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.

Wideband Spectrum Sensing in Cognitive Radio Networks

Abstract: Spectrum sensing is an essential enabling functionality for cognitive radio networks to detect spectrum holes and opportunistically use the under-utilized frequency bands without causing harmful interference to legacy networks. This paper introduces a novel wideband spectrum sensing technique, called multiband joint detection, which jointly detects the signal energy levels over multiple frequency bands rather than consider one band at a time. The proposed strategy is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. The spectrum sensing problem is formulated as a class of optimization problems in interference limited cognitive radio networks. By exploiting the hidden convexity in the seemingly non-convex problem formulations, optimal solutions for multiband joint detection are obtained under practical conditions. Simulation results show that the proposed spectrum sensing schemes can considerably improve the system performance. This paper establishes important principles for the design of wideband spectrum sensing algorithms in cognitive radio networks.

Modeling Bird Flight Formations Using Diffusion Adaptation

Abstract: Flocks of birds self-organize into V-formations when they need to travel long distances. It has been shown that this formation allows the birds to save energy, by taking advantage of the upwash generated by the neighboring birds. In this work we use a model for the upwash generated by a flying bird, and show that a flock of birds can self-organize into a V-formation if every bird were to process spatial and network information through an adaptive diffusive process. The diffusion algorithm requires the birds to obtain measurements of the upwash, and also to use information from neighboring birds. The result has interesting implications. First, a simple diffusion algorithm can account for self-organization in birds. The algorithm is fully distributed and runs in real time. Second, according to the model, that birds can self-organize based on the upwash generated by the other birds. Third, that some form of information sharing among birds is necessary to achieve flight formation. We also propose a modification to the algorithm that allows birds to organize into a U-formation, starting from a V-formation. We show that this type of formation leads to an equalization effect, where every bird in the flock observes approximately the same upwash.

Performance Limits for Distributed Estimation Over LMS Adaptive Networks

Abstract: In this work, we analyze the mean-square performance of different strategies for distributed estimation over least-mean-squares (LMS) adaptive networks. The results highlight some useful properties for distributed adaptation in comparison to fusion-based centralized solutions. The analysis establishes that, by optimizing over the combination weights, diffusion strategies can deliver lower excess-mean-square-error than centralized solutions employing traditional block or incremental LMS strategies. We first study in some detail the situation involving combinations of two adaptive agents and then extend the results to generic N -node ad-hoc networks. In the latter case, we establish that, for sufficiently small step-sizes, diffusion strategies can outperform centralized block or incremental LMS strategies by optimizing over left-stochastic combination weighting matrices. The results suggest more efficient ways for organizing and processing data at fusion centers, and present useful adaptive strategies that are able to enhance performance when implemented in a distributed manner.

Compensation of Phase Noise in OFDM Wireless Systems

Abstract: Phase noise causes significant degradation in the performance of orthogonal frequency division multiplexing (OFDM)-based wireless communication systems. The presence of phase noise can reduce the effective signal-to-noise ratio (SNR) at the receiver, and consequently, limit the bit error rate (BER) and data rate. In this paper, the effect of phase noise on OFDM wireless systems is studied, and a compensation scheme is proposed to mitigate the common phase error and intercarrier interference (ICI) caused by phase noise. In the proposed scheme, the communication between the transmitter and receiver blocks consists of two stages. In the first stage, block-type pilot symbols are transmitted and the channel coefficients are jointly estimated with the phase noise in the time domain. In the second stage, comb-type OFDM symbols are transmitted such that the receiver can jointly estimate the data symbols and the phase noise. It is shown both by theory and computer simulations that the proposed scheme can effectively mitigate the ICI caused by phase noise and improve the BER of OFDM systems. Another benefit of the proposed scheme is that the sensitivity of OFDM receivers to phase noise can be significantly lowered, which helps simplify the oscillator and circuitry design in terms of implementation cost and power consumption.

Cooperative Sensing via Sequential Detection

Abstract: Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. This paper presents a cooperative sequential detection scheme to reduce the average sensing time that is required to reach a detection decision. In the scheme, each cognitive radio computes the log-likelihood ratio for its every measurement, and the base station sequentially accumulates these log-likelihood statistics and determines whether to stop making measurement. The paper studies how to implement the scheme in a robust manner when the assumed signal models have unknown parameters, such as signal strength and noise variance. These ideas are illustrated through two examples in spectrum sensing. One assumes both the signal and noise are Gaussian distributed, while the other assumes the target signal is deterministic.

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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