Abdellatif Medouri

A survey of various propagation models for mobile communication

In order to estimate the signal parameters accurately for mobile systems, it is necessary to estimate a systems propagation characteristics through a medium. Propagation analysis provides a good initial estimate of the signal characteristics. The ability to accurately predict radio-propagation behavior for wireless personal communication systems, such as cellular mobile radio, is becoming crucial to system design. Since site measurements are costly, propagation models have been developed as a suitable, low-cost, and convenient alternative. Channel modeling is required to predict path, loss and to characterize the impulse response of the propagating channel. The path loss is associated with the design of base stations, as this tells us how much a transmitter needs to radiate to service a given region. Channel characterization, on the other hand, deals with the fidelity of the received signals, and has to do with the nature of the waveform received at a receiver. The objective here is to design a suitable receiver that will receive the transmitted signal, distorted due to the multipath and dispersion effects of the channel, and that will decode the transmitted signal. An understanding of the various propagation models can actually address both problems. This paper begins with a review of the information available on the various propagation models for both indoor and outdoor environments. The existing models can be classified into two major classes: statistical models and site-specific models. The main characteristics of the radio channel - such as path loss, fading, and time-delay spread - are discussed. Currently, a third alternative, which includes many new numerical methods, is being introduced to propagation prediction. The advantages and disadvantages of some of these methods are summarized. In addition, an impulse-response characterization for the propagation path is also presented, including models for small-scale fading, Finally, it is shown that when two-way communication ports can be defined for a mobile system, it is possible to use reciprocity to focus the energy along the direction of an intended user without any explicit knowledge of the electromagnetic environment in which the system is operating, or knowledge of the spatial locations of the transmitter and the receiver.

Parameter estimation of exponentially damped sinusoids using a higher order correlation-based approach

A very common problem in signal processing is parameter estimation of exponentially damped sinusoids from a finite subset of noisy observations. When the signal is contaminated with colored noise of unknown power spectral density, a cumulant-based approach provides an appropriate solution to this problem. We propose a new class of estimator, namely, a covariance-type estimator, which reduces the deterministic errors associated with imperfect estimation of higher order correlations from finite-data length. This estimator allows a higher order correlation sequence to be modeled as a damped exponential model in certain slices of the moments plane. This result shows a useful link with well-known linear-prediction-based methods, such as the minimum-norm principal-eigenvector method of Kumaresan and Tufts (1982), which can be subsequently applied to extracting frequencies and damping coefficients from the 1-D correlation sequence. This paper discusses the slices allowed in the moments plane, the uses and limitations of this estimator using multiple realizations, and a single record in a noisy environment. Monte Carlo simulations applied to standard examples are also performed, and the results are compared with the KT method and the standard biased-estimator-based approach. The comparison shows the effectiveness of the proposed estimator in terms of bias and mean-square error when the signals are contaminated with additive Gaussian noise and a single data record with short data length is available.

The relationship between AR-modelling bispectral estimation and the theory of linear prediction

Abstract In this paper, a relationship between bispectrum estimation by means of autoregressive modelling and the theory of linear prediction is presented. It is demonstrated that the ‘third-order recursion’ equations for a causal and an anticausal model can be derived via minimization of a set of forward- and backward-prediction squared errors, respectively.

Signal enhancement in a near-field MIMO environment through adaptivity on transmit

A technique is presented on how to enhance the received signals in a near field multi-input multi-output (MIMO) environment where beam forming is not possible. This is done through the use of adaptivity on transmit. This technique is based on the principle of reciprocity, is independent of the material medium in which it is transmitting, and incorporates near-field environments and multipath. The objective here is to select a set of weights adapted to each receiver to be applied to each transmitting antenna, which is a function of the user location, so that the transmitted signal at the carrier frequency may be directed to a particular receiver location while simultaneously minimizing the received signal strengths at other receiver locations. Numerical simulations have been made to illustrate the novelty of the proposed approach.

A “constrained third-order mean”-type estimator to calculate the parameters of exponentially damped sinusoids using third-order statistics

Abstract The extraction of parameters of exponentially damped sinusoids using the extension of the minimum-norm principal eigenvectors method (also called Kumaresan-Tufts method) to the third-order statistic domain is considered. We present a modification of the approach introduced by Papadopoulos and Nikias (1990) and compare our method with theirs. This new method is designed as an adaptation of the ‘constrained third-order mean (CTOM) method’ for bispectrum estimation via AR modelling, to apply it to bispectral estimation of the poles of exponentially damped sinusoids. The method therefore constructs the basic third-order correlation matrix using a CTOM-type (unwindowed) estimator instead of the previously proposed TOR-type (two-windowed) one. By mean-simulation results, it is shown that the proposed method provides a much more accurate estimation of the poles than the TOR-type method when the number of available data is small and the SNR values are not excessively low. The case of exponentially damped signals with poles closely spaced in frequency and few data is also studied, with the CTOM-type estimator proving to have a much higher capacity for resolving them than the TOR-type one.