Abstract: Session SAM-5

SAM-5.1	Criteria for Direct Blind Deconvolution of MIMO FIR Systems Driven by White Source Signals Yujiro Inouye (Department of Electronic and Control Systems Engineering, Shimane University, 1060 Nishikawatsu, Matsue, Shimane 690-8504, Japan), Ruey-wen Liu (Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA) This paper addresses the blind deconvolution of multi-input-multi-output (MIMO) FIR systems driven by white non-Gaussian source signals. First, we present a weaker condition on source signals than the so-called i.i.d. condition so that blind deconvolution is possible. Then, under this condition, we provide a necessary and sufficient condition for blind deconvolution of MIMO FIR systems. Finally, based on this result, we propose two maximization criteria for blind deconvolution of MIMO FIR systems. These criteria are simple enough to be implemented by adaptive algorithms.
SAM-5.2	A New Method for Blind Source Separation of Nonstationary Signals Douglas L Jones (University of Illinois at Urbana-Champaign) Many algorithms for blind source separation have been introduced in the past few years, most of which assume statistically stationary sources. In many applications, such as separation of speech or fading communications signals, the sources are nonstationary. We present a new adaptive algorithm for blind source separation of nonstationary signals which relies only on the nonstationary nature of the sources to achieve separation. The algorithm is an efficient, on-line, stochastic gradient update based on minimizing the average squared cross-output-channel-correlations along with deviation from unity average energy in each output channel. Advantages of this algorithm over existing methods include increased computational efficiency, a simple on-line, adaptive implementation requiring only multiplications and additions, and the ability to blindly separate nonstationary sources regardless of their detailed statistical structure.
SAM-5.3	A Channel Order Independent Method for Blind Equalization of MIMO Systems João M F Xavier, Victor A N Barroso (Instituto Superior Técnico - Instituto de Sistemas e Robótica) We study blind equalization of noisy MIMO-FIR systems driven by white sources. We present a new second order statistics based approach which does not require the knowledge of the channel order. This technique blindly transforms a convolutive mixture of users into an instantaneous one. Thus, in the special case of a single user (SIMO systems), an estimate of the input signal is readily obtained. Computer simulations results illustrate the promising performance of the proposed technique. We compare our method with the multistep prediction approach (SIMO systems), and evaluate the algorithm capability in globally nulling the intersymbol interference for MIMO systems.
SAM-5.4	Blind knowledge based algorithms based on second order statistics Lisa Perros-Meilhac, Pierre Duhamel (ENST/TSI), Pascal Chevalier (Thomson-CSF Communications), Eric Moulines (ENST/TSI) Most second order Single Input Multiple Output (SIMO) identification algorithms identify the global impulse channel response, convolution of an emission filter and a propagation channel. This paper makes an explicit use of this channel structure in a second order algorithm. We present several stuctured methods exploiting more or less prior informations on the emission filter. Proofs of convergence are provided, and simulations show that some knowledge based algorithms greatly improve over classical blind algorithms, even in the case where the knowledge is partial.
SAM-5.5	A Schur Method for Multiuser Multichannel Blind Identification Luc Deneire, Dirk T.M. Slock (Eurecom Institute) We address the problem of blind multiuser multichannel identification in a Spatial Division Multiple Access (S.D.M.A.) context. Using a stochastic model for the input symbols and only second order statistics, we develop a simple algorithm, based on the Generalized Schur algorithm to apply LDU decomposition of the covariance matrix of the received data. We show that this method leads to identification of the channel, up to a unitary mixture matrix. Furthermore, the identification algorithm is shown to be robust to channel length overestimation and approaches the performance of the Weighted Linear Prediction (WLP) method, at low computational cost.
SAM-5.6	A Convolutive Source Separation Method with Self-optimizing Non-linearities Nabil Charkani (Philips Consumer Communications (PCC), Advanced Development, Route d'Angers, 72081 Le Mans Cedex 9, France.), Yannick Deville (Laboratoire d'Acoustique, de Metrologie, d'Instrumentation (LAMI), Universite Paul Sabatier, 38 Rue des 36 Ponts, 31400 Toulouse, France.) This paper deals with the separation of two convolutively mixed signals. The proposed approach uses a recurrent structure adapted by a generic rule involving arbitrary separating functions. These functions should ideally be set so as to minimize the asymptotic error variance of the structure. However, these optimal functions are often unknown in practice. The proposed alternative is based on a self-adaptive (sub-)optimization of the separating functions, performed by estimating the projection of the optimal functions on a predefined set of elementary functions. The equilibrium and stability conditions of this rule and its asymptotic error variance are studied. Simulations are performed for real mixtures of speech signals. They show that the proposed approach yields much better performance than classical rules.
SAM-5.7	BLIND CLOSED-FORM ARRAY RESPONSE ESTIMATION IN WIRELESS COMMUNICATION Liang Jin, Min-li Yao, Qin-ye Yin (Xi'an Jiaotong University) In this paper, a closed-form array response estimation (CARE) technique for blind source separation in wireless communication is developed. By exploiting the data structure of second-order statistics of the array output in the presence of multipath, we construct a signature matrix in such away that its eigenvectors corresponding to none-zero eigenvalues are just the array response vectors. Thus a closed-form solution of array response can be obtained by eigrn-decomposition. The theoretical analysis and the simulations show that the proposed method achieves array response estimation with little constraint on signal property and propagation environment such as scatters or angular spread. Moreover, the array considered here can be of arbitrary geometry and even uncalibrated.
SAM-5.8	Adaptive blind source separation by second order statistics and naturalgradient Yong Xiang, Karim Abed-Meraim, Yingbo Hua (University of Melbourne) Separation of sources that are mixed by a unknown (hence, "blind") mixing matrix is an important task for a wide range of applications. This paper presents an adaptive blind source separation method using second order statistics (SOS) and natural gradient. The SOS of observed data is shown to be sufficient for separating mutually uncorrelated sources provided that the temporal coherences of all sources are independent of each other. By applying the natural gradient, new adaptive algorithms are derived that have a number of attractive properties such as invariance of asymptotical performance (with respect to the mixing matrix) and guaranteed local stability. Simulations suggest that the new algorithms can outperform some of the best existing ones.
SAM-5.9	Weighted ACMA Alle-Jan van der Veen (Delft University of Technology) The analytical constant modulus algorithm (ACMA) is a deterministic array processing algorithm to separate sources based on their constant modulus. It has been derived without detailed regard to noise processing. In particular, the estimates of the beamformer are known to be asymptotically biased. In the present paper, we investigate this bias, and obtain a straightforward weighting scheme that will whiten the noise and remove the bias. This leads to improved performance for larger data sets.
SAM-5.10	A COMBINED KALMAN FILTER AND CONSTANT MODULUS ALGORITHM BEAMFORMER FOR FAST-FADING CHANNELS WANCHALEAM PORA, JONATHON A CHAMBERS, ANTHONY G CONSTANTINIDES (DEPT. OF ELECTRICAL & ELECTRONIC ENGINEERING, IMPERIAL COLLEGE, LONDON, SW7 2BT, UK) Beamformers which use only the Constant Modulus Algorithm (CMA) are unable to track properly time-variant signals in fast-fading channels. The Kalman Filter (KF), however, has significant advantage in time-varying channels but needs a training sequence to operate. A combined CMA and KF algorithm is therefore proposed in order to utilise the advantages of both algorithms. The associated stepsize of the combination is also varied in accordance with the magnitude of the output. Simulations are presented to demonstrate the potential of this new approach.

< SAM-4

SAM-6 >

Last Update:  February 4, 1999         Ingo Höntsch