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Ali H. Sayed
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 766
Citations - 39568
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.
Papers
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Distributed Pareto Optimization via Diffusion Strategies
Jianshu Chen,Ali H. Sayed +1 more
TL;DR: A detailed mean-square error analysis is performed and it is established that all agents are able to converge to the same Pareto optimal solution within a sufficiently smallmean-square-error (MSE) bound even for constant step-sizes.
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Mobile Adaptive Networks
Sheng-Yuan Tu,Ali H. Sayed +1 more
TL;DR: This paper applies adaptive diffusion techniques to guide the self-organization process, including harmonious motion and collision avoidance, of adaptive networks when the individual agents are allowed to move in pursuit of a target.
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MIMO OFDM receivers for Systems with IQ imbalances
A. Tarighat,Ali H. Sayed +1 more
TL;DR: Simulation results show significant improvement in the achievable BER of the proposed MIMO receivers for space-time block-coded OFDM systems in the presence of IQ imbalances.
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Parameter Estimation in the Presence of Bounded Data Uncertainties
TL;DR: In this paper, the authors formulate and solve a new parameter estimation problem in the presence of data uncertainties, which is suitable when a priori bounds on the uncertain data are available, and its solution leads to more meaningful results, especially when compared with other methods such as total least squares and robust estimation.
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Diffusion Adaptation Over Networks Under Imperfect Information Exchange and Non-Stationary Data
TL;DR: This paper investigates the mean-square performance of general adaptive diffusion algorithms in the presence of various sources of imperfect information exchanges, quantization errors, and model non-stationarities and reveals that link noise over the regression data modifies the dynamics of the network evolution in a distinct way, and leads to biased estimates in steady-state.