authors: Roberto Bomfin, Rausley A A De Souza, Dayan Adionel GuimaraesAbstract:The cooperative power spectral density split cancellation (CPSC) algorithm for spectrum sensing has low computational complexity and is robust under dynamical noise. We propose a novel circular folding CPSC (CF-CPSC) algorithm that expressively outperforms the original CPSC. Making use of the incomplete regularized Beta function, the cumulative distribution functions of the main random variables that form the Decision Statistic of the CF-CPSC model are derived. It is also derived an expression for the global probability of false alarm of the CF-CPSC, which is particularized to an approximate simple closed form that yields very accurate results. A closed-form expression of the Decision threshold is provided as well. The analytical results are verified by Monte Carlo simulations. Other simulation results are provided to demonstrate the suitability of the CF-CPSC for scenarios of practical interest.
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on the probability of false alarm of the power spectral density split cancellation method
IEEE Wireless Communications Letters, 2016Co-Authors: Roberto Bomfin, Dayan Adionel Guimaraes, Rausley A A De SouzaAbstract:The cooperative power spectral density split cancellation (CPSC) method was recently proposed for cooperative spectrum sensing, with the novelties of being robust against noise uncertainty and having low computational complexity. The probability of false alarm has been derived in the related paper, but under assumptions that make it inaccurate. In this letter, we derive the correct cumulative distribution functions of the main random variables that form the Decision Statistic of the CPSC model. We also derive the correlation coefficient between sub-band Decisions, necessary information that was neglected in the reference paper, leading to an expression for the final probability of false alarm that is more accurate than the original one. Our theoretical results are validated with simulations.
A Chockalingam - One of the best experts on this subject based on the ideXlab platform.
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ber optimal linear parallel interference cancellation for multicarrier ds cdma in rayleigh fading
IEEE Transactions on Communications, 2007Co-Authors: S Manohar, R Annavajjala, V Tikiya, A ChockalingamAbstract:In this paper, we consider the design and bit-error performance analysis of linear parallel interference cancellers (LPIC) for multicarrier (MC) direct-sequence code division multiple access (DS-CDMA) systems. We propose an LPIC scheme where we estimate and cancel the multiple access interference (MAI) based on the soft Decision outputs on individual subcarriers, and the interference cancelled outputs on different subcarriers are combined to form the final Decision Statistic. We scale the MAI estimate on individual subcarriers by a weight before cancellation. In order to choose these weights optimally, we derive exact closed-form expressions for the bit-error rate (BER) at the output of different stages of the LPIC, which we minimize to obtain the optimum weights for the different stages. In addition, using an alternate approach involving the characteristic function of the Decision variable, we derive BER expressions for the weighted LPIC scheme, matched filter (MF) detector, decorrelating detector, and minimum mean square error (MMSE) detector for the considered multicarrier DS-CDMA system. We show that the proposed BER-optimized weighted LPIC scheme performs better than the MF detector and the conventional LPIC scheme (where the weights are taken to be unity), and close to the decorrelating and MMSE detectors.
Xiaodong Wang - One of the best experts on this subject based on the ideXlab platform.
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Fast and Robust Spectrum Sensing via Kolmogorov-Smirnov Test
IEEE Transactions on Communications, 2010Co-Authors: Guowei Zhang, Xiaodong Wang, Ying-chang LiangAbstract:A new approach to spectrum sensing in cognitive radio systems based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some Decision Statistic obtained from the received signal, and comparing it with the ECDF of the channel noise samples. A sequential version of the K-S-based spectrum sensing technique is also proposed. Extensive simulation results demonstrate that compared with the existing spectrum detection methods, such as the energy detector and the eigenvalue-based detector, the proposed K-S detectors offer superior detection performance and faster detection, and is more robust to channel uncertainty and non-Gaussian noise.
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Fast and Robust Modulation Classification via Kolmogorov-Smirnov Test
IEEE Transactions on Communications, 2010Co-Authors: Fanggang Wang, Xiaodong WangAbstract:A new approach to modulation classification based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some Decision Statistic derived from the received signal, and comparing it with the CDFs or the ECDFs of the signal under each candidate modulation format. The K-S-based modulation classifiers are developed for various channels, including the AWGN channel, the flat-fading channel, the OFDM channel, and the channel with unknown phase and frequency offsets, as well as the non-Gaussian noise channel, for both QAM and PSK modulations. Extensive simulation results demonstrate that compared with the traditional cumulant-based classifiers, the proposed K-S classifiers offer superior classification performance, require less number of signal samples (thus is fast), and is more robust to various channel impairments.
