M. Bassem Ben Salah

Spatial-Spectral Formulation of Method of Moment for Rigorous Analysis of Microstrip Structures

In this paper, we present an efficient hybrid spatial- spectral formulation of the method of moment (MoM) in conjunction with the Mixed-Potential Integral Equation (MPIE) for planar circuit analysis. This method is based on the decomposition of the Greens functions in two parts: quasi-static in the near field region and the dynamic contribution in the far field region. Using this decomposition of Greens functions, the method of moment matrix entries can be reduced to a sum of two integrals. The first one is expressed in the spatial field and corresponds to the quasi-static contribution. It is analytically evaluated after a development in Taylor series of the exponential terms in the function to be integrated. The integrals expressed in the spectral field and corresponding to the dynamic part have the advantage of being calculated on a finite range and this is independent of the choice of the basis and test functions. The integrals expressed in the spectral field are performed by using numerical integration. It is also demonstrated that this hybrid method has accelerated the matrix fill in time by using a Fast Fourier Transform (FFT) algorithm. In order to validate the proposed method, numerical results are presented.

Ricean K-Factor and SNR Estimation for M-PSK Modulated Signals Using the Fourth-Order Cross-Moments Matrix

In wireless communication systems, the Ricean K-factor estimation is required in several applications such as link budget calculations. While the SNR estimation can be used to measure the link quality. In this paper, the Ricean K-factor estimation for M-ary Phase Shift Keying (M-PSK) modulated signals with additive noise is studied. To this end, the Fourth-Order Cross-Moments (FOCM) of the received signal are considered. First, the Signal-to-Noise Ratio (SNR) is computed by estimating the powers of the desired components of the signal and the noise. Then, the K-factor is estimated using the kurtosis of the Ricean channel. Simulation results show that our approach outperforms the most recent developed estimator in the literature.

Moment-Based Joint Estimation of Ricean K-Factor and SNR Over Linearly-Modulated Wireless SIMO Channels

In this paper, we develop a new joint estimator for the Ricean K-factor and the signal-to-noise ratio (SNR) when multiple antenna elements receive linearly-modulated signals with a complex additive white Gaussian noise spatially uncorrelated. The fourth-order cross-moments and the second-order moments of the received signal at a single-input multiple-output (SIMO) system are considered. The SNR is deduced by estimating the powers of the useful signals and the noise. While the K-factor is computed by estimating the kurtosises of the transmitted data and the Ricean channel. The new joint estimator is non-data-aided and does not require the a priori knowledge of the modulation type or order. The performances of this algorithm are investigated in terms of normalized root mean square error over different modulated transmissions and compared with the data-aided auto-correlation function (DA-ACF) based joint estimator extended to a SIMO configuration. Simulation results show that our approach outperforms the DA-ACF estimator.

Channel parameters estimation using cross-correlation matrix of a wireless SIMO system

In this work, we develop a new method for the maximum Doppler Spread (DS), the Angular Spread (AS) and the mean Angle of Arrival (AoA) estimation in a Rayleigh channel with a Single Input Multiple Output (SIMO) system using an Uniform Linear Array (ULA) at the receiver. We also consider a macro-cell environment. In this paper, the cross-correlation matrix is defined for the Laplacian angular distribution and their derivatives are used to estimate the three parameters. Simulation results show that, our proposed algorithm outperforms the Auto-Correlation Function (ACF) based approach developed in [1] for the maximum DS estimation. For the mean AoA and the AS estimation, our approach offers lower Root Mean Square Error (RMSE) than the Spread Root MUSIC (SRM) estimator [2].

Mean angle of arrival and Angular Spread estimation: Maximum Likelihood vs two low-complexity approaches

In this paper, three different nominal Angle of Arrival (AoA) and Angular Spread (AS) estimators are compared. A single source transmitter through a Rayleigh channel scenario is considered. First, the Maximum Likelihood (ML) estimator is studied. Then, low-complexity estimators which are the Spread Root-MUSIC and the two-stage approach, are presented. Simulation results show that the ML offers lower error estimation while the other estimators present a good compromise between computational complexity and estimation accuracy.

Efficient application of the Discrete Complex Images Method in the analysis of planar guiding structures

Planar guiding structures are analyzed using a Mixed-Potential Integral-Equation formulation (MPIE) solved by the method of moments (MoM). Closed-form two-dimensional Greens functions for the scalar and vector potentials are analytically obtained in the space domain due to the approximation of its spectral-domain version with Discrete Complex Images Method (DCIM), thus avoiding lengthy numerical evaluations. It will be shown that a suitable choice of the sampling path is key to reducing the number of images and to avoiding the necessity of extracting spectral poles. The advantages of this approach will be illustrated by numerical results.