Alien noise in the vectored very-high-speed digital subscriber line (VDSL) system is part of the additive noise at the receiver and exhibits strong correlation among users. We present a per-tone co-operative alien noise cancellation (CoMAC) algorithm for the upstream (US) VDSL that can be applied subsequent to any self far-end-crosstalk (FEXT) mitigation strategy. CoMAC operates by predicting the noise seen by a given user based on the error samples from the remaining users. These errors are conveniently obtained after slicing the self-FEXT canceled signal of all the vectored users. We show that if the estimation of these errors is accurate, the proposed alien canceler achieves the Cramer-Rao lower bound (CRLB). In practice, the seamless rate adaptation (SRA) operation, which enables increased bit rate by increasing the bit-loading per-tone, can cause decision errors in any decision directed strategy. We also analyze the impact of these decision errors - an issue not addressed in the literature. We propose a strategy for bit-loading during the SRA operation by formulating a max-min optimization problem and demonstrate a possibility of a guaranteed (minimum) improvement in the per-user rate. Simulations indicate that performance of the algorithm can exceed the minimum value significantly in practical situations.

We present cooperative MIMO for alien noise cancellation (CoMAC): a per-tone, blind, low-complexity, linear, and adaptive noise whitening algorithm for alien crosstalk mitigation in upstream vectored VDSL systems. CoMAC directly acts on the residual errors of the vectored users after self-FEXT cancellation and frequency domain equalization, and thus, leverages the inherent alien-crosstalk-induced spatial correlation across users. CoMAC employs a low-complexity recursion scheme derived from the optimal MMSE noise whitener to non-disruptively initialize, engage, and adapt the noise canceller while the vectored users operate in data mode. Assuming reliable transmit symbol estimation at its input, we show that CoMAC achieves the Cramer-Rao lower bound. Further, the SNR improvements accruing from CoMAC can be translated into substantial rate improvements for upstream vectored VDSL.

The present paper proposes two original approaches to reduce computational resources necessary for expanding survivor paths while searching in a K-best manner the MIMO detection tree. The first approach involves precomputing some products that are so far recomputed for each path expansion. The second technique is a new method to compute the path metrics. In all K-best detection techniques proposed so far, the complexity necessary to expand a path grows with the path depth in the tree. The originality of our approach stems from the fact that the path expansion complexity decreases with the path depth. Since the number of expanded paths increases with their depth, our approach better balances the path expansion complexity and the number of expanded paths at a given depth, which can yield a significant complexity reduction. We then present a new K-best Hard-Output Lattice Decoding (K-HOLD) algorithm that combines both proposed techniques. A complexity analysis shows that K-HOLD can reduce by up to 70% the overall path expansion complexity as compared to the less-complex known algorithms. This advantage comes about at no cost in terms of performance degradation.

We develop an analytical framework to evaluate the probability-of-miss and probability-of-false-alarm performance of an impulse noise (IN) detection algorithm for orthogonal frequency-domain multiplexing (OFDM) symbols based on monitoring the post-frequency-domain-equalizer (FEQ) instantaneous error at the output of the QAM constellation demapper over a range of tones (sub-carriers). Using the VDSL system as a reference, we further analyze the impact of impulse-noise detection error events at the scale of the OFDM symbol (miss and false alarm) on the bit-error-rate (BER) performance of interleaved Reed-Solomon (RS) codes with erasure-decoding. The analysis culminates with a sequence of steps to design the parameters of the frequency-domain IN detector based on the design BER of the communication system and practical transceiver features such as noise-margin and seamless-rate-adaptation (SRA). We conclude that a tone-error based frequency-domain IN detector monitoring 100 tones is capable of providing IN detection performance that does not cause a deterioration of the average BER below the VDSL system design BER of 10-7when interleaved RS coding with erasure-decoding is employed.

This paper introduces a new family of per frequency linear pre-coders, called off-diagonal MIMO pre-coders (ODMP), for discrete multi-tone (DMT) based digital subscriber line (DSL) systems. ODMP target pre-compensation of the off- diagonal self far-end cross talk (FEXT) terms in the downstream DSL-MIMO channel matrix. We derive a low complexity and adaptive ODMP that simultaneously maximizes the Shannon Capacities per tone, experienced by the (non-cooperative) end-users. The updating scheme makes use of the error samples computed in each receiver. No matrix inversion is required in the algorithm which may be used for learning, tracking, and incrementing the ODMP without any disruption of data service. Finite precision simulation results reveal that coding the complex error with 16 bits allows for the algorithm to reach "self-FEXT free" performance after only 150 iterations in the learning phase and 450 iterations in the incrementing phase.

This paper presents two theoretical upper-bounds on the word and bit error probabilities of individual levels of multilevel coded modulation schemes with equal error protection and multistage decoding. Lattice-type or PSK modulation with Ungerboeck partitioning over an AWGN channel is assumed. Inspired by both Imai's [1] and Wachsmann's [2] channel models, a new model is introduced, yielding the expression of two upperbounds tighter than those known so far [3][4]. The tightness of the bounds is illustrated with simulation results and compared to existing bounds. Finally, a method for approximating the simulated performance of individual levels is discussed.

The 8-ary phase shift keying (8-PSK) constellation remains today widely used in turbo coded modulation schemes transmitting 3 coded bits per signal waveform. The present paper investigates a binary turbo coded modulation scheme using unbalanced 8-PSK (8-UPSK) constellations. A 8-UPSK with a structure similar to that of quadrature amplitude modulation (QAM) is proposed, yielding a low complexity calculation of the soft-decision associated with each bit. We suggest an architecture for computing the exact logarithm of the maximum likelihood ratio with a complexity reduced by more than 50% compared to that required for 8-PSK demapping. In addition, Monte-Carlo simulations show that some UPSK turbo coded modulations outperform the regular PSK modulation, hence achieving improved error performance with a reduced computational complexity.

Reducing the computational complexity of well-performing Multiple Inputs Multiple Outputs (MIMO) detection schemes is a crucial step toward designing high-throughput, low-cost MIMO receivers. In this paper we present a general multistage approach to MIMO detection, where a low complexity detector processes the received signal, and only few selected symbols are reprocessed using a more efficient, but more complex scheme. The global complexity is hence reduced while achieving near optimal symbol-space performance. Extending the work that was presented in [9], data that need further processing are identified using a reduced basis optimality test. Performing the test in the reduced lattice space diminishes the probability of false alarm of our test at no extra cost in terms of complexity, except during initialization. Usage of the secondary detector is thus more efficient as almost no data are unnecessarily reprocessed.

Maximum likelihood detection of multiple input multiple output (MIMO) signals requires computational capabilities that are challenging to obtain in most practical systems. Although many alternative schemes have been devised to reduce the operational burden, the complexity of algorithmic schemes remains much higher than that of equalizer-based techniques, which in turn provide poor performance in terms of error rate. In this paper, we derive a low-complexity test that allows to run a more efficient but complex detection scheme only on selected received symbols. The few symbols that are identified as erroneously detected by the low complexity method are the only ones to be reprocessed. The global complexity is hence reduced, typically by more than one order of magnitude, with very little loss in performance

Multicarrier transmission over a frequency selective channel implies large differences between the signal to noise ratios (SNR) on the transmitted tones. For independently detected tones, the best performance in terms of average bit error rate (BER) is obtained when conditions are equally good on all subcarriers, as proved by Jensen's inequality. In the case of blindly precoded orthogonal frequency division multiplexing (OFDM), independent minimum square error (MSE) detection over each tone and joint-maximum-likelihood (ML) detections are not equivalent, as tones are no longer independent. Jensen's bound, which is reached by MSE detection, can then be outperformed. However, the computational complexity of joint-ML detection makes it unrealistic in practical systems, In this paper we present a low complexity detection scheme based on Bernoulli-Gaussian (B-G) evaluation over the complex field that outperforms Jensen's MSE bound typically by several dB, without the need for feedback to the transmitter.

The present paper provides Shannon performance bounds of MIMO cooperative copper units (CCU) in the presence of crosstalk originating in neighboring pairs. Limited here to just two pairs, MIMO CCU operates either in multiplexing or diversity modes transmitting independent or identical data streams. Contrary to the rank instability of frequency dispersive wireless channel, CCU always experience full rank MIMO channels. Nevertheless, the major gain inherent in CCU optimal joint processing does not stem from the channel full rank property but from the possibility to partially cancel the crosstalk. Capacity may indeed be largely increased if the crosstalk correlation experienced by the CCU paths of each impaired tone of the copper bandwidth is leveraged through maximum likelihood estimation. The paper gives the theoretical foundation of this result by deriving an original (according to our knowledge) form of the MIMO Shannon bound in terms of the Fisher information matrix. We finally address a space-frequency modulation (SFM) that combines the selection of the best precoder with the multicarrier modulation.

This paper presents a new efficient algorithm for determining the perforation patterns yielding variable-rate convolutional coding schemes with improved performance. Given a particular code rate, the new criteria optimize the code for a specific bit error rate, compared to existing techniques searching codes with good asymptotical performance. As an example, the patterns to be used in ultra-wideband systems following the upcoming IEEE standard 802.15.3a were evaluated for different error rates. The new patterns improve the performance of the currently suggested patterns by up to 0.4 dB without changing the structure and complexity of the encoder-decoder.

This paper introduces a new multilevel coding scheme, identified as hierarchical trellis coded modulation (HTCM), that significantly improves a given TCM, while maintaining a 100% compatible fallback mode into the original trellis structure. HTCM exhibits a reasonable complexity increase when compared to higher order trellises or full turbo schemes that could reach similar gains. HTCM combines a given TCM code and a very high protection code (HPC), such as turbo code or low-density parity-check (LDPC) code. The HPC encodes the least significant bits of a large constellation, thus increasing the minimum Euclidean distance between TCM coded signals. The paper derives the theoretical HTCM asymptotic gain. HTCM is then applied to enhance the 16-state 4D Wei scheme by 1.76 dB of net coding gain., making use of a rate 1/2 parallel concatenated turbo code for the HPC and a new DSQ (diagonally shifted QAMs) constellation mapping. HTCM modularity, efficiency and relative simplicity open new ways to TCM enhancement that would help many broadband communication fields including wireless, satellite and wired systems.

Dynamic bit-loading for ADSL-DMT is driven by the signal-to-noise ratio (SNR) achieved per tone. 14.3 dB of minimum SNR is required to load a tone with the smallest square 4QAM constellation, for a fixed 10/sup -7/ bit error rate (BER). In the presence of physical layer impairments such as loop attenuation, bridge taps or crosstalks, many tones cannot be used because their individual SNR values are too small. On the other hand the associated total unloaded bandwidth exhibits a significant capacity. To benefit from parts of this unloaded bandwidth capacity, trellis coding with one bit or fractional bit loading has been proposed, unfortunately requiring some trellis decoding modifications. This paper introduces a new method called clustering modulation (CM) that does not require any trellis change and which clusters unloaded tones together to increase the SNR after maximum likelihood cluster payload demodulation in the frequency domain. Clustering modulation achieves up to 424 kbits/s of rate improvement on a 2.8 km (9 kfeet) 26AWG loop and up to 200 m of reach increase against SDSL noise B crosstalks.

In this paper, a theoretical expression of the optimum non-Gaussian radar detector is derived from the non-Gaussian SIRP model (Spherically Invariant Random Process) clutter and a bayesian estimator of the characteristic function of the SIRP. The SIRP model is used to perform coherent detection and to modelize the clutter as a complex Gaussian process whose variance is itself a positive random variable (r.v.). The PDF of the variance characterizes the statistics of the SIRP and after performing a bayesian estimation of this PDF from reference clutter cells we derive the Bayesian Optimum Radar Detector (BORD) and its statistical asymptotic form without any knowledge about the statistics of the clutter. We evaluate BORD performance for an unknown target signal embedded in K-distributed clutter and compare with optimum detectors performance (such as Optimum K Detector - OKD - in K-distributed clutter).

A low-complexity and iterative receiver is proposed for the CDMA uplink, consisting of a low-complexity equalizer and single-user decoders. Iterations are used to feed the equalizer with more reliable data, which improves performance. This receiver called turbo-CDMA is aimed to eliminate MUI almost completely in the presence of multipath and to be robust to the near-far effect. Simulation results show that the performance approaches single-user performance at moderate and also low signal-to-noise ratios, even for a severe distortion channel.

We are interested in the problem of restoring scintigraphic images acquired by a gamma detector in nuclear medicine. The aim is to improve the detectability of possible heterogeneous areas in different organs. We propose to solve the problem in the Bayesian framework with the maximum a posteriori (MAP) principle. The prior information was modeled by a Markov random field (MRF). The optimization is based on two kinds of methods: the stochastic algorithm of simulated annealing with a Gibbs sampler, and the deterministic algorithm of graduated non-convexity (GNC). We compared the results to the images restored by the Metz filter, more classical in this field. We applied these methods to the restoration of cold or warm nodules in the thyroid gland. We noticed the superiority of the proposed methods in terms of contrast around the nodules and uniformity in the images.

In this paper we address the problem of the joint detection and estimation of sinusoids embedded in noise, from a Bayesian point of view. We first propose an original Bayesian model. A large number of parameters has to be estimated, including the number of sinusoids. No analytical developments can be performed. This leads us to design a new stochastic algorithm relying on reversible jump MCMC (Markov chain Monte Carlo). We obtain very satisfactory results.

We present some aspects of non-Gaussian H-ARMA models. After recalling that an H-ARMA process is obtained by passing an ARMA process through a Hermite polynomial nonlinearity, we describe the theoretical analysis of their cumulants and cumulant spectra. The main advantage of this kind of model is that the cumulant structure of the output can be deduced directly from the input covariance sequence. We give the analytic forms of these cumulants, together with some comments on their estimation. Then, we present the problems we are facing concerning the identification of the model's parameters, and give a first (and naive) method for their estimation. We give some results obtained on synthetic data and finally conclude with some remarks on this class of processes.

We propose an original algorithm for the Bayesian joint estimation and detection of shot noise processes. The solution we propose relies on Markov chain Monte Carlo methods and provides the a posteriori probability density of the unknown parameters conditionally to the observations. The solution we propose provides many degrees of freedom for the inclusion of any a priori knowledge.

This paper introduces a new test statistic of normality which evaluates the cross covariances between choosen Hermite polynomials which are zero under the null hypothesis. The special form of the test leads to a modified sphericity statistic and we have called it the Hermite normality test (S/sub H/). We present its asymptotical distribution both under the null and nonnull hypothesis. A large number of simulations have been made to compare some specific Hermite tests to three others taken form the literature. If our test is better for a lot of nonnormal populations but works worse for some other, the main point is that we defined a wide range of tests which may match different nonnormal distributions.

In this paper, we use the Gibbs sampler to carry out Bayesian inference on conditionally linear Gaussian state space models. In a Bayesian framework, the Gibbs sampler is a powerful iterative procedure which can be seen as a stochastic analogue of the EM algorithm. To use it, it is necessary to sample from complex multivariate densities. An efficient algorithm is derived. An application to Bernoulli-Gauss processes deconvolution is given for which very satisfactory results are obtained. For this example, the geometric convergence of the algorithm is established.

Three new methods of instantaneous frequency estimation are introduced and compared in view of characterizing gravitational waves. Two methods are Bayesian and can be formulated as solutions of an ill-posed inverse problem with two different stochastic regularizations. Using either a state-space model for the time-frequency data or a compound non-uniform Bernoulli-Gauss model for the instantaneous frequency. The third method uses a reassignment technique applied to a spectrogram. In each case, averages based on different windowings permit to enhance the signal-to-noise ratio, leading to accurate results even below 0 dB.

This paper introduces a new time-frequency representation called the bicorspectral transform (BET), derives most of its theoretical properties and details relevant applications. This representation is devoted to non-Gaussian processes either stationary or not that exhibit non-vanishing third order moments. It is also closely related to the deterministic correlation between the signal and its Wigner-Ville distribution which yields efficient implementation with almost the same numerical complexity as a pseudo-Wigner-Ville representation.

A class of nonlinear filter, the p-th order complex transversal Volterra filter (TVF), is proposed. Its output is represented by a specific inner product of two vectors, which allows a unique formulation of problems such as the nonlinear detection, using the deflection criterion, and the nonlinear estimation in a mean square (MS) sense, by p-th order complex TV filtering, is introduced. It is shown that the p-th order complex TVF which maximizes the deflection is also the one which minimizes the distance, in a MS sense, to the likelihood ratio. The calculation of the optimal p-th order complex TVF requires the knowledge of the statistical properties of the data up to the 2p-th order.>

Linear-quadratic filters are a special example of Volterra filters that are limited to the second order. It is shown that all the results recently published which are valid in the linear-quadratic case can be extended with the appropriate notations to Volterra filters of arbitrary order. Particularly, the optimum Volterra filter giving the maximum of the deflection for detecting a signal in noise is wholly calculated. In addition, several geometrical properties of optimal Volterra filters are investigated by introducing appropriate scalar products. In particular, the concept of space orthogonal to the signal and the noise alone reference (NAR) property are introduced, allowing a decomposition of the optimal filter that exhibits a relation between detection and estimation. Extensions to the infinite case and relations with the likelihood ratio are also investigated.>

A general and unifying approach is presented that has different features, depending on whether the third-order moments vanish. All the well known procedures used in the linear context, such as the stochastic least mean squares (LMS), root mean square, and fast transversal filtering procedures, are extended. The role played by the third-order moments is pointed out. For any procedure if the third-order moments are vanishing, the optimum approach is parallel operation of two separate procedures. Otherwise, the optimum approach is a coupled algorithm. It is shown that the performance of the linear Wiener filter is always improved by adding a quadratic filter even if the third-order moments are vanishing, provided that the observation and the unknown processes are not jointly Gaussian. A general theory of the convergence of the linear-quadratic LMS method is established, assuming the m-ary independence of the underlying process. The effect of the mismatch in the algorithms is considered as the effect of a noise whose variance is derived. All these ideas are illustrated by simulation.>

A method for the selection of window parameters in the WD (Wigner distribution) domain is presented. The amount of smoothing can be controlled by varying the window lengths in both the frequency and time domains independently. The authors propose to estimate the window length by estimating the center frequency and center time of the WD of each signal component, using the block least-mean-square (BLMS) algorithm coupled with an unsupervised clustering technique. The window parameters are updated adaptively in order to obtain adequate smoothing in the case of nonstationary signals. A smoothing factor is introduced to obtain a measure of smoothing. Examples are given of applying this method to multicomponent synthetic signals and actual speech data.>

Vector quantization is a procedure widely used in communications. The problem has been solved for the basic detection situation using the locally optimal procedure and with some constraints on the quantization scheme. It is stated in its widest sense, which consists of finding the partition of the observation space and the values associated with it in such a way that the detection performance is optimal. It is solved completely with the deflection criterion, and the solution introduces the concept of quantization by a likelihood ratio procedure.>

The problem of linear-quadratic systems for detection has long been solved by assuming the deflection criterion and Gaussian noise. It is shown here that the Gaussian assumption can be removed, and a complete solution is presented for an arbitrary probability distribution with finite fourth-order moments. The optimal solution can always be obtained by solving a linear system of equations. Some properties of the optimal systems are developed for particular examples of nonGaussian noise. It is shown that there is a strong relationship between linear-quadratic optimal detection and optimal estimation, which extends results known for the purely linear case.>