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Showing papers by "Ali H. Sayed published in 2010"


Journal ArticleDOI
TL;DR: This work motivates and proposes new versions of the diffusion LMS algorithm that outperform previous solutions, and provides performance and convergence analysis of the proposed algorithms, together with simulation results comparing with existing techniques.
Abstract: We consider the problem of distributed estimation, where a set of nodes is required to collectively estimate some parameter of interest from noisy measurements. The problem is useful in several contexts including wireless and sensor networks, where scalability, robustness, and low power consumption are desirable features. Diffusion cooperation schemes have been shown to provide good performance, robustness to node and link failure, and are amenable to distributed implementations. In this work we focus on diffusion-based adaptive solutions of the LMS type. We motivate and propose new versions of the diffusion LMS algorithm that outperform previous solutions. We provide performance and convergence analysis of the proposed algorithms, together with simulation results comparing with existing techniques. We also discuss optimization schemes to design the diffusion LMS weights.

1,116 citations


Journal ArticleDOI
TL;DR: This work studies the problem of distributed Kalman filtering and smoothing, and proposes diffusion algorithms to solve each one of these problems, and compares the simulation results with the theoretical expressions, and notes that the proposed approach outperforms existing techniques.
Abstract: We study the problem of distributed Kalman filtering and smoothing, where a set of nodes is required to estimate the state of a linear dynamic system from in a collaborative manner. Our focus is on diffusion strategies, where nodes communicate with their direct neighbors only, and the information is diffused across the network through a sequence of Kalman iterations and data-aggregation. We study the problems of Kalman filtering, fixed-lag smoothing and fixed-point smoothing, and propose diffusion algorithms to solve each one of these problems. We analyze the mean and mean-square performance of the proposed algorithms, provide expressions for their steady-state mean-square performance, and analyze the convergence of the diffusion Kalman filter recursions. Finally, we apply the proposed algorithms to the problem of estimating and tracking the position of a projectile. We compare our simulation results with the theoretical expressions, and note that the proposed approach outperforms existing techniques.

782 citations


Journal ArticleDOI
TL;DR: Numerical results show that the proposed multiuser two-way relay processing can efficiently eliminate both co-channel interference (CCI) and self-interference (SI).
Abstract: In this paper, multiple-input multiple-output (MIMO) relay transceiver processing is proposed for multiuser two-way relay communications. The relay processing is optimized based on both zero-forcing (ZF) and minimum mean-square-error (MMSE) criteria under relay power constraints. Various transmit and receive beamforming methods are compared including eigen beamforming, antenna selection, random beamforming, and modified equal gain beamforming. Local and global power control methods are designed to achieve fairness among all users and to maximize the system signal-to-noise ratio (SNR). Numerical results show that the proposed multiuser two-way relay processing can efficiently eliminate both co-channel interference (CCI) and self-interference (SI).

314 citations


Journal ArticleDOI
TL;DR: Simulation results show that the diffusion L MS algorithm with the proposed adaptive combiners outperforms those with existing static combiners and the incremental LMS algorithm, and that the theoretical analysis provides a good approximation of practical performance.
Abstract: This paper presents an efficient adaptive combination strategy for the distributed estimation problem over diffusion networks in order to improve robustness against the spatial variation of signal and noise statistics over the network. The concept of minimum variance unbiased estimation is used to derive the proposed adaptive combiner in a systematic way. The mean, mean-square, and steady-state performance analyses of the diffusion least-mean squares (LMS) algorithms with adaptive combiners are included and the stability of convex combination rules is proved. Simulation results show (i) that the diffusion LMS algorithm with the proposed adaptive combiners outperforms those with existing static combiners and the incremental LMS algorithm, and (ii) that the theoretical analysis provides a good approximation of practical performance.

295 citations


Journal ArticleDOI
TL;DR: The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square type distributed adaptive filters with colored inputs to achieve an acceptable misadjustment performance with lower computational and memory cost.
Abstract: We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton's method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The analysis of transient and steady-state performances at each individual node within the network is developed by using a weighted spatial-temporal energy conservation relation and confirmed by computer simulations. The simulation results also verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance with lower computational and memory cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, as compared to a distributed recursive-least-squares (RLS) based method.

166 citations


Journal ArticleDOI
TL;DR: A cooperative sequential detection scheme to reduce the average sensing time that is required to reach a detection decision and how to implement the scheme in a robust manner when the assumed signal models have unknown parameters, such as signal strength and noise variance is studied.
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.

144 citations


Journal ArticleDOI
TL;DR: It is shown that the two nonconvex subproblems can be solved by semidefinite programs in a globally optimal fashion and by employing a special matrix decomposition technique, the optimal linear fusion rule for the distributed detection problem is obtained.
Abstract: Consider the problem of signal detection via multiple distributed noisy sensors. We study a linear decision fusion rule of [Z. Quan, S. Cui, and A. H. Sayed, ?Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks,? IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 28-40, Feb. 2008] to combine the local statistics from individual sensors into a global statistic for binary hypothesis testing. The objective is to maximize the probability of detection subject to an upper limit on the probability of false alarm. We propose a more efficient solution that employs a divide-and-conquer strategy to divide the decision optimization problem into two subproblems. Each subproblem is a nonconvex program with a quadratic constraint. Through a judicious reformulation and by employing a special matrix decomposition technique, we show that the two nonconvex subproblems can be solved by semidefinite programs in a globally optimal fashion. Hence, we can obtain the optimal linear fusion rule for the distributed detection problem. Compared with the likelihood-ratio test approach, optimal linear fusion can achieve comparable performance with considerable design flexibility and reduced complexity.

83 citations


Journal ArticleDOI
TL;DR: This paper investigates mixture structures with respect to their final mean-square error (MSE) and tracking performance in the steady state for stationary and certain nonstationary data, respectively.
Abstract: In this paper, we consider mixture approaches that adaptively combine outputs of several parallel running adaptive algorithms. These parallel units can be considered as diversity branches that can be exploited to improve the overall performance. We study various mixture structures where the final output is constructed as the weighted linear combination of the outputs of several constituent filters. Although the mixture structure is linear, the combination weights can be updated in a highly nonlinear manner to minimize the final estimation error such as in Singer and Feder 1999; Arenas-Garcia, Figueiras-Vidal, and Sayed 2006; Lopes, Satorius, and Sayed 2006; Bershad, Bermudez, and Tourneret 2008; and Silva and Nascimento 2008. We distinguish mixture approaches that are convex combinations (where the linear mixture weights are constrained to be nonnegative and sum up to one) [Singer and Feder 1999; Arenas-Garcia, Figueiras-Vidal, and Sayed 2006], affine combinations (where the linear mixture weights are constrained to sum up to one) [Bershad, Bermudez, and Tourneret 2008] and, finally, unconstrained linear combinations of constituent filters [Kozat and Singer 2000]. We investigate mixture structures with respect to their final mean-square error (MSE) and tracking performance in the steady state for stationary and certain nonstationary data, respectively. We demonstrate that these mixture approaches can greatly improve over the performance of the constituent filters. Our analysis is also generic such that it can be applied to inhomogeneous mixtures of constituent adaptive branches with possibly different structures, adaptation methods or having different filter lengths.

80 citations


Proceedings ArticleDOI
14 Mar 2010
TL;DR: The resulting algorithms are robust to node and link failure, scalable, and fully distributed, in the sense that no fusion center is required, and nodes communicate with their neighbors only.
Abstract: We study the problem of distributed state-space estimation, where a set of nodes are required to estimate the state of a nonlinear state-space system based on their observations. We extend our previous work on distributed Kalman filtering to the nonlinear case, and propose algorithms for Extended and Unscented Kalman filtering. The resulting algorithms are robust to node and link failure, scalable, and fully distributed, in the sense that no fusion center is required, and nodes communicate with their neighbors only. We apply the algorithms to the problem of estimating the position of every node in an ad-hoc network, also known as wireless localization. Simulation results illustrate the performance of the proposed algorithms.

55 citations


Journal ArticleDOI
TL;DR: A multiuser two-way relay system using space division multiple access (SDMA) communications is designed and an optimal scheduling method is devised that maximizes the sum rate while ensuring fairness among users.
Abstract: In this paper, we design a multiuser two-way relay system using space division multiple access (SDMA) communications and devise an optimal scheduling method that maximizes the sum rate while ensuring fairness among users. To reduce the computational load at the relays, we propose rate- and angle-based suboptimal scheduling methods. The numerical results illustrate tradeoff between complexity and the performance. Specifically, when the relay has two antennas, we verify that the rate-based method can provide significant computational savings at the cost of a rate reduction of less than 4% when compared with the optimal scheduling method.

41 citations


Proceedings ArticleDOI
14 Mar 2010
TL;DR: The method applies to generic topologies and avoids the need to establish a Hamiltonian cycle over the network, generalizing the original incremental mode, while keeping nearly the same mean-square performance, as illustrated by the simulations.
Abstract: We introduce an incremental cooperation mode into the framework of adaptive networks (AN). The method applies to generic topologies and avoids the need to establish a Hamiltonian cycle over the network, generalizing the original incremental mode, while keeping nearly the same mean-square performance, as illustrated by the simulations. We motivate the new mode by relying on an LMS rule at the nodes, and mean-square analysis is provided.

Proceedings ArticleDOI
01 Nov 2010
TL;DR: Simulation results suggest that cooperation among bacteria is critical for effective foraging to improve their decisions of movement and schemes for cooperation and diffusion of information are proposed.
Abstract: Bacteria forage by moving towards nutrient sources in a process known as chemotaxis The bacteria follow gradient variations by tumbling or moving in straight lines Both modes of locomotion are affected by Brownian motion Bacteria are also capable of interactions through chemical signaling As the bacteria swim towards nutrients, they emit chemicals that can be sensed by their neighboring bacteria and used to adjust the direction of motion In this paper, we propose schemes for cooperation and diffusion of information [1]–[7] and study their effect on bacteria motility Because bacteria are limited in their abilities, we restrict the sharing of information to binary choices (such as whether to run or tumble) Simulation results suggest that cooperation among bacteria is critical for effective foraging to improve their decisions of movement

Proceedings ArticleDOI
14 Jun 2010
TL;DR: This work develops a model for food foraging and explains how a school of fish can move as a group if every fish were to employ a distributed strategy, known as diffusion adaptation.
Abstract: Fish organize themselves into schools as a way to defend against predators and improve foraging efficiency. In this work we develop a model for food foraging and explain how a school of fish can move as a group if every fish were to employ a distributed strategy, known as diffusion adaptation. The algorithm assumes the fish sense the general direction of food and can also infer the general direction of motion of their neighbors. The result indicates that a simple diffusion algorithm can account for the foraging behavior. The study also reveals that some form of communication among the fish is crucial to achieve schooling.

Proceedings ArticleDOI
01 Nov 2010
TL;DR: This paper proposes a dynamic technique that enables the node to pick from among its neighbors that neighbor that is likely to lead to the best mean-square deviation (MSD) performance, and describes the proposed method and illustrates its behavior via simulations.
Abstract: Diffusion LMS is a distributed algorithm that allows a network of nodes to solve estimation problems in a fully distributed manner by relying solely on local interactions. The algorithm consists of two steps: a consultation step whereby each node combines in a convex manner information collected from its neighbors and an adaptation step where the node updates its local estimate based on local data and on the data exchanged with the neighbors. Various forms of diffusion algorithms are possible such as combine-then-adapt (CTA) and adapt-then-combine (ATC) forms, in addition to probabilistic implementations where consultations are performed only with a subset of the neighbors chosen at random. In this paper we propose an alternative protocol to reduce the communications cost during the consultation process. Each node is limited to selecting only one of its neighbors for consultation, and we propose a dynamic technique that enables the node to pick from among its neighbors that neighbor that is likely to lead to the best mean-square deviation (MSD) performance. In other words, rather than picking nodes at random, the proposed algorithm is meant to enable nodes to perform the selection in a more informed manner. The paper describes the proposed method and illustrates its behavior via simulations.

Journal ArticleDOI
TL;DR: It is shown that retrieval of frequency content by a fast Fourier transform-search method, instead of only inspecting the angle of a particular root of the error predictor filter, enhances performance, particularly at very low SNR levels.
Abstract: We propose a robust and low complexity scheme to estimate and track carrier frequency from signals traveling under low signal-to-noise ratio (SNR) conditions in highly nonstationary channels. These scenarios arise in planetary exploration missions subject to high dynamics, such as the Mars exploration rover missions. The method comprises a bank of adaptive linear predictors (ALP) supervised by a convex combiner that dynamically aggregates the individual predictors. The adaptive combination is able to outperform the best individual estimator in the set, which leads to a universal scheme for frequency estimation and tracking. A simple technique for bias compensation considerably improves the ALP performance. It is also shown that retrieval of frequency content by a fast Fourier transform (FFT)-search method, instead of only inspecting the angle of a particular root of the error predictor filter, enhances performance, particularly at very low SNR levels. Simple techniques that enforce frequency continuity improve further the overall performance. In summary we illustrate by extensive simulations that adaptive linear prediction methods render a robust and competitive frequency tracking technique.

Proceedings ArticleDOI
01 Nov 2010
TL;DR: This paper develops adaptation algorithms that exhibit self-organization properties and apply them to the modeling of collective behavior in biological systems, such as fish schooling, to provide an explanation for the agile adjustment of network patterns of fish schools in the presence of predators.
Abstract: Adaptive networks consist of a collection of nodes with learning abilities that interact with each other locally in order to solve distributed processing and distributed inference problems in real-time Various algorithms and performance analyses have been put forward for such networks, such as the adapt-then-combine (ATC) and combine-then-adapt (CTA) diffusion algorithms, the probabilistic diffusion algorithm, and diffusion with adaptive weights over the links In this paper, we add mobility as another dimension and study the behavior of the network when the nodes move in pursuit/avoidance of a target Mobility leads naturally to an adaptive topology with changing neighborhoods Mobility also imposes physical constraints on the proximity among the nodes and on the velocity and location of the center of the network We develop adaptation algorithms that exhibit self-organization properties and apply them to the modeling of collective behavior in biological systems, such as fish schooling The results help provide an explanation for the agile adjustment of network patterns of fish schools in the presence of predators

Proceedings ArticleDOI
09 Nov 2010
TL;DR: This paper investigates the self-organization and cognitive abilities of adaptive networks when the individual agents are allowed to move in pursuit of an objective and applies the ensuing model to the foraging behavior of fish schools in search of food sources.
Abstract: In this paper we investigate the self-organization and cognitive abilities of adaptive networks when the individual agents are allowed to move in pursuit of an objective. The network as a whole acts as an adaptive entity with localized processing and is able to respond to stimuli in real-time. We apply the ensuing model to the foraging behavior of fish schools in search of food sources and reproduce their ability to move in remarkable coherence.

Proceedings ArticleDOI
03 Aug 2010
TL;DR: Two methods to estimate the jitter for superheterodyne receiver architectures and cognitive radio architectures at high sampling rates are proposed and a method to compensate for the jitters is proposed.
Abstract: Clock timing jitters refer to random perturbations in the sampling time in analog-to-digital converters (ADCs). The perturbations are caused by circuit imperfections in the sampling clock. This paper analyzes the effect of sampling clock jitter on the acquired samples. The paper proposes two methods to estimate the jitter for superheterodyne receiver architectures and cognitive radio architectures at high sampling rates. The paper also proposes a method to compensate for the jitter. The methods are tested and validated via computer simulations and theoretical analysis.

Proceedings ArticleDOI
03 Aug 2010
TL;DR: The algorithm estimates the jitter errors from the spurious sidebands and provides a way to compensate the distorted sampled data in the digital domain.
Abstract: In a non-ideal PLL circuit, leakage of the reference signal into the control line produces spurious tones. When the distorted PLL signal is used in an analog-to-digital converter (ADC), it creates spurious tones in the sampled data as well. This paper analyzes this effect and proposes a solution to remove the leakage effects. The algorithm estimates the jitter errors from the spurious sidebands and provides a way to compensate the distorted sampled data in the digital domain.

Journal ArticleDOI
TL;DR: This paper considers model combination methods for adaptive filtering that perform unbiased estimation and studies the steady-state performance of previously introduced methods as well as novel combination algorithms for stationary and nonstationary data that use stochastic gradient updates.
Abstract: In this paper, we consider model combination methods for adaptive filtering that perform unbiased estimation. In this widely studied framework, two adaptive filters are run in parallel, each producing unbiased estimates of an underlying linear model. The outputs of these two filters are combined using another adaptive algorithm to yield the final output of the system. Overall, we require that the final algorithm produce an unbiased estimate of the underlying model. We later specialize this framework where we combine one filter using the least-mean squares (LMS) update and the other filter using the least-mean fourth (LMF) update to decrease cross correlation in between the outputs and improve the overall performance. We study the steady-state performance of previously introduced methods as well as novel combination algorithms for stationary and nonstationary data. These algorithms use stochastic gradient updates instead of the variable transformations used in previous approaches. We explicitly provide steady-state analysis for both stationary and nonstationary environments. We also demonstrate close agreement with the introduced results and the simulations, and show for this specific combination, more than 2 dB gains in terms of excess mean square error with respect to the best constituent filter in the simulations.


Proceedings ArticleDOI
14 Mar 2010
TL;DR: Simulation results indicate that the MMSE cooperative beamforming method with mode selection achieves better performance compared to other beamforming methods: directbeamforming, relay beamforming, and cooperativebeamforming without mode selection.
Abstract: In this paper, we design cooperative beamforming weights for source, relay and destination nodes based on a minimum means-quare-error (MMSE) formulation under network power constraints. We also propose a mode selection procedure based on the instantaneous system throughput. Simulation results indicate that the MMSE cooperative beamforming method with mode selection achieves better performance compared to other beamforming methods: direct beamforming, relay beamforming, and cooperative beamforming without mode selection.