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.

A deconvolution algorithm for estimating jointly the line-of-sight code delay and carrier phase of GNSS signals

Abstract: An important task of a Global Navigation Satellite System (GNSS) receiver is to achieve fine synchronization between the received Line-of-Sight (LOS) signal and the reference code, which would allow the computation of the satellite-receiver distance. This synchronization process, known also as tracking stage, requires the Doppler shift to be successfully removed from the received signal (or that the residual error is kept within allowable limits) and typically involves the estimation of signal parameters such as the code delay, the carrier frequency and/or carrier phase. A challenging issue in the estimation of the synchronization parameters is the mitigation of multipath effects that appear due to the wireless propagation channel characteristics. In this paper, we deal with the problem of joint LOS code delay and carrier phase estimation of GNSS signals in a multipath environment. The problem is formulated into a linear system of equations in which the unknowns are the channel complex coefficients corresponding to each observed signal sample. We introduce a modified Projection Onto Convex Sets (POCS) algorithm that we optimize for both the new L1 Open Service and Coarse/Acquisition (C/A) signals employed by the future European Galileo and the Global Position System (GPS), respectively. We compare the performance of the algorithm with other state-of-art deconvolution algorithms. The simulation results indicate that our modified POCS algorithm is the most resistant in closely-spaced multipath static channels both when LOS code delay and carrier phase estimation are concerned.

Cooperative off-policy prediction of Markov decision processes in adaptive networks

Abstract: We apply diffusion strategies to propose a cooperative reinforcement learning algorithm, in which agents in a network communicate with their neighbors to improve predictions about their environment. The algorithm is suitable to learn off-policy even in large state spaces. We provide a mean-square-error performance analysis under constant step-sizes. The gain of cooperation in the form of more stability and less bias and variance in the prediction error, is illustrated in the context of a classical model. We show that the improvement in performance is especially significant when the behavior policy of the agents is different from the target policy under evaluation.

Beam coordination via diffusion adaptation over array networks

Abstract: In this work, we consider a distributed beam coordination problem, where a collection of arrays are interconnected by a certain topology. The beamformers employ an adaptive diffusion strategy to compute the beamforming weight vectors by relying solely on cooperation with their local neighbors. We analyze the mean-square-error (MSE) performance of the proposed strategy, including its transient and steady-state behavior. Simulation results support the findings that the MSE performance improves uniformly across the network relative to non-cooperative designs.

Optimal linear fusion for distributed spectrum sensing via semidefinite programming

Abstract: As an enabling functionality of overlay cognitive radio networks, spectrum sensing needs to reliably detect licensed signal in the band of interest. To achieve reliable sensing, we propose a linear fusion scheme for distributed spectrum sensing to combine the sensing results from multiple spatially distributed cognitive radios. The optimal linear fusion design is formulated into a nonconvex optimization problem. We show that the optimal solution of such a nonconvex problem can be solved via semi-definite programming reformulation.

Proximal diffusion for stochastic costs with non-differentiable regularizers

Abstract: We consider networks of agents cooperating to minimize a global objective, modeled as the aggregate sum of regularized costs that are not required to be differentiable. Since the subgradients of the individual costs cannot generally be assumed to be uniformly bounded, general distributed subgradient techniques are not applicable to these problems. We isolate the requirement of bounded subgradients into the regularizer and use splitting techniques to develop a stochastic proximal diffusion strategy for solving the optimization problem by continuously learning from streaming data. We represent the implementation as the cascade of three operators and invoke Banach's fixed-point theorem to establish that, despite gradient noise, the stochastic implementation is able to converge in the mean-square-error sense within O(μ) from the optimal solution, for a sufficiently small step-size parameter, μ.

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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