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Signal Processing Advances in Wireless Communications, First IEEE Signal Processing Workshop on

Date 16-18 April 1997

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Displaying Results 1 - 25 of 106
  • First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications

    Publication Year: 1997
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    Freely Available from IEEE
  • Constrained maximum likelihood estimation of time-varying linear channels

    Publication Year: 1997 , Page(s): 1 - 4
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (433 KB)  

    This paper considers the identification of time-varying linear channels using maximum likelihood estimation. The channel is modelled as a tapped-delay line filter with complex coefficients. Due to the complexity of the likelihood function and the large number of parameters to be estimated, an analytical maximization of the likelihood function is infeasible. Therefore, gradient algorithms are considered. We consider the structure of the channel tap covariances, and present a gradient algorithm for finding the constrained maximum likelihood estimates of the channel parameters. View full abstract»

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  • Passband complex fractionally-spaced equalization of MSK signals over the mud pulse telemetry channel

    Publication Year: 1997 , Page(s): 5 - 8
    Cited by:  Papers (1)
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    Mud pulse telemetry is a method used for measurement-while-drilling (MWD) in the oil industry. Information (tool-face orientation, temperature, pressure etc.) is sent from the bottom of the well to the surface by means of pressure waves in the drilling fluid (the drilling mud). It is shown that adaptive equalization of the received MSK pressure waveform is possible using methods commonly used in telephone modems, cellular telephones and acoustic telemetry systems. A passband complex fractionally-spaced decision-feedback equalizer, using a weighted LS algorithm for updating the coefficients, is shown to give good results in reducing signal distortion. View full abstract»

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  • An equalizer including a soft channel decoder

    Publication Year: 1997 , Page(s): 9 - 12
    Cited by:  Papers (7)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (444 KB)  

    This paper deals with the presentation of a joint equalizer/channel decoder for the transmission of a digital modulation, with a convolutional encoder, on a dispersive channel with additive white Gaussian noise. This equalizer directly produces the detection of the emitted sequence of information bits rather than the ones after channel coding, as classically done, i.e., the novelty of this blind equalizer is to include the decoding step directly in the equalizer's structure. View full abstract»

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  • A new form of combined adaptive equalization and decoding for TCM signals over fast time-variant multipath links

    Publication Year: 1997 , Page(s): 13 - 16
    Cited by:  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (329 KB)  

    A novel adaptive coherent receiver for TCM signals transmitted over randomly time-variant frequency-selective fast-fading wireless links is presented. Its main feature is that the three different tasks of channel-estimation, equalization and decoding are jointly carried out in an optimal way. The last two operations are jointly accomplished by a symbol-by-symbol (SbS) Abend-Fritchman like detector (Proakis, 1989) which delivers both hard-decisions and suitably defined soft-output statistics. The latter directly feed a nonlinear Kalman-type estimator for channel tracking. Using the more informative soft-statistics in place of usual hard statistics for channel estimation constitutes the novel attractive feature of the proposed receiver; in this way, the channel estimator is able to fully exploit the coding redundancy so to enhance the tracking capability. View full abstract»

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  • Unconditional maximum likelihood channel estimation and equalisation

    Publication Year: 1997 , Page(s): 17 - 20
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    A novel blind unconditional maximum likelihood algorithm for fractionally-spaced nonminimum phase FIR channel identification and equalisation is presented. The algorithm results from using a low signal to noise approximation to the average of the likelihood function with respect to the transmitted data sequence. The channel estimation equation is derived in a closed form, and the resulting algorithm is computationally efficient since it only requires the calculation of one eigenvector. Simulation results are presented to show the performance of the proposed algorithm. View full abstract»

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  • Second-order cyclic statistics based blind channel identification and equalization

    Publication Year: 1997 , Page(s): 21 - 24
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    Blind channel identification and equalization based on second-order statistics by subspace fitting and linear prediction have received a lot of attention lately. On the other hand, the use of cyclic statistics in fractionally sampled channels has also raised considerable interest. We propose to use these statistics in subspace fitting and linear prediction for (possibly multiuser) channel identification. The main benefit expected is to get rid of the dependence on the color of the additive noise, due to the properties of the cyclocorrelations. We also present some simulations to illustrate the effectiveness of the method. View full abstract»

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  • A new method for blind identification of multipath channel by exploiting signal cyclostationarity

    Publication Year: 1997 , Page(s): 25 - 28
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (387 KB)  

    This paper proposes a new blind identification algorithm for identifying an unknown multipath channel driven by cyclostationary inputs. Like many other blind identification algorithms, this method also exploits only the second-order cyclic statistics of digital modulated signals, but it further makes use of other useful a priori known information such as the baseband pulse shape and the modulation type. In light of the fundamental relationship between the spectral correlation densities (SCD) of channel input and output, we apply some simple techniques of harmonic retrieval, outlier censoring, and combinatorial analysis to determine the number of paths and estimate the parameters, i.e., the path delays and attenuation For different baseband pulse shapes such as rectangular and raise-cosine pulses, some practical discussions on the channel identifiability and resolution performance of the algorithm are included. Simulation results are also provided to demonstrate its performance in the presence of stationary noise and interfering signal. View full abstract»

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  • Unimodal blind adaptive channel equalization: an RLS implementation of the mutually referenced equalizers

    Publication Year: 1997 , Page(s): 29 - 32
    Cited by:  Papers (1)
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    The blind, direct, and adaptive linear equalization of possibly rapidly time varying communication channels is a problem of important theoretical and practical concerns. In this paper a new solution is developed for the equalization of oversampled channels (temporally or spatially) which improves on most existing related contributions. First, the proposed criterion is purely quadratic, hence it ensures global convergence. Then, the algorithm provides a complete set of channel inverses, resulting in robustness properties w.r.t. reconstruction delay problems. Finally, and more importantly, all standard adaptive filtering structures can be used to implement the method. Among these, the RLS-based solution reaches convergence after a hundred iterations only, under certain conditions. View full abstract»

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  • Channel robust blind fractionally-spaced equalization

    Publication Year: 1997 , Page(s): 33 - 36
    Cited by:  Papers (5)
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    We present a new adaptive robust equalization method aimed to overcome the issue of lack of disparity of most recent spatio-temporal diversity based equalization algorithms, and to pick the equalizer minimizing input/output errors in the presence of additive noise. It combines fractionally-spaced equalization by constant modulus algorithm (FSE-CMA) robustness to lack of disparity and delay diversity to find several equalizer settings in different basins of attraction of the FSE-CM cost-function. We prove that the algorithm allows one to choose the best equalizer's output when there is disparity. Moreover, in the worst possible channel case, i.e. when there is such a lack of disparity that even the FSE-CMA fails, some of the equalizers may escape from converging to undesired settings and come very close to MMSE. View full abstract»

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  • On the robustness of the fractionally-spaced constant modulus criterion to channel order undermodeling. I

    Publication Year: 1997 , Page(s): 37 - 40
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (509 KB)  

    This paper studies the robustness properties of the constant modulus (CM) criterion specifically when the fractionally-spaced equalizer time span is less than that of the channel. Hence, there necessarily exists an error in the equalized signal. Noiseless, binary signalling is considered. The change in CM cost from a perfect equalization setting is derived for two cases: (i) perturbations to the channel outside the time span of the equalizer, and (ii) equalizer truncation. This CM cost is related to the mean squared error (MSE) cost and a design guideline for length selection is proposed. This guideline is shown by example to be robust when noisy, multi-level complex signalling is considered. View full abstract»

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  • Blind equalization in the presence of bounded errors with unknown bound

    Publication Year: 1997 , Page(s): 41 - 44
    Cited by:  Papers (1)
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    We consider the blind identification of a FIR channel when the errors on the observations (received signal) are bounded. We show that, using the fact that the input signal is in a finite alphabet, the channel parameters and the bound can be estimated on-line. However, since the input sequence is unknown, the number of estimators to be run in parallel grows exponentially fast. A sub-optimal approach is thus considered, which consists in retaining only the K best candidate sequences for the L/sub /spl infin//-norm. View full abstract»

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  • Channel-surfing re-initialization of CMA

    Publication Year: 1997 , Page(s): 45 - 48
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (308 KB)  

    One of the unsolved problems in applying the constant modulus algorithm (CMA) is the initialization of CMA and the re-initialization when CMA is trapped in an undesirable local minimum. A new re-initialization scheme for CMA is proposed by exploiting the structure of the minimum mean square error (MMSE) equalizer and connections between the constant modulus and the MMSE equalizers. The proposed scheme, referred to as the channel surfing re-initialization (CSR), searches in the channel parameter space for the constant modulus equalizer with minimum mean square error. View full abstract»

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  • Predictive blind equalization with the constant modulus criterion

    Publication Year: 1997 , Page(s): 49 - 52
    Cited by:  Papers (2)
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    This paper resumes recent research on blind equalization based in predictive filter structures. The predictive filter structure is composed by a cascade of an infinite impulse response (IIR) forward prediction error filter and a FIR backward prediction-error filter (also called innovators). This equalizer views the channel as the cascade of a minimum-phase and a maximum-phase systems. The filter structure is optimized with the constant modulus criterion. Simulation results show that in several situations, conventional finite impulse response (FIR) equalizers do not achieve similar convergence properties as those shown by the predictive blind equalizer. View full abstract»

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  • On blind MIMO channel estimation and blind signal separation in unknown additive noise

    Publication Year: 1997 , Page(s): 53 - 56
    Cited by:  Papers (4)
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    The problem of estimating the impulse response function of an FIR multiple-input multiple-output (MIMO) system given only the noisy measurements of the vector output of the system, is considered. The system is assumed to be driven by a spatially and temporally i.i.d. non-Gaussian vector sequence (which is not observed). The problem of blind separation of independent linear signals from their convolutive mixtures also leads to the above mathematical model. The model order is unknown. The FIR N/spl times/M (N/spl ges/M) MIMO transfer function is assumed to have full column rank on the unit circle; there are no other assumptions. Higher-order cumulant matching is used to consistently estimate the MIMO impulse response via nonlinear optimization. For blind signal separation the estimated channel is used to decompose the received signal at each sensor into its independent signal components via a Wiener filter. A recently proposed inverse filter criteria based approach (which yields biased estimates in noise) is used to obtain initialization for the cumulant matching approach. A simulation example is presented to illustrate the two approaches for both channel estimation as well as convolutive signal separation. View full abstract»

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  • Blind source separation using second-order cyclic-statistics

    Publication Year: 1997 , Page(s): 57 - 60
    Cited by:  Papers (6)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (430 KB)  

    This paper addresses the problem of blind separation of cyclostationary sources. By exploiting the cyclostationarity property of the source signals, a new approach based on second-order cyclic statistics is proposed for identifying the parameter matrix and estimating the source signals. The new approach does not impose any restriction on the cyclic frequencies of the signals and yields waveform-preserving estimates of the source signals. Simulation examples are presented to illustrate the effectiveness of this approach. View full abstract»

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  • Blind unitary source separation using a multidimensional phase-locked loop

    Publication Year: 1997 , Page(s): 61 - 64
    Cited by:  Patents (1)
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    Blind source separation cannot generally be performed using second-order statistics alone because of a unitary matrix ambiguity. We present the multidimensional phase-locked loop (MPLL) as a blind algorithm for resolving this ambiguity. The MPLL is a multidimensional generalization of the scalar decision-directed PLL for resolving phase rotations in scalar digital communication systems, and as such is applicable only to discrete-alphabet sources. We compare the MPLL to other known unitary source separation algorithms, and find that the MPLL compares favorably in terms of both performance and complexity. View full abstract»

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  • Maximizing the information transfer for adaptive unsupervised source separation

    Publication Year: 1997 , Page(s): 65 - 68
    Cited by:  Papers (4)
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    The problem of adapting linear multi-input-multi-output systems for unsupervised separation of linear mixtures of sources arises in a number of applications in multiuser wireless communications, such as mobile telephony. In this paper we propose a new statistical criterion to adapt the separating system. It involves the well-known Godard criterion as part of it and is interpreted by information theory as the maximization of information transfer in a single layer nonlinear neural network. The proposed criterion is free from undesirable stationary points provided that the signals to be separated have negative kurtosises, which is the case in communications. View full abstract»

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  • Analysis of the convergence properties of a self-normalized source separation neural network

    Publication Year: 1997 , Page(s): 69 - 72
    Cited by:  Papers (1)
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    An extended source separation neural network was derived by Cichocki et al. (1995) from the classical Herault-Jutten network. It was claimed to have several advantages, but its convergence properties were not described. In this paper, we exhaustively define the equilibrium points of the standard version of this network and analyze their stability. We prove that the stationary independent sources that this network can separate are the globally sub-gaussian signals. As the Herault-Jutten network applies to the same sources, we show that the advantages of the new network are not counterbalanced by a reduced field of application, which confirms its attractiveness. View full abstract»

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  • Stability analysis and optimization of time-domain convolutive source separation algorithms

    Publication Year: 1997 , Page(s): 73 - 76
    Cited by:  Papers (9)
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    In this paper, we investigate the self-adaptive source separation problem for convolutively mixed signals. The proposed approach uses a recurrent structure adapted by a generic rule involving arbitrary separating functions. We first analyze the stability of this generic algorithm and we apply these results to some classical rules that were proposed in the literature but only partly analyzed. We then derive the expression of the asymptotic error variance achieved by this rule (for strictly causal mixtures). This enables us to determine the optimum separating functions that minimize this error variance. They are shown to be only related to the probability density functions of the sources. The performance improvement achieved by this approach is illustrated by simulations performed with real mixtures of speech signals. View full abstract»

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  • Improved TSML algorithm for multichannel blind identification

    Publication Year: 1997 , Page(s): 77 - 80
    Cited by:  Papers (1)
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    In this paper we present a new two-step maximum likelihood (TSML) algorithm for estimating the impulse responses of multiple FIR channels driven by an arbitrary unknown input. In Hua (1996), the original TSML method was developed as a fast alternative to the direct maximum likelihood approach. The TSML method exploits a novel orthogonal complement (OC) matrix of the block Sylvester matrix. Despite its high-SNR (signal-to-noise-ratio) efficiency, the TSML method is still computationally expensive as it requires approximately O(q/sup 3/N/sup 3/) flops, where N is the sample size and q is the number of system outputs. The contribution in this paper consists in introducing a new TSML method which exploits a non-redundant OC matrix whose column vectors are shown to form a basis of the noise subspace. The new TSML method is shown to require only O(q/sup 3/N/sup 2/) flops. Like the original TSML method, the new TSML method requires no initial estimates and is asymptotically (high SNR) optimum. View full abstract»

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  • On cyclic correlation approaches for blind identification of FIR communication channels

    Publication Year: 1997 , Page(s): 81 - 84
    Cited by:  Papers (1)
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    We address the problem of the blind identification of a non-minimum phase FIR communication channel by exploiting the cyclostationarity of the received signal sampled at rate greater of the symbol rate. By explicitely taking into account the (known) color of the additive noise, we extend the approach presented by Giannakis (see Proc. 28th Asilomar Conf. Sig., Sys. and Comp., California, USA, 1994), based on second-order cyclic correlations. Moreover, we propose the use of higher-order cyclic correlations to overcome the lack of channel identifiability when second-order only cyclic correlations are considered. The resulting nonlinear equations are solved using the same numerical techniques commonly adopted in the classical minimum eigenvalue/eigenvector problem. View full abstract»

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  • An adaptive system for direct blind multi-channel equalization

    Publication Year: 1997 , Page(s): 85 - 88
    Cited by:  Papers (10)
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    Blind equalization has important applications to wireless communication and cable HDTV. It is known that the output of the channel bank has sufficient information for blind identification, and hence also for blind equalization It may be surprising to find that the composite output of the equalizer bank indeed has sufficient information for blind equalization, though it might not have sufficient for blind identification. Specially, if the zero-mean transmitted signal is temporally uncorrelated and if the channel bank has no common nonzero zeros, the equalizer bank equalizes the channel bank if, and only if, its composite output is temporally uncorrelated. An adaptive algorithm is introduced in this paper as an implementation of above theorem, and computer simulations demonstrate its fast convergence (less than 100 iterations) and good tracking ability (for simulated 70 mph mobile channels with SNR=15 dB). View full abstract»

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  • Equivariant blind deconvolution of MIMO-FIR channels

    Publication Year: 1997 , Page(s): 89 - 92
    Cited by:  Papers (4)
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    We address the blind identification and deconvolution of multiple input multiple output (MIMO) linear FIR channels. This is an instance of blind separation of convolutive mixtures. The unknown system is decomposed in two factors. The first factor can be deterministically identified from a finite data set; the second factor is shown to belong to a multiplicative group. This last property allows the implementation of effective equivariant deconvolution algorithms, including the maximum likelihood (ML) estimator. View full abstract»

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  • A block algorithm for blind signal deconvolution

    Publication Year: 1997 , Page(s): 93 - 96
    Cited by:  Papers (2)
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    The problem of multi-input/multi-output blind signal deconvolution is considered. In this paper, given an "adapted" definition of the contrast function, some results in Moreau and Tririon (1996) are generalized. In order to optimize the criteria, a gradient based block algorithm is proposed. It does not require parametrization of the lossless final separating filter. Computer simulations are also presented to demonstrate the effectiveness of the method. View full abstract»

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