Ali H. Sayed

Bio: Ali H. Sayed is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Adaptive filter & Optimization problem. The author has an hindex of 81, co-authored 728 publications receiving 36030 citations. Previous affiliations of Ali H. Sayed include Harbin Engineering University & University of California, Los Angeles.

Adaptive frequency-domain joint equalization and interference cancellation for multi-user space-time block-coded systems

Abstract: We develop an efficient adaptive receiver for joint equalization and interference cancellation for multi-user space-time block-coded transmissions. The receiver exploits the code structure and allows multiple user transmission over frequency-selective fading channels with reduced complexity and lower system overhead. The adaptation scheme is based on a recursive least-squares implementation for faster convergence; nevertheless, it exploits the code structure to attain RLS performance at LMS complexity.

Digital compensation of RF nonlinearities in software-defined radios

Abstract: The wideband RF receiver in a software-defined radio (SDR) system suffers from the nonlinear effects caused by the front-end analog processing. In the presence of strong blocker (interference) signals, nonlinearities introduce severe cross modulation over the desired signals. This paper investigates how the nonlinear distortions can be compensated for by using digital signal processing techniques. In the proposed solution, the SDR scans the wide spectrum and locates the desired signal and strong blocker signals. After down-converting these signals separately into the baseband, the baseband processor processes them jointly to mitigate the cross-modulation interferences. As a result, the sensitivity of the wideband RF receiver to the non-linearity impairment can be significantly lowered, simplifying the RF and analog circuitry design in terms of implementation cost and power consumption.

On the effects of colluded statistical attacks in cooperative spectrum sensing

Abstract: Cooperative spectrum sensing is vulnerable to attacks from malicious nodes, especially when collusion occurs. In this paper, we analyze the effect of colluded statistical attacks and show that collusion could cause performance degradation in terms of both false-alarm and detection probabilities, which is not possible via independent attacks. Closed-form expressions for system performance under the majority fusion rule are provided for a generalized form of colluded attacks. Then, for specific scenarios of collusion and mimicry attacks, we study the conditions under which the probabilities of false alarm and detection are both degraded.

Design of robust filters with improved robustness margins via parameter scaling

Abstract: The paper describes a procedure for improving the robustness margins of robust filters via parameter scaling. The scaling parameter is chosen as the square-root factor of the inverse of a positive-definite solution to certain matrix inequalities. This choice is motivated by the desire to generate an estimator dynamics with a stable closed-loop matrix whose maximum singular value is bounded by unity; a step that enhances the robustness of the filters.

An uplink DS-CDMA receiver using a robust post-correlation Kalman structure

Abstract: Kalman filtering has been proposed in the literature for wireless channel estimation, however, it is not sufficiently robust to uncertainties in the channel auto-correlation model as well as to multiple access interference (MAI). This paper presents a receiver structure for direct-sequence code-division multiple-access (DS-CDMA) systems by using robust Kalman estimation and post-correlation (i.e., symbol rate) processing for channel estimation. The proposed structure is also generalized to incorporate multiple-antenna combining and interference cancellation techniques. The resulting receiver outperforms earlier structures in the presence of channel modeling uncertainties, MAI, and low-received signal-to-noise ratio. The enhancement in performance is achieved at the same order of complexity as a standard Kalman-based receiver.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Tensor Decompositions and Applications

Abstract: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Table Of Integrals Series And Products

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