Samir Saoudi

Stochastic K -means algorithm for vector quantization

Abstract The proposed stochastic K-means algorithm (SKA) associates a vector with a cluster according to a probability distribution, which depends on the distance between the vector and the cluster gravity centre. It is less dependent than the K-means algorithm (KMA) on the initial centre choice. It can reach local minima closer to the global minimum of a distortion measure than the KMA. It has been applied to vector quantization of speech signals.

Space–Time Turbo Equalization With Successive Interference Cancellation for Frequency-Selective MIMO Channels

This paper addresses the issue of iterative space-time equalization for multiple-input-multiple-output (MIMO) frequency-selective fading channels. A new soft equalization concept based on successive interference cancellation (SIC) is introduced for a space-time bit-interleaved coded modulation (STBICM) transmission. The proposed equalizer allows us to separate intersymbol interference (ISI) and multiantenna interference (MAI) functions. Soft ISI is successively suppressed using a low-complexity suboptimum minimum mean square error (MMSE) criterion. The decoupling of ISI and MAI offers more flexibility in the design of the whole space-time equalizer. Different multiantenna detection criteria can be considered, ranging from simple detectors to the optimal maximum a posteriori (MAP) criterion. In particular, we introduce two soft equalizers, which are called SIC/SIC and SIC/MAP, and we show that they can provide a good performance-to-complexity tradeoff for many system configurations, as compared with other turbo equalization schemes. This paper also introduces an MMSE-based iterative channel state information (CSI) estimation algorithm and shows that attractive performance can be achieved when the proposed soft SIC space-time equalizer iterates with the MMSE-based CSI estimator.

A new efficient algorithm to compute the LSP parameters for speech coding

Abstract In this paper, the split Levinson algorithm is used to develop an efficient algorithm to compute the Line Spectrum Pairs (LSP) in Linear Predictive Coding (LPC) of speech. We propose two new real functions defined from the reciprocal and antireciprocal parts of the predictor polynomials obtained from the split Levinson algorithm. These functions are shown to obey three-term recurrence relations. Thus the LSP parameters are directly available from the eigenvalues of tridiagonal matrices, the entries of which are computed from only one version of the split Levinson algorithm. When compared with other existing methods, this algorithm is better in terms of complexity.

Turbo packet combining strategies for the MIMO-ISI ARQ channel

This paper addresses the issue of efficient turbo packet combining techniques for coded transmission with a Chase-type automatic repeat request (ARQ) protocol operating over a multiple-input multiple-output (MIMO) channel with intersymbol interference (ISI). First of all, we investigate the outage probability and the outage-based power loss of the MIMO-ISI ARQ channel when optimal maximum a posteriori (MAP) turbo packet combining is used at the receiver. We show that the ARQ delay (i.e., the maximum number of ARQ rounds) does not completely translate into a diversity gain. We then introduce two efficient turbo packet combining algorithms that are inspired by minimum mean square error (MMSE)-based turbo equalization techniques. Both schemes can be viewed as low-complexity versions of the optimal MAP turbo combiner. The first scheme is called signal-level turbo combining and performs packet combining and multiple transmission ISI cancellation jointly at the signal-level. The second scheme, called symbol-level turbo combining, allows ARQ rounds to be separately turbo equalized, while combining is performed at the filter output. We conduct a complexity analysis where we demonstrate that both algorithms have almost the same computational cost as the conventional log-likelihood ratio (LLR)-level combiner. Simulation results show that both proposed techniques outperform LLR-level combining, while for some representative MIMO configurations, signal-level combining has better ISI cancellation capability and achievable diversity order than that of symbol-level combining.

An iterative soft bit error rate estimation of any digital communication systems using a nonparametric probability density function

In general, performance of communication system receivers cannot be calculated analytically. The bit error rate (BER) is thus computed using the Monte Carlo (MC) simulation (Bit Error Counting). It is shown that if we wish to have reliable results with good precision, the total number of transmitted data must be conversely proportional to the product of the true BER by the relative error of estimate. Consequently, for small BERs, simulation results take excessively long computing time depending on the complexity of the receiver. In this paper, we suggest a new means of estimating the BER. This method is based on an estimation, in an iterative and nonparametric way, of the probability density function (pdf) of the soft decision of the received bit. We will show that the hard decision is not needed to compute the BER and the total number of transmitted data needed is very small compared to the classical MC simulation. Consequently, computing time is reduced drastically. Some theoretical results are also given to prove the convergence of this new method in the sense of mean square error (MSE) criterion. Simulation results of the suggested BER are given using a simple synchronous CDMA system.

Analytical plug-in method for kernel density estimator applied to genetic neutrality study

The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs). Here, a faster procedure than that of the common plug-in method is proposed. The mean integrated square error (MISE) depends directly upon which is linked to the second-order derivative of the pdf. As we intend to introduce an analytical approximation of , the pdf is estimated only once, at the end of iterations. These two kinds of algorithm are tested on different random variables having distributions known for their difficult estimation. Finally, they are applied to genetic data in order to provide a better characterisation in the mean of neutrality of Tunisian Berber populations.

Turbo packet combining for broadband space-time BICM hybrid-ARQ systems with co-channel interference

In this paper, efficient turbo packet combining for single carrier (SC) broadband multiple-input-multiple-output (MIMO) hybrid-automatic repeat request (ARQ) transmission with unknown co-channel interference (CCI) is studied. We propose a new frequency domain soft minimum mean square error (MMSE)-based signal level combining technique where received signals and channel frequency responses (CFR)s corresponding to all retransmissions are used to decode the data packet. We provide a recursive implementation algorithm for the introduced scheme, and show that both its computational complexity and memory requirements are quite insensitive to the ARQ delay, i.e., maximum number of ARQ rounds. Furthermore, we analyze the asymptotic performance, and show that under a sum-rank condition on the CCI MIMO ARQ channel, the proposed packet combining scheme is not interference-limited. Simulation results are provided to demonstrate the gains offered by the proposed technique.

A Novel Non-Parametric Iterative Soft Bit Error Rate Estimation Technique for Digital Communications Systems

This paper addresses the problem of unsupervised soft BER estimation for digital communications systems, where no prior knowledge about transmitted information bits is available. We show that the problem of BER estimation is equivalent to estimating the conditional probability density functions (pdf)s of soft channel/receiver outputs. We also propose a non parametric Gaussian Kernel-based pdf estimation technique. Then, we introduce an iterative stochastic expectation maximization (EM) algorithm for the estimation of both a priori and a posteriori probabilities of transmitted information bits, and the classification of soft observations according to transmitted bit values. These inputs serve in the iterative procedure used for the estimation of conditional pdfs. We analyze the performance of the proposed BER estimator and show that is asymptotically unbiased and point-wise consistent. We also provide numerical results in the case of CDMA systems and show that attractive performance is achieved compared with conventional Monte Carlo (MC)-aided techniques.

Joint Hybrid ARQ and Iterative Space-Time Equalization for Coded Transmission over the MIMO-ISI Channel

This paper focuses on the problem of efficient packet combining techniques for coded systems with hybrid automatic repeat request (ARQ) protocols operating over the frequency selective fading multiple input multiple output (MIMO) channel. We introduce a receiver scheme where space-time soft equalization is integrated into the packet combiner. This allows to create at each transmission an additional set of virtual receive antennas, thereby increasing the diversity order of the system. We also propose a second packet combining scheme where equalization is performed separately for each transmission. Iterative channel estimation is also investigated, and a training scheme where the pilot symbols are not retransmitted is introduced. Block error rate (BLER) performance is investigated via computer simulations for both perfect and imprecise channel state information (CSI) at the receiver side for demonstrating the potential of integrated soft equalization-based packet combining for transmission over multipath MIMO channels.

Performance Analysis of Project-and-Forward Relaying in Mixed MIMO-Pinhole and Rayleigh Dual-Hop Channel

In this letter, we present an end-to-end performance analysis of dual-hop project-and-forward relaying in a realistic scenario, where the source-relay and the relay-destination links are experiencing MIMO-pinhole and Rayleigh channel conditions, respectively. We derive the probability density function of both the relay postprocessing and the end-to-end signal-to-noise ratios, and the obtained expressions are used to derive the outage probability of the analyzed system as well as its end-to-end ergodic capacity in terms of generalized functions. Applying then the residue theory to Mellin-Barnes integrals, we infer the system asymptotic behavior for different channel parameters. As the bivariate Meijer-G function is involved in the analysis, we propose a new and fast MATLAB implementation enabling an automated definition of the complex integration contour. Extensive Monte-Carlo simulations are invoked to corroborate the analytical results.