Impact Of Higher-order Statistics On Adaptive Algorithms For Blind Source Separation

The paper is devoted to present an analysis of the impact of higher order statistics (HOS) in adaptive blind source separation criteria. Despite the well known fact that they are necessary to provide source separation in a general framework, their impact on the performance of adaptive solutions is a still open research field. The approach of probability density function (pdf) recovering is used. In order to verify the analysis, two constrained adaptive algorithms are investigated. Namely, the multiuser kurtosis algorithm (MUK) and the multiuser constrained fitting probability density function algorithm (MU-CFPA) are used due to the desired characteristics of different HOS involved in their design. Simulation results are carried out to basis our analysis. © 2004 IEEE.170174Hyvärinen, A., Oja, E., Independent component analysis: Algorithms and applications (2000) Neural Networks, 13 (4-5), pp. 411-430Hérault, J., Jutten, C., Ans, B., Détection de grandeurs primitives dans un message composite par une architecture de calcul neuromimétique en apprentissage non supervisé (1985) Actes du Xéme Colloque GRETSI, pp. 1017-1022. , Nice, France, MaiPapadias, C.B., Globally convergente blind source separation based on a multiuser kurtosis maximization criterion (2000) IEEE Transactions on Signal Processing, 48 (12), pp. 3508-3519. , DecemberHaykin, S., (2000) Unsupervised Adaptive Filtering, Ser. (Series on Adaptive and Learning Systems for Signal Processing, Communications and Control), 1. , John Wiley & Sons: Source SeparationDonoho, D., (1981) On Minimum Entropy Deconvolution, pp. 565-608. , Academic PressComon, P., Independent component analysis: A new concept? (1994) Signal Processing, 36 (3), pp. 287-314. , AprilPapadias, C.B., Paulraj, A.J., A constant modulus algorithm for multiuser signal separation in presence of delay spread using antenna array (1997) IEEE Signal Processing Letters, 4 (6), pp. 178-181. , JunePapadias, C.B., (2000) Blind Separation of Independent Sources Based on Multiuser Kurtosis Optimization Criteria, 2, pp. 147-179. , John-Wiley & Sons, ch. 4Cavalcante, C.C., Cavalcanti, F.R.P., Mota, J.C.M., Romano, J.M.T., A constrained version of fitting PDF algorithm for blind source separation (2003) Proceeding of IEEE Signal Processing Advances for Wireless Communications (SPAWC 2003), , Rome, Italy, June, 15-18Benveniste, A., Goursat, M., Ruget, G., Robust identification of a nonminimum phase system: Blind adjustment of a linear equalizer in data communications (1980) IEEE Transactions on Automatic Control, AC-25 (3), pp. 385-399. , JuneNadal, J.-P., Parga, N., Redundancy reduction and independent component analysis: Conditions on cumulants and adaptive approaches (1997) Neural Computation, 9, pp. 1421-1456Shalvi, O., Weinstein, E., New criteria for blind deconvolution of nonminimum phase systems (channels) (1990) IEEE Transactions on Information Theory, 36 (2), pp. 312-321. , MarchHaykin, S., (1998) Neural Networks: A Comprehensive Foundation, 2nd Ed., , Prentice HallLacoume, J.-L., Amblard, P.-O., Comon, P., (1997) Statistiques d'Ordre Supérieur pour le Traitement du Signal, , ser. (Traitement du Signal). Paris: MassonCavalcante, C.C., (2001) Neural Prediction and Probability Density Function Estimation Applied to Blind Equalization, , Master's thesis, Federal University of Ceará (UFC), Fortaleza-CE, Brasil, FebruaryCavalcante, C.C., Cavalcanti, F.R.P., Mota, J.C.M., Adaptive blind multiuser separation criterion based on log-likelihood maximisation (2002) IEE Electronics Letters, 38 (20), pp. 1231-1233. , SeptemberHaykin, S., (2000) Unsupervised Adaptive Filtering, , ser. (Series on Adaptive and Learning Systems for Signal Processing, Communications and Control). John Wiley & Sons, vol. II: Blind DeconvolutionCavalcante, C.C., Cavalcanti, F.R.P., Mota, J.C.M., A PDF estimation-based blind criterion for adaptive equalization (2002) Proceedings of IEEE Int. Symposium on Telecommunications (ITS 2002), , Natal, Brazil, SeptemberBishop, C.M., (1995) Neural Networks for Pattern Recognition, , UK: Oxford University Pres

Polynomial Expansion Of The Probability Density Function About Gaussian Mixtures

A polynomial expansion to probability density function (pdf ) approximation about Gaussian mixture densities is proposed in this paper. Using known polynomial series expansions we apply the Parzen estimator to derive an orthonormal basis that is able to represent the characteristics of probability distributions that are not concentrated in the vicinity of the mean point such as the Gaussian pdf. The blind source separation problem is used to illustrate the applicability of the proposal in practical analysis of the dynamics of the recovered data pdf estimation. Simulations are carried out to illustrate the analysis. © 2004 IEEE.163172Cavalcante, C.C., (2004) On Blind Source Separation: Proposals and Analysis of Multi-user Processing Strategies, , Ph.D. thesis, State University of Campinas (UNICAMP) - DECOM, Campinas, SP - Brazil, AprilCavalcante, C.C., Cavalcanti, F.R.P., Mota, J.C.M., Romano, J.M.T., A Constrained version of fitting PDF algorithm for blind source separation (2003) Proceeding of IEEE Signal Processing Advances for Wireless Communications (SPAWC 2003), , Rome, Italy, June, 15-18Haykin, S., (1998) Neural Networks: A Comprehensive Foundation, , Prentice Hall, 2nd ednHyvärinen, A., Oja, E., Karhunen, J., (2001) Independent Component Analysis, , John Wiley & SonsLacoume, J.-L., Amblard, P.-O., Comon, P., (1997) Statistiques D'Ordre Supérieur Pour le Traitement du Signal, , (Traitement du Signal), Paris: MassonLaster, J.D., (1997) Robust GMSK Demodulation Using Demodulator Diversity and BER Estimation, , Ph.D. thesis, Faculty of the Virginia Polytechnic Institute and State University, Blacksburg, Virginia, MarchNikias, C.L., Petropulu, A.P., (1993) Higher-order Spectra Analysis, , Prentice HallPapadias, C.B., (2000) Blind Separation of Independent Sources Based on Multiuser Kurtosis Optimization Criteria, 2, pp. 147-179. , John-Wiley & Sons, chap. 4Papoulis, A., (1991) Probability, Random Variables and Stochastic Processes, , (Electrical & Electronic Engineering Series), McGraw-Hill International, 3rd ednParzen, E., On estimation of a probability density function and mode (1962) The Annals of Mathematical Statistics, 33 (3), pp. 1066-1076. , SeptemberSilverman, B.W., (1986) Density Estimation for Statistics and Data Analysis, , (Monographs on Statistics and Applied Probability), Bristol, Great Britain: Chapman and HallTherrien, C.W., (1992) Discrete Random Signals and Statistical Signal Processing, , (Prentice-Hall Signal Processing Series), Prentice-Hall InternationalWegman, E.J., Nonparametric probability density estimation: I. A summary of available methods (1972) Technometrics, 14 (3), pp. 533-546. , Augus

Sparse Blind Deconvolution Based On Scale Invariant Smoothed 0-norm

In this work, we explore the problem of blind deconvolution in the context of sparse signals. We show that the 0-norm works as a contrast function, if the length of the impulse response of the system is smaller than the shortest distance between two spikes of the input signal. Demonstrating this sufficient condition is our basic theoretical result. However, one of the problems of dealing with the 0-norm in optimization problems is the requirement of exhaustive or combinatorial search methods, since it is a non continuous function. In order to propose an alternative for that, Mohimani et al. (2009) proposed a smoothed and continuous version of the 0-norm. Here, we propose a modification of this criterion in order to make it scale-invariant and, finally, we derive a gradient-based algorithm for the modified criterion. Results with synthetic data suggests that the imposed conditions are sufficient but not strictly necessary.461465Robinson, E.A., (1954) Predictive Decomposition of Time Series with Applications to Seismic Exploration, , Ph. D. Thesis, Massachusetts Institute of Technology, Dept. of GeologyWiggins, R.A., Minimum entropy deconvolution (1978) Geoexploration, 16 (1-2), pp. 21-35Ooe, M., Ulrych, T.J., Minimum entropy deconvolution with an exponential transformation (1979) Geophysical Prospecting, 27, pp. 458-473Rosec, O., Boucher, M., Nsiri, B., Chonavel, T., Blind marine seismic deconvolution using statistical mcmc methods (2003) IEEE Journal of Oceanic Engineering, 28, pp. 502-512Nandi, A.K., Mampel, D., Roscher, B., Blind deconvolution of ultrasonic signal in nondestructive testing applications (1997) IEEE Transactions on Signal Processing, 45, pp. 1382-1390Romano, J.M.T., Attux, R., Cavalcante, C.C., Suyama, R., Unsupervised signal processing: Channel equalization and source separation (2011) CRC PressDonoho, D., (1981) On Minimum Entropy Deconvolution, pp. 656-608. , Academic PressShalvi, O., Weinstein, E., New criteria for blind deconvolution of nonminimun phase systems (channels) (1990) IEEE Transactions on Information Theory, 36, pp. 312-321Mohimani, H., Babaie-Zadeh, M., Jutten, C., A fast approach for overcomplete decomposition based on smoothed 0 norm (2009) IEEE Transactions on Signal Processing, 57, pp. 289-301Donoho, D.L., For most large undetermined systems of equations, the minimal 1-norm near-solution approximates the sparsest near-solution (2006) Communications on Pure and Applied Mathematics, 59, pp. 907-934Douglas, S.C., Amari, S.I., Kung, S.Y., On gradient adaptation with unit-norm constraints (2000) IEEE Transactions on Signal Processing, 48, pp. 1843-184

Blind Channel Shortening For Simo Space-time Channels Using Linear Prediction In Ofdm Systems

In this paper, we make use of a blind adaptive linear predictor for channel shortening in single input multiple output (SIMO) channels. We compare our approach to the so-called MERRY blind channel shortener. We assess through simulations that our proposed approach provides faster convergence rate and it better exploits the spatio-temporal diversity present in the SIMO channels. © 2006 SBrT.783788Haykin, S., (1994) Blind Deconvolution, , Prentice-HallBalakrishnan, J., Martin, R.K., Johnson Jr., C.R., Blind, adaptive channel shortening by sum-squared auto-correlation minimumization (SAM) (2003) IEEE Transactions on Signal Processing, 51 (12), pp. 3086-3093. , DecMartin, R.K., Balakrishnan, J., Sethares, W.A., Johnson Jr., C.R., Blind, adaptive channel shortening for multicarrier systems (2002) Proc. IEEE Asilomar Conf. Signals, Syst., Comput, , Pacific Grove, CA, NovMartin, R.K., Johnson Jr., C.R., Adaptive equalization: Transitioning from single-carrier to multicarrier systems (2005) Signal Processing Magazine, 22 (6). , NovGardner, W.A., Exploitation of Spectral Redundancy in Cyclostationary Signals (1991) IEEE Signal Processing Magazine, 8, pp. 14-36. , AprilPapadias, C.B., Slock, D.T.M., Fractionally Spaced Equalization of Linear Polyphase Channels and Related Blind Techniques Based on Linear Prediction (1999) IEEE Transactions on Signal Processing, 47 (3), pp. 641-654. , MarchCastro, M.S., Romano, J.M.T., Adaptive Approaches for Blind Equalization Based on Multichannel Linear Prediction (2002) International Telecommunications Symposium, , Natal, BrasilArslan, G., Evans, B.L., Kiaei, S., Equalization for discrete multitone transceivers to maximize bit rate (2001) IEEE Transactions on Signal Processing, 49 (12), pp. 3123-3135. , DecMartin, R.K., Walsh, J.M., Johnson Jr., C.R., Low complexity MIMO blind, adaptive channel shortening (2005) IEEE Trans. Signal Processing, 53 (4), pp. 1324-1334. , AprMiyajima, T., Zhi, D., Second-order statistical approaches to channel shortening in multicarrier systems (2004) IEEE Transactions on Signal Processing, 52 (11), pp. 3253-3264. , NovBartolome, D., Perez-Neira, A.I., MMSE techniques for space diversity receivers in OFDM-based wireless LANs (2003) Selected Areas in Communications, IEEE Journal on, 21 (2), pp. 151-160. , Fe

Low Cost Smart Antenna Array Hardware Implementation

This paper presents and describes a low-cost hardware implementation of smart antenna based on FPGA. Unlike systems using high-cost devices, the case herein uses a low-end FPGA for beamforming algorithm implementation, enabling smart antenna applications in practical and commercial problems, as for instance interference mitigation. The low-cost smart antenna array underwent two different types of tests, solving the beamforming problem by a non-adaptive algorithm and by a blind adaptive one. The desired beamforming is reached in both cases and interference mitigation is achieved in the second one. © 2011 IEEE.784788Rappaport, T.S., (1996) Wireless Communications: Principles and Practice, , Prentice HallBarton, P., Digital Beam Forming for Radar (1980) Proc. of IEE, 127 (4), pp. 266-277. , AugustRayleight, G.G., Cioffi, J.M., Spatio-temporal coding of wireless communication (1998) IEEE Trans. on Communications, 46, pp. 357-366. , MarchAhmad, F., Zhang, Y., Amin, M.G., Three Dimensional Wideband Beamforming for Imaging Through a Single Wall (2008) IEEE Geoscience and Remote Sensing Letters, 5 (2), pp. 176-179. , AprilSmolders, B., Hampson, G., Deterministic RF Nulling in Phased Arrays for Next Generation of Radio Telescopes (2002) IEEE Ant. Prop., 44 (4), pp. 13-22. , AugustTenCate, J.A., Beamforming on Seismic Interface Waves with an Array of Geophones on the Swallow Sea Floor (1995) IEEE Journal of Oceanic Eng., 20 (4), pp. 300-310. , OctoberBond, E.J., Microwave Imaging via Space-Time Beamforming for Early Detection of Breast Cancer (2003) IEEE Trans. on Ant. Prop., 51 (8), pp. 1690-1705. , AugustGeiser, A., Smart Antenna Terminals for Broadband Mobile Satellite Communications at Ka-Band 2 nd Int. ITG Conf. on Antennas, March 2007Nuteson, T.W., Performance Characterization of FPGA Techniques for Calibration and Beamforming in Smart Antenna Applications (2002) IEEE Trans. on Microwave Theory and Techniques, 50 (2), pp. 3043-3051. , DecemberCapon, J., High Resolution Frequency-Wavenumber Spectrum Analysis (1969) Proccedings of IEEE, 57 (8), pp. 1408-1418. , AugustGriffths, L.J., Jim, C.W., An Alternative Approach to Linearly Constrained Adaptive Beamforming (1982) IEEE Trans. on Antennas and Propagation, AP-30, pp. 27-34. , JanuarySchimidt, R.O., Multiple Emitter Location and Signal Parameter Estimation Proc. of RADC Spectrum Estimation Workshop, Griffiss Air Force Base, NY, 1979Roy, R., Kailath, T., ESPRIT - Estimation of Signal Parameters Via Rotational Invariance Techniques (1989) IEEE Trans. on Acoustics, Speech and Signal Processing, 37 (7). , JulyGoddard, D.N., Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication System (1980) IEEE Trans. on Communications, COM-28 (11). , NovemberTreichler, J.R., Agee, B.G., New Approach to Multipath Correction of Constant Modulus Signals (1983) IEEE Trans. on Acoustics, Speech and Signal Processing, ASSP-31 (2), pp. 459-471. , AprilAvizienis, A., Signed-Digit Number Representations for Fast Parallel Arithmetic (1961) IRE Trans. on Electronic Computing, 10, pp. 389-400Chinatto, A., (2011) Widely Linear Processing in Antenna Arrays: Proposal, Evaluation and Practical Implementation of Algorithms, , Master Thesis, School of Electrical and Computer Engineering Unicamp, February in Portugues

Enhancing Dsss-signals Channel Estimation Through Compressive Sensing

A new approach for channel estimation, based on the concept of compressive sensing is presented for situations where signals are DSSS transmitted. Compressive sensing encompasses elegant mathematical background where the information of the sparse characteristic of the signal is used to create an efficient representation of it using fewer measurements than required by the Shannon-Nyquist criterion. The reconstruction is performed through linear programming or greedy algorithms. In this paper, we demonstrate that compressive sensing can enable reduction in the analog-to-digital sampling rate, allowing the design of simpler and cheaper receivers, while keeping or improving resolution in the DSSS channel estimation.Donoho, D., Compressed sensing (2006) IEEE Trans. Info. Theory, 52 (4), pp. 1289-1306. , SepCandès, E., Tao, T., Near optimal signal recovery from random projections: Universal encoding strategies? (2006) IEEE Trans. Info. Theory, 52 (12), pp. 5406-5425. , DecMallat, S., Zhang, Z., Matching pursuit with time-frequency dictionaries (1993) IEEE Trans. Signal Proc., 41 (12), pp. 3397-3415. , DecChen, S., Donoho, D.L., Saunders, M., Atomic decomposition by basis pursuit (1998) SIAM J. Sci. Computing, 20 (1), pp. 33-61Boyd, S., Vanderberghe, L., (2004) Convex Optimization, , Cambridge Univ. PressFoucart, S., A note on guaranteed sparse recovery via l1 minimization (2010) Appl. Comput. Harmon. Anal., 29 (1), pp. 97-103Needell, D., Tropp, J.A., CoSaMP: Iterative signal recovery from incomplete and inaccurate samples (2008) Appl. Comput. Harmon. Anal., 26 (3), pp. 301-321. , MayCandès, E., Tao, T., Decoding by linear programming (2005) IEEE Trans. Info. Theory, 51 (12), pp. 4203-4215Candès, E., Romberg, J., Tao, T., Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information (2006) IEEE Trans. Info. Theory, 52 (2), pp. 489-509Baraniuk, R., Davenport, M., Devore, R., Wakin, M., A simplied proof of the restricted isometry property for random matrices (aka the Johnson-Lindenstrauss lemma meets compressed sensing) (2007) Constructive ApproximationDuarte, M., Eldar, Y., Structured compressed sensing: From theory to applications (2011) IEEE Trans. Sig. Proc., 59 (9), pp. 4053-4085. , SepDohono, D., Elad, M., Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization (2003) Proc. Nat. Acad. Sci., 100 (5), pp. 2197-2202. , MarCandès, E., Romberg, J., Sparsity and incoherence in compressive sampling (2007) Inverse Problems, 23 (3), pp. 969-985Gershgorin, S.A., Über die abgrenzung der eigenwerte einer matrix (1931) Izv. Akad. Nauk SSSR Ser. Fiz-Mat., 6, pp. 749-754Cai, T.T., Xu, G., Zhang, J., On recovery of sparse signals via minimization (2009) IEEE Trans. Info. Theory, 55 (7), pp. 3388-3397. , JulDevore, R.A., Deterministic constructions of compressed sensing matrices (2007) J. Complex, 23 (4), pp. 918-925. , AugDonoho, D.L., Tamer, J., Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing (2009) Phil. Trans. Royal Soc. A, 367 (1906), pp. 4273-4293. , NovDossal, C., Peyré, G., Fadili, J., A numerical exploration of compressed sampling recovery (2010) Linear Algebra and Its Applications, 432 (7), pp. 1663-1679. , MarSkolnik, M., (1970) Radar Handbook, , New York, NY, USA, McGraw HillPickholtz, R.L., Schilling, D.L., Milstein, L.B., Theory of spreadspectrum communications-A tutorial (1982) IEEE Trans. on Comm., 30 (5). , MayParkinson, B.W., Spilker, J.J., Global positioning system: Theory and applications (1996) Progress in Astronautics and Aeronautics, 163. , USEBaraniuk, R., Steeghs, P., Compressive radar imaging (2007) IEEE Radar Conf., pp. 128-133. , AprPirkl, M., Holpp, W., From research to application: How phased array radars conquered the real world (2013) Int. Radar Symp., pp. 17-2

A Novel Approach For Spectral Analysis Of Monitored Power Systems

This paper presents a novel approach that combines the adaptive and non-adaptive notch filters, multilayer perceptron neural network, and warped discrete Fourier transform (WDFT) to estimate amplitudes, frequencies and phases of the fundamental and harmonic components in power systems. Simulation results show that only 1.25 cycles of the fundamental component are enough to provide small frequency, phase and amplitude error estimations. ©2004 IEEE.255259Arrillaga, J., Bollen, M.H.J., Watson, N.R., Power quality following deregulation (2000) Proceedings of the IEEE, 88 (2), pp. 246-261. , FebMelhorn, C., McGranaghan, M.F., Interpretation and analysis of power quality measurements (1995) IEEE Trans. on Industry Applications, 31 (6), pp. 1363-1370. , Nov./DecBollen, M.H.J., (2000) Power Quality Problems: Voltage Sags and Interruptions, , IEEE Press, NJ(1995) IEEE Recommended Practice for Monitoring Electric Power Quality, pp. 1159-1995. , IEEE StandardKhan, A.K., Monitoring power for the future (2001) Power Engineering Journal, 15 (2), pp. 81-85. , AprRibeiro, M.V., Duque, C.A., Romano, J.M.T., An improved method for signal processing and compression in power quality evaluation (2004) IEEE Trans. on Power Delivery, 19 (2), pp. 464-471. , AprWon, D.-J., Chung, I.-Y., Kim, J.-M., Moon, S.-I., Seo, J.-C., Choe, J.-W., Development of power quality monitoring system with central processing scheme (2002) Proc. IEEE Power Engineering Society Summer Meeting, pp. 915-919. , JulGhartemani, M.K., Iravani, M.R., A signal processing module for power system applications (2003) IEEE Trans. on Power Delivery, 18 (4), pp. 1118-1126. , OctRoutray, A., Pradhan, A.K., Rao, K.P., A novel Kalman filter for frequency estimation of distorted signals in power systems (2002) IEEE Trans. on Instr. and Measurement, 51 (3), pp. 469-479. , JunGharieb, R.R., Higher order statistics based IIR notch filtering scheme for enhancing sinusoids in coloured noise (2000) IEE Proc.-vis. Image Signal Processing, 147 (2), pp. 115-121. , AprFranz, S., Mitra, S.K., Doblinger, G., Frequency estimation using warped discrete fourier transform (2003) Signal Processing, 83, pp. 1661-1671Mitra, S.K., (2001) Digital Signal Processing: A Computer-based Approach, 2nd Edition, , McGraw-HillMarkur, A., Mitra, S.K., Warped discrete Fourier transform: Theory and applications (2001) IEEE Trans. Circuits and Systems-1: Fundamental Theory and Applications, 48 (9), pp. 1086-1093. , SeptMitra, S.K., Bagchi, S., (1999) The Nonuniform Discrete Fourier Transform and Its Applications in Signal Processing, , Kluwer Academic PublishersMoore, P.J., Português, I.E., The influence of personal computer processing mode on line current harmonics (2003) IEEE Trans. on Power Delivery, 18 (4), pp. 1363-1587. , OctRibeiro, M.V., Barbedo, J.G.A., Romano, J.M.T., Lopes, A., Fourier-lapped-multilayer perceptron (FLMLP) method for speech quality assessment (2003) EURASIP Journal on Applied Signal Processing, Special Issue on Anthropomorphic Processing of Audio and Speech, , submitte

Multiuser Processing Using Blind Source Separation Methods

Multiuser Processing refers to a set of fundamental techniques for improving the performance of the modern wireless communications systems. Most of these techniques have been proposed and studied for specific scenarios, according to the multiple access technology to be employed. The theory and methods of blind source separation (BSS) can provide a more general framework over which multiuser techniques may be investigated and analysed as a particular case. This is the main idea to be exploited along the present paper. We show how this point of view opens perspectives to deal with important problems of multiuser systems such as signal detection and collision resolution. For this task, an information-theoretic based criterion and algorithm, issued from BSS theory, is presented together with an algorithm for estimation of the number of active users in the system. Simulation results are carried out to illustrate the performance of the designed approach, compared to other blind and trained solutions. Copyright © 2008 John Wiley & Sons, Ltd.197827836Paulraj, A.J., Gore, D.A., Nabar, R.U., Bölcskei, H., An overview of MIMO communications - a key to gigabit wireless (2004) Proceedings of the IEEE, 92 (2), pp. 198-218Cavalcante, C.C., (2004) On blind source separation: Proposals and analysis of multi-user processing strategies, , PhD Thesis, State University of Campinas (UNICAMP)-DECOM, Campinas, SP-Brazil, AprilPapadias, C.B., (2000) Blind Separation of Independent Sources Based on Multiuser Kurtosis Optimization Criteria, 2, pp. 147-179. , chapter 4. John-Wiley & Sons: New York, NY, USACavalcante, C.C., Cavalcanti, F.R.P., Mota, J.C.M., Adaptive blind multiuser separation criterion based on log-likelihood maximisation (2002) IEE Electronics Letters, 38 (20), pp. 1231-1233Cover, T.M., Thomas, J.A., (2006) Elements of Information Theory, , 2nd edn, John Wiley & Sons: Hoboken, NJ, USACavalcante, C.C., Romano, J.M.T., Multi-user pdf estimation based criteria for adaptive blind separation of discrete sources (2005) Signal Processing, 85 (5), pp. 1059-1072Papadias, C.B., Paulraj, A.J., A constant modulus algorithm for multiuser signal separation in presence of delay spread using antenna array (1997) IEEE Signal Processing Letters, 4 (6), pp. 178-181Cavalcanti, F.R.P., Romano, J.M.T., Blind multiuser detection in space division multiple access systems (1999) Annales des Télécommunications, 54 (7-8), pp. 411-419van Trees, H.L., (2002) Optimum Array Processing, , Wiley-Interscience: New York, NY, USAJiang, L., Cai, P., ling Wand, Y., Xu, D., A new source number estimation method based on the beam eigenvalue (2007) Journal of Marine Science and Application, 6 (1), pp. 41-46Paraschiv-Ionescu, A., Jutten, C., Bouvier, G., Source separation based processing for integrated hall sensor arrays (2002) IEEE Sensors Journal, 2 (6), pp. 663-673Chen, P., Wicks, M.C., Adve, R.S., Development of a statistical procedure for detecting the number of signals in a radar measurement (2001) IEE Proceedings-Radar, Sonar and Navigation, 148 (4), pp. 219-226Golub, G.H., Loan, C.F.V., (1996) Matrix Computations, , 3rd edn, The Johns Hopkins University Press: Baltimore, USACarleial, A.B., Hellman, M.E., Bistable behavior of ALOHA-type systems (1975) IEEE Transactions on Communications, 23, pp. 410-440Ghez, S., Verdú, S., Schwartz, S., Optimal decentralised control in the random access multipacket channel (1989) IEEE Transactions on Automatic Control, 34 (11), pp. 1153-1163Ward, J., Compton Jr., R.T., High throughput slotted ALOHA packet radio networks with adaptive arrays (1993) IEEE Transactions on Communications, 41 (3), pp. 460-470Cavalcanti, F.R.P., Romano, J.M.T., Using adaptive array for collisiFabdon resolution in slotted ALOHA packet radio systems (2000) Journal of Communications and Networks, 2 (4), pp. 344-35

Multiple Contribution Travel-time Equation In The Mcg Domain

Multiple attenuation is a crucial phase in the processing of marine seismic data. In specific, free surface multiples (waterair interface) that reflect from near water bottom layers are so strong that they mask reflections from lower geological layers. Surface Related Multiple Elimination (SRME) process is composed of two steps: prediction and adaptive subtraction. This paper deals with the prediction step. We analyze the signals that are registered on the Multiple Contribution Gather (MCG) domain and how they can be processed in order to obtain better prediction results. The better the prediction, the better the adaptation and subtraction. We suggest an approximation to the Multiple Contribution Traveltime (MCT) equation in the MCG domain. We use this equation to correct and filter the signals on the MCG domain.136140Dedem, E.J.V., Verschuur, D.J., 3D surface-related multiple prediction: A sparse inversion approach (2005) Geophysics, 70 (3), pp. V31. , ISSN 00168033 doi:10. 1190/1. 1925752Donno, D., Chauris, H., 2D multiple prediction in the curvelet domain (2010) EAGE-European Association of Geoscientists & Engineers, , JuneVerschuur, D.J., Seismic multiple removal techniques: Past, present and future (2006) Book, (8). , ISSN 1537-6613Yilmaz, O., Seismic data analysis: Processing, inversion and interpretation of seismic data, invest (2001) Society of Exploration Geophysicists, , ISBN 978156080098

Adding Diversity To Multi-user Downlink Beamforming By Using Ber Constraints

In this work, we propose a novel structure and criterion for joint transmit diversity and multi-user downlink beamforming. The proposed structure can be seen as an extension of the purely spatial downlink beamforming and is a transformation that, for each user, transforms K real antennas into L virtual antennas, with L ≤ K. Classical transmit diversity is then applied to these virtual antennas. We propose a design criterion to optimize the precoders in order to find the best trade-off between minimizing the interference generated towards the other users and maximizing the channel diversity for the desired user. This criterion is based on the minimization of the total transmit power subjected to a target BER constraint for each user. Applied to a multiple-antenna transmitter, the proposed technique outperforms the purely-spatial downlink beamformer by about 5 dB for a target BER of 10 -2 and by 9 dB for 10-3 in a simple scenario. © 2006 IEEE.672677Rappaport, T.S., (2001) Wireless Communications: Principles & Practice, , 2nd ed. Prentice HallRashid-Farrokhi, F., Liu, K.J.R., Tassiulas, L., Transmit Beamforming and Power Control for Cellular Wireless Systems (1998) IEEE Journal on Sel. Areas in Communications, 16 (8), pp. 1437-1450. , OctoberZanatta Filho, D., Féty, L., A Fast Algorithm for Joint Downlink Beamforming and Power Control in UMTS Systems (2002) Proc. IEEE International Telecommunications Symposium (ITS'2002), , Natal, Brazil, SeptemberAlamouti, S.M., A simple transmit diversity technique for wireless communications (1998) IEEE Journal on Select Areas in Communications, 16 (8), pp. 1451-1458. , OctoberTarokh, V., Seshadri, N., Calderbank, A.R., Space-time codes for high data rate wireless communications: Performance criterion and code construction (1998) IEEE Transactions on Information Theory, 44 (2), pp. 744-765. , MarchTarokh, V., Jafarkhani, H., Calderbank, A.R., Space-Time Block Codes from Orthogonal Designs (1999) IEEE Transactions on Information Theory, 45 (5), pp. 1456-1467. , JulySeshadri, N., Winters, J.H., Two signaling schemes for improving the error performance of frequency-division-duplex (FDD) transmissions systems using transmitter antenna diversity (1993) Proc. IEEE Vehicular Technology Conference, pp. 508-511. , Secausus, USA, MayZhou, S., Giannakis, G.B., Optimal Transmitter Eigen-Beamforming and Space-Time Block Coding Based on Channel Correlations (2003) IEEE Transactions on Information Theory, 49 (7), pp. 1673-1690. , JulyZanatta Filho, D., Féty, L., Joint Transmit Diversity and Downlink Beamforming by using a Minimum BER Criterion (2006) Proc. VI International Telecommunications Symposium (ITS'2006), , Fortaleza, Brazil, SeptemberYacoub, M.D., (1993) Foundations of mobile radio engineering, , Boca Raton: CRC PressProakis, J.G., (1989) Digital Communications, , 2nd ed. McGraw-HillHaykin, S., (1996) Adaptive Filter Theory, , 3rd ed. Prentice HallGolub, G.H., van Loan, C.F., (1996) Matrix Computations, , 3rd ed. The Johns Hopkins University Pres