Jacek Ilow

Analytic alpha-stable noise modeling in a Poisson field of interferers or scatterers

This paper addresses non-Gaussian statistical modeling of interference as a superposition of a large number of small effects from terminals/scatterers distributed in the plane/volume according to a Poisson point process. This problem is relevant to multiple access communication systems without power control and radar. Assuming that the signal strength is attenuated over distance r as 1/r/m, we show that the interference/clutter could be modeled as a spherically symmetric /spl alpha/-stable noise. A novel approach to stable noise modeling is introduced based on the LePage series representation. This establishes grounds to investigate practical constraints in the system model adopted, such as the finite number of interferers and nonhomogeneous Poisson fields of interferers. In addition, the formulas derived allow us to predict noise statistics in environments with lognormal shadowing and Rayleigh fading. The results obtained are useful for the prediction of noise statistics in a wide range of environments with deterministic and stochastic power propagation laws. Computer simulations are provided to demonstrate the efficiency of the /spl alpha/-stable noise model in multiuser communication systems. The analysis presented will be important in the performance evaluation of complex communication systems and in the design of efficient interference suppression techniques.

Detection for binary transmission in a mixture of Gaussian noise and impulsive noise modeled as an alpha-stable process

The impact of nonGaussian impulsive noise combined with Gaussian noise on the performance of the binary transmission is analyzed. The impulsive noise is modeled as an alpha-stable process. The probability of error for optimum, linear and nonlinear receivers is derived. The proposed nonlinear detectors show substantial improvements in performance compared to linear ones. The obtained results will be useful in performance evaluation of digital communication links subject to Gaussian and impulsive noises.<<ETX>>

Forecasting network traffic using FARIMA models with heavy tailed innovations

This paper presents a data traffic model capable of describing the long-range as well as short-range dependence structure of packet data traffic. Specifically, we use the fractionally integrated autoregressive-moving average (FARIMA) process with non-Gaussian white driving sequence to describe packet arrival rate in a unit time. We introduce a procedure to estimate the fractional differencing parameter and ARMA coefficients: this procedure uses a cepstrum approach and does not require any prior knowledge about the driving noise distribution and the type of ARMA system. Since the main purpose of workload modeling is to aid in network performance evaluations, we are particularly interested in using the FARIMA model to predict bandwidth requirements for network traffic. We propose a dynamic bandwidth allocation strategy by employing linear predictors designed based on the estimated FARIMA parameters.

Compensation of nonlinear distortions with memory effects in digital transmitters

High-level linear modulation schemes used in modern digital communication systems exhibit large peak-to-average power ratios (PAPRs). The performance of transceivers is very sensitive to nonlinear distortions, which arise mainly from the high power amplifier (HPA). Also, because of the wideband signals, the nonlinear distortions are frequency-dependent. The paper proposes an algebraic solution to compensate at the transmitter for the HPA nonlinearity. The HPA is represented by a memoryless nonlinear block followed by a linear filter. We first estimate the parameters of the unknown nonlinearity, which is modelled through a polynomial expansion. The frequency response of the unknown filter is then calculated, in order to capture the memory effects in the system. Using the identified nonlinear system parameters, a cascade of the inverse filter and the inverse memoryless nonlinearity is constructed preceding the HPA, in order to predistort the input signals and achieve overall linear transmitter characteristics. The scheme is examined through computer simulations for quadrature amplitude modulation (QAM). Improvements in the bit error rate (BER) and out-of-band spectrum regrowth are demonstrated for the travelling wave tube (TWT) HPA model. The results show that the proposed method is effective in compensating for amplitude-to-amplitude (AM/AM) distortions with memory using a relatively-small number of data points at the identification stage.

Applications of the empirical characteristic function to estimation and detection problems

Abstract In this paper, we are concerned with those estimation and detection problems for which standard approaches based on the probability density function (pdf) are difficult to implement. We consider the characteristic function (cf) for the description of random variables (RVs). We use the empirical characteristic function (ecf) to estimate the cf of a mixture of symmetric α -stable (S α S) RVs and extract information about their parameters. These parameters are further used in the design of discrete-time detectors which operate in a mixture of Gaussian and α -stable noise. We introduce a novel receiver based on the ecf which exploits the correlation structure of the ecf evaluated at a finite number of points. We analyze and optimize analytically the performance of the detector proposed. The estimation and detection methods developed are general enough to be used for other noise models with the cf in a closed form, as it is demonstrated on univariate K-distributed data. Through numerical simulations, the ecf is shown to be a useful tool for parameter estimation and detection with high efficiency and certain robustness features. Statistical properties of the ecf and the ecf-based methods are examined and computational issues are discussed.

Self-similar texture modeling using FARIMA processes with applications to satellite images

A texture model for synthetic aperture radar (SAR) images is presented. Specifically, a sea surface in satellite images is modeled using the two-dimensional (2-D) fractionally integrated autoregressive-moving average (FARIMA) process with a non-Gaussian white driving sequence. The FARIMA process is an ARMA type model which is asymptotically self-similar. It captures the long-range as well as short-range spatial dependence structure of an image with a small number of parameters. To estimate these parameters, an efficient estimation procedure based on a spectral fit is presented. Real-life ocean surveillance radar images collected by the RADARSAT sensor are used to evaluate the practicality of this FARIMA approach. Using the radial power spectral density, the new model is shown to provide a more accurate description of the SAR images than the conventional moving-average (MA), autoregressive (AR), and fractionally differenced (FD) models.

Compensation of nonlinear distortions with memory effects in OFDM transmitters

The OFDM modulation scheme is characterized by the Gaussian-like signal behavior with a relatively high peak-to-average power ratio (PAPR). As a result, it is very sensitive to nonlinear distortions, which arise mainly from the high power amplifier (HPA). This paper proposes an algebraic solution to compensate at the transmitter for nonlinearity of the HPA with memory effects, where the HPA behavioral model is represented by the Hammerstein structure, a cascade of a memoryless nonlinear block followed by a linear filter. In particular, a frequency domain parameter identification methodology is developed that first estimates the parameters of the unknown nonlinearity, which is modelled through a polynomial expansion. The frequency response of the unknown filter is then calculated, in order to capture the memory effects in the system. Using the identified nonlinear system parameters, an inverse Wiener structure, consisting of a cascade of the inverse filter and the inverse memoryless nonlinearity, is constructed preceding the HPA, in order to predistort the input signals so as to achieve overall linear transmitter characteristics.

Reliable multipath routing with fixed delays in MANET using regenerating nodes

This paper proposes a new framework in mobile ad hoc networks (MANET) for reliable multipath routing with fixed delays based on packet level forward error control (FEC). The novelty of this work stems from the integrated optimization of the redundancy at the path and the FEC packet levels to arrive at the concept of the regenerating nodes. The regenerating nodes can reduce the packet loss rate (PLR) between the source and the intermediate nodes so that, eventually, the PLR between the source and the destination is minimized. In general, the residual PLR in the system is reduced to the PLR on the connection between the last regenerating node and the destination. Extensive Monte Carlo simulations are provided to demonstrate the robust performance of the proposed scheme in the network environments with frequently changing topologies and PLR scenarios. The scheme accommodates various constraints for delay and reliability in terms of PLR that can be tailored to the specific applications, such as real-time multimedia services in MANET.

Adaptive Volterra predistorters for compensation of non-linear effects with memory in OFDM transmitters

This paper proposes an adaptive extension of a least squares Volterra predistorter to compensate for the non-linearity of the high power amplifier (HPA) with memory effects in orthogonal frequency division multiplexing (OFDM) systems at the transmitter side. Specifically, the input and output of the nonlinear HPA are accessed in the feedback loop structure to obtain the Volterra kernel parameters using least mean square (LMS) and recursive least square (RLS) algorithms. Once the Volterra kernel is obtained and signals pass through the cascaded system of the predistorter and the HPA, overall linear system characteristics are achieved. The proposed method is non-parametric as it does not assume any specific model for the HPA and the signal structure. The performance of the proposed scheme is verified through computer simulations. The improvements in the reduction of out-of-band spectral regrowth and enhanced performance in terms of the bit error rate (BER) are documented for the traveling wave tube (TWT) HPA model

A least-squares Volterra predistorter for compensation of non-linear effects with memory in OFDM transmitters

This paper proposes a general solution to compensate for the nonlinearity of a high power amplifier (HPA) with memory effects in orthogonal frequency division multiplexing (OFDM) communication systems at the transmitter side. Mean square error (MSE) minimization is used to derive the predistorter, which is modelled as a simplified finite order Volterra system, A general feedback loop structure is used in order to reduce the computational complexity. The input and output of the nonlinear system with memory effects are accessed, and a least-squares solution is used to obtain the Volterra kernel, which represents the predistorter. Once the Volterra kernel is obtained, when signals pass through the cascaded system of the predistorter and the HPA, overall linear system characteristics are achieved. The advantage of the proposed method is that it does not assume any specific model for the HPA. The performance of the proposed scheme is verified through computer simulations. Specifically, the improvements in the reduction of out-of-band spectral regrowth and enhanced performance in terms of the bit error rate (BER) are documented for the travelling wave tube (TWT) HPA model.