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.

Robust wireless location over fading channels

Abstract: This paper develops an estimation algorithm for the time and amplitude of arrival of a known transmitted sequence over a single-path fading channel. The algorithm is optimized to enhance robustness to fast channel fading and low signal-to-noise ratio (SNR) conditions, which are common in wireless location applications. The paper also presents a noise and fading bias correction technique for amplitude of arrival estimation that improves the estimation accuracy significantly. The proposed algorithm is then applied to the case of code-division multiple-access (CDMA) wireless location finding for which the paper gives simulation results that demonstrate significant estimation accuracy improvement over known algorithms.

On the baseband compensation of IQ imbalances in OFDM systems

Abstract: OFDM is a widely recognized and standardized modulation scheme for future high bit rate communications. Implementation of OFDM-based systems suffers from inphase-quadrature phase (IQ) imbalances in the front-end analog processing. The IQ imbalances can severely limit the operating SNR and, consequently, the supported constellation sizes. In this paper, the effect of IQ imbalances on OFDM receivers is analyzed and system level algorithms to compensate for these distortions are proposed. The algorithms include different post and pre-FFT estimation and correction techniques.

On the Influence of Bias-Correction on Distributed Stochastic Optimization

Abstract: Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact solution under proper conditions. However, their performance under stochastic and adaptive settings is less explored. It is still unknown whether , when and why these bias-correction methods can outperform their traditional counterparts with noisy gradient and constant step-sizes. This work studies the performance of exact diffusion under the stochastic and adaptive setting, and provides conditions under which exact diffusion has superior steady-state mean-square deviation (MSD) performance than traditional algorithms without bias-correction. In particular, it is proven that this superiority is more evident over sparsely-connected network topologies such as lines, cycles, or grids. Conditions are also provided under which exact diffusion method can or degrade the performance of traditional methods. Simulations are provided to validate the theoretical findings.

On the convergence of blind adaptive equalizers for constant modulus signals

Abstract: This paper studies the behavior of the error sequence of stop-anti-go variants of two adaptive blind equalizers, namely CMA2-2 and Sato's (1975) algorithm. It is shown that for transmitted signals with constant modulus /spl gamma/, the equalizer output can be made to lie within the circle of radius /spl gamma//spl radic/c infinitely often, for some value of c that is only slightly larger than one.

Excess-Risk of Distributed Stochastic Learners

Abstract: This paper studies the learning ability of consensus and diffusion distributed learners from continuous streams of data arising from different but related statistical distributions. Four distinctive features for diffusion learners are revealed in relation to other decentralized schemes even under left-stochastic combination policies. First, closed-form expressions for the evolution of their excess-risk are derived for strongly convex risk functions under a diminishing step-size rule. Second, using these results, it is shown that the diffusion strategy improves the asymptotic convergence rate of the excess-risk relative to non-cooperative schemes. Third, it is shown that when the in-network cooperation rules are designed optimally, the performance of the diffusion implementation can outperform that of naive centralized processing. Finally, the arguments further show that diffusion outperforms consensus strategies asymptotically and that the asymptotic excess-risk expression is invariant to the particular network topology. The framework adopted in this paper studies convergence in the stronger mean-square-error sense, rather than in distribution, and develops tools that enable a close examination of the differences between distributed strategies in terms of asymptotic behavior, as well as in terms of convergence rates.

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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