An analytical method for approximate performance evaluation of binary linear block codes

An analytical method for approximate performance evaluation of binary linear block codes using an additive white Gaussian noise channel model with binary phase-shift keying modulation is presented. We focus on the probability density function of the bit log-likelihood ratio (LLR), which is expressed in terms of the Gram-Charlier series expansion. This expansion requires knowledge of the statistical moments of the bit LLR. We introduce an analytical method for calculating these moments. This is based on some recursive calculations involving certain weight enumerating functions of the code. It is proved that the approximation can be as accurate as desired, if we use enough terms in the Gram-Charlier series expansion. Numerical results are provided for some examples, which demonstrate close agreement with simulation results.     

A Convenient Multivariate Gram-Charlier Type A Series

It is often necessary to approximate the probability density function of a random variable from given statistical moments. The Gram-Charlier Type A series is one well known method for such representations. In this note, the Gram-Charlier Type A series is generalized to the multidimensional case.     

A recursive method for computing the coefficients of the Gram-Charlier series

The Gram-Charlier series is a known tool for approximating a probability density function when the moments or the cumulants of a random variable are known. A recursive procedure is presented which is well suited for the numerical computation of the coefficients of the series.     

A Gram-Charlier series method of calculating the probability of error in lightwave transmission systems

A Gram-Charlier series method has previously been applied to the calculation of the conditional probability of error, for a fixed data sequence, in the presence of intersymbol interference, detector multiplication noise, shot noise, and thermal noise. The probability of error is obtained by averaging the conditional probability of error over all possible data sequences. In this paper it is shown how the computational efficiency of the Gram-Charlier series method can be improved by calculating the probability of error without the need for an exhaustive averaging procedure.     

On Gram-Charlier Approximations

A Gram-Charlier series involves the expansion of one probability density function in terms of the derivatives of another density function. This paper generalizes the original series introduced by Gram, an expansion that minimizes a weighted square error. Two new Gram-Charlier expansions for the density of a finite variate are derived and recursion formulas for series coefficients are given.     

The time-frequency distributions of nonstationary signals based ona Bessel kernel

A kernel based on the first kind Bessel function of order one is proposed to compute the time-frequency distributions of nonstationary signals. This kernel can suppress the cross terms of the distribution effectively. It is shown that the Bessel distribution (the time-frequency distribution using Bessel kernel) meets most of the desirable properties with high time-frequency resolution. A numerical alias-free implementation of the distribution is presented. Examples of applications in time-frequency analysis of the heart's sound and Doppler blood flow signals are given to show that the Bessel distribution can be easily adapted to two very different signals for cardiovascular signal processing. By controlling a kernel parameter, this distribution can be used to compute the time-frequency representations of transient deterministic and random signals. The study confirms the potentials of the proposed distribution in nonstationary signal analysis     

Moment characterization of phase noise in coherent optical systems

The statistical characterization of the phase noise introduced by a semiconductor laser in a coherent optical transmission system is a key problem in the system performance evaluation. The authors consider the moment characterization, of the complex random process. Starting from the implicit representation of the probability density function through the Fokker-Planck equation, the authors obtain closed form analytical expressions for the moments of the filtered phase noise both in stationary and nonstationary conditions. Then the use of the moments for the computation of probability densities through orthogonal polynomial series expansion and maximum entropy approach is considered in application examples     

Accurate evaluation of bit-error rates of optical communication systems using the Gram-Charlier series

The probability densities and cumulative distribution functions of decision statistics of optical communications systems are expanded as a Gram-Charlier (G-C) series, leading to arbitrarily accurate systematic evaluation of bit-error rates (BERs) and optimal decision thresholds of optical communication systems. The method displays negligible computational complexity and is applicable whenever the moment or cumulant generating functions of the decision statistics are analytically available. We applied the technique to a birth-and-death Markovian model of a direct-detection receiver with optical preamplifier in a two-level amplitude-shift keying system. The modal expansion series rapidly converged, whereas the alternative saddlepoint approximation method predicted a BER which deviated by 7% from the G-C result.     

Modulations numériques à grand nombre d’états dans un canal de transmission hertzienne

The implementation of high capacity (140 Mbit/s) digital radio links leads to the use of high level modulation schemes in order to reduce spectrum occupation. A comparison between these modulations (with 16, 32, 64 and 128 levels) in the presence of impairments of a real channel is interesting to determine the spectral efficiency we can reach. The error probability is given by a method based upon a Gram-Charlier expansion using only the moments of the intersymbol interference random variable.     

Hybrid linear/quadratic time-frequency attributes

We present an efficient method for robustly calculating time-frequency attributes of a signal, including instantaneous mean frequency, bandwidth, kurtosis, and other moments. Most current attribute estimation techniques involve a costly intermediate step of computing a (highly oversampled) two-dimensonal (2-D) quadratic time-frequency representation (TFR), which is then collapsed to the one-dimensonal (1-D) attribute. Using the principles of hybrid linear/quadratic time-frequency analysis (time-frequency distribution series), we propose computing attributes as nonlinear combinations of the (slightly oversampled) linear Gabor coefficients of the signal. The method is both computationally efficient and accurate; it performs as well as the best techniques based on adaptive TFRs. To illustrate, we calculate an attribute of a seismic cross section     

Modified Cohen-Lee time-frequency distributions and instantaneousbandwidth of multicomponent signals

Cohen (1989, 1995) has introduced and extensively studied and developed the concept of the instantaneous bandwidth of a signal. Specifically, instantaneous bandwidth is interpreted as the spread in frequency about the instantaneous frequency, which is itself interpreted as the average frequency at each time. This view stems from a joint time-frequency distribution (TFD) analysis of the signal, where instantaneous frequency and instantaneous bandwidth are taken to be the first two conditional spectral moments, respectively, of the distribution. However, the traditional definition of instantaneous frequency, namely, as the derivative of the phase of the signal, is not consistent with this interpretation, and new definitions have therefore been proposed previously. We show that similar problems arise with the Cohen-Lee (1988, 1989) instantaneous bandwidth of a signal and propose a new formulation for the instantaneous bandwidth that is consistent with its interpretation as the conditional standard deviation in frequency of a TFD. We give the kernel constraints for a distribution to yield this new result, which is a modification of the kernel proposed by Cohen and Lee. These new kernel constraints yield a modified Cohen-Lee TFD whose first two conditional moments are interpretable as the average frequency and bandwidth at each time, respectively     

Modernized method of estimating the parameters of LFM signals based on the time-frequency distribution correction and the use of the Hough transformation

The method has been modernized for obtaining the parameter estimation of the fine structure of LFM signals (linear-frequency-modulated signals) at small values of the signal-to-noise ratio. The development of this method was based on the analysis of the signal time-frequency distribution (TFD) and the Hough transform. The specific feature of this method is correction of the time-frequency parameters in the TFD image considered and the use of the principle of detecting the straight line by the Hough transform that allows us to obtain estimates of parameters of LFM signals from the radio signal received within a shorter time interval at small signal-to-noise ratios. The results of simulation modeling demonstrate the capabilities of the method proposed.     

Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study

In this paper, we introduce the nonstationary signal analysis methods to analyze the myoelectric (ME) signals during dynamic contractions by estimating the time-dependent spectral moments. The time-frequency analysis methods including the short-time Fourier transform, the Wigner-Ville distribution, the Choi-Williams distribution, and the continuous wavelet transform were compared for estimation accuracy and precision on synthesized and real ME signals. It is found that the estimates provided by the continuous wavelet transform have better accuracy and precision than those obtained with the other time-frequency analysis methods on simulated data sets. In addition, ME signals from four subjects during three different tests (maximum static voluntary contraction, ramp contraction, and repeated isokinetic contractions) were also examined.     

Design of time-frequency representations using a multiform,tiltable exponential kernel

A novel Cohen's (1981) class time-frequency representation with a tiltable, generalized exponential kernel capable of attaining a wide diversity of shapes in the ambiguity function plane is proposed for improving the time-frequency analysis of multicomponent signals. The first advantage of the proposed kernel is its ability to generate a wider variety of passband shapes, e.g., rotated ellipses, generalized hyperbolas, diamonds, rectangles, parallel strips at arbitrary angles, crosses, snowflakes, etc., and narrower transition regions than conventional Cohen's class kernels; this versatility enables the new kernel to suppress undesirable cross terms in a broader variety of time-frequency scenarios. The second advantage of the new kernel is that closed form design equations can now be easily derived to select kernel parameters that meet or exceed a given set of user specified passband and stopband design criteria in the ambiguity function plane. Thirdly, it is shown that simple constraints on the parameters of the new kernel can be used to guarantee many desirable properties of time-frequency representations. The well known Choi-Williams (1989) exponential kernel, the generalized exponential kernel, and Nuttall's (1990) tilted Gaussian kernel are special cases of the proposed kernel     

非线性混叠信号的可分离性及分离方法研究

该文分析了非线性混叠信号的可分离性及分离条件,指出现阶段非线性混叠信号盲分离的局限性。将Edgeworth展开代入信息后向传输算法中,通过一种新的自适应累积量估计方法,克服了原算法指出的Edgeworth展开在盲信号分离中的缺限。计算机仿真结果表明了所提算法在特定非线性混叠模型信号分离的效果,我们还对不同的方法进行了分析对比,指出了累积量对不同算法的影响。     

Gaussianization: An Efficient Multivariate Density Estimation Technique for Statistical Signal Processing

Multivariate density estimation is an important problem that is frequently encountered in statistical learning and signal processing. One of the most popular techniques is Parzen windowing, also referred to as kernel density estimation. Gaussianization is a procedure that allows one to estimate multivariate densities efficiently from the marginal densities of the individual random variables. In this paper, we present an optimal density estimation scheme that combines the desirable properties of Parzen windowing and Gaussianization, using minimum Kullback–Leibler divergence as the optimality criterion for selecting the kernel size in the Parzen windowing step. The utility of the estimate is illustrated in classifier design, independent components analysis, and Prices’ theorem.     

Efficient radar target classification using adaptive jointtime-frequency processing

This paper presents a new target recognition scheme via adaptive Gaussian representation, which uses adaptive joint time-frequency processing techniques. The feature extraction stage of the proposed scheme utilizes the geometrical moments of the adaptivity spectrogram. For this purpose, we have derived exact and closed form expressions of geometrical moments of the adaptive spectrogram in the time, frequency, and joint time-frequency domains. Features obtained by this method can provide substantial savings of computational resources, preserving as much essential information for classifying targets as possible. Next, a principal component analysis is used to further reduce the dimension of feature space, and the resulting feature vectors are passed to the classifier stage based on the multilayer perceptron neural network. To demonstrate the performance of the proposed scheme, various thin-wire targets are identified. The results show that the proposed technique has a significant potential for use in target recognition     

Sensitivity Study of the Cumulant Method for Evaluating Reliability Measures of Two Interconnected Systems

The rationale for using the cumulant method to take advantage of its computational efficiency is well known among power system planners. However, although an analysis of the sensitivity of the univariate Gram-Charlier series has been investigated, an equivalent analysis of the sensitivity of the bivariate Gram-Charlier series has not yet been reported in the literature. This paper investigates the sensitivity of the bivariate Gram-Charlier series in the evaluation of reliability for several types of interconnected systems. The impact of different number of terms in the series on the accuracy of the results as well as on the computational requirements is also investigated. Load correlation between the interconnected systems is considered. As anticipated, the cumulant method is much faster than the commonly used recursive method. However, the reliability indexes, obtained using this method for interconnected systems with low reserve margin and with units of low forced outage rates can not be trusted. The relative error in the calculation of the loss of load probabilities increases with the increase of tie line capacity. However, the error is greatly reduced if the systems have units of higher forced outage rate. The use of additional terms in the bivariate Gram-Charlier series increases somewhat the accuracy of the results but also increases the computational time.     

Error Probability and SINR Analysis of Optimum Combining in Rician Fading

This paper considers the analysis of optimum combining systems in the presence of both co-channel interference and thermal noise. We address the cases where either the desired-user or the interferers undergo Rician fading. Exact expressions are derived for the moment generating function of the SINR which apply for arbitrary numbers of antennas and interferers. Based on these, we obtain expressions for the symbol error probability with M-PSK. For the case where the desireduser undergoes Rician fading, we also derive exact closed-form expressions for the moments of the SINR. We show that these moments are directly related to the corresponding moments of a Rayleigh system via a simple scaling parameter, which is investigated in detail. Numerical results are presented to validate the analysis and to examine the impact of Rician fading on performance.     

Volterra series representation of time-frequency distributions

This paper addresses a Volterra series representation of bilinear (or quadratic) time-frequency distributions that belong to Cohen's class, whereby the analogy of the bilinear class with a second-order double Volterra series is utilized. In addition, a different viewpoint for the bilinear kernel and a complementary interpretation concerning the quadratic time-frequency distributions are provided.