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99
Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity
"... Abstract—A cooperative approach to the sensing task of wireless cognitive radio (CR) networks is introduced based on a basis expansion model of the power spectral density (PSD) map in space and frequency. Joint estimation of the model parameters enables identification of the (un)used frequency bands ..."
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Cited by 79 (7 self)
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Abstract—A cooperative approach to the sensing task of wireless cognitive radio (CR) networks is introduced based on a basis expansion model of the power spectral density (PSD) map in space and frequency. Joint estimation of the model parameters enables identification of the (un)used frequency bands at arbitrary locations, and thus facilitates spatial frequency reuse. The novel scheme capitalizes on two forms of sparsity: the first one introduced by the narrowband nature of transmitPSDs relative to the broad swaths of usable spectrum; and the second one emerging from sparsely located active radios in the operational space. An estimator of the model coefficients is developed based on the Lasso algorithm to exploit these forms of sparsity and reveal the unknown positions of transmitting CRs. The resultant scheme can be implemented via distributed online iterations, which solve quadratic programs locally (one per radio), and are adaptive to changes in the system. Simulations corroborate that exploiting sparsity in CR sensing reduces spatial and frequency spectrum leakage by 15 dB relative to leastsquares (LS) alternatives. Index Terms—Cognitive radios, compressive sampling, cooperative systems, distributed estimation, parallel network processing, sensing, sparse models, spectral analysis. I.
Distributed parameter estimation in sensor networks: Nonlinear observation models and imperfect communication
 IEEE Transactions on Information Theory
, 2012
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Giannakis, “Distributed sparse linear regression
 IEEE Trans. Signal Process
, 2010
"... Abstract—The Lasso is a popular technique for joint estimation and continuous variable selection, especially wellsuited for sparse and possibly underdetermined linear regression problems. This paper develops algorithms to estimate the regression coefficients via Lasso when the training data are di ..."
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Cited by 44 (8 self)
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Abstract—The Lasso is a popular technique for joint estimation and continuous variable selection, especially wellsuited for sparse and possibly underdetermined linear regression problems. This paper develops algorithms to estimate the regression coefficients via Lasso when the training data are distributed across different agents, and their communication to a central processing unit is prohibited for e.g., communication cost or privacy reasons. A motivating application is explored in the context of wireless communications, whereby sensing cognitive radios collaborate to estimate the radiofrequency power spectrum density. Attaining different tradeoffs between complexity and convergence speed, three novel algorithms are obtained after reformulating the Lasso into a separable form, which is iteratively minimized using the alternatingdirection method of multipliers so as to gain the desired degree of parallelization. Interestingly, the per agent estimate updates are given by simple softthresholding operations, and interagent communication overhead remains at affordable level. Without exchanging elements from the different training sets, the local estimates consent to the global Lasso solution, i.e., the fit that would be obtained if the entire data set were centrally available. Numerical experiments with both simulated and real data demonstrate the merits of the proposed distributed schemes, corroborating their convergence and global optimality. The ideas in this paper can be easily extended for the purpose of fitting related models in a distributed fashion, including the adaptive Lasso, elastic net, fused Lasso and nonnegative garrote. Index Terms—Distributed linear regression, Lasso, parallel optimization, sparse estimation. I.
Distributed LMS for ConsensusBased InNetwork Adaptive Processing
"... Abstract—Adaptive algorithms based on innetwork processing of distributed observations are wellmotivated for online parameter estimation and tracking of (non)stationary signals using ad hoc wireless sensor networks (WSNs). To this end, a fully distributed least meansquare (DLMS) algorithm is dev ..."
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Cited by 44 (4 self)
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Abstract—Adaptive algorithms based on innetwork processing of distributed observations are wellmotivated for online parameter estimation and tracking of (non)stationary signals using ad hoc wireless sensor networks (WSNs). To this end, a fully distributed least meansquare (DLMS) algorithm is developed in this paper, offering simplicity and flexibility while solely requiring singlehop communications among sensors. The resultant estimator minimizes a pertinent squarederror cost by resorting to i) the alternatingdirection method of multipliers so as to gain the desired degree of parallelization and ii) a stochastic approximation iteration to cope with the timevarying statistics of the process under consideration. Information is efficiently percolated across the WSN using a subset of “bridge ” sensors, which further tradeoff communication cost for robustness to sensor failures. For a linear data model and under mild assumptions aligned with those considered in the centralized LMS, stability of the novel DLMS algorithm is established to guarantee that local sensor estimation error norms remain bounded most of the time. Interestingly, this weak stochastic stability result extends to the pragmatic setup where intersensor communications are corrupted by additive noise. In the absence of observation and communication noise, consensus is achieved almost surely as local estimates are shown exponentially convergent to the parameter of interest with probability one. Meansquare error performance of DLMS is also assessed. Numerical simulations: i) illustrate that DLMS outperforms existing alternatives that rely either on information diffusion among neighboring sensors, or, local sensor filtering; ii) highlight its tracking capabilities; and iii) corroborate the stability and performance analysis results. Index Terms—Distributed estimation, LMS algorithm, wireless sensor networks (WSNs).
Distributed compressive spectrum sensing in cooperative multihop cognitive networks
 IEEE Journal of Selected Topics in Signal Processing
, 2010
"... Abstract—In wideband cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing, but entails several major technical challenges: very high sampling rates required for wideband processing, limited power and computing resources per CR, frequencyselectiv ..."
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Cited by 43 (0 self)
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Abstract—In wideband cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing, but entails several major technical challenges: very high sampling rates required for wideband processing, limited power and computing resources per CR, frequencyselective wireless fading, and interference due to signal leakage from other coexisting CRs. In this paper, a cooperative approach to wideband spectrum sensing is developed to overcome these challenges. To effectively reduce the data acquisition costs, a compressive sampling mechanism is utilized which exploits the signal sparsity induced by network spectrum underutilization. To collect spatial diversity against wireless fading, multiple CRs collaborate during the sensing task by enforcing consensus among local spectral estimates; accordingly, a decentralized consensus optimization algorithm is derived to attain high sensing performance at a reasonable computational cost and power overhead. To identify spurious spectral estimates due to interfering CRs, the orthogonality between the spectrum of primary users and that of CRs is imposed as constraints for consensus optimization during distributed collaborative sensing. These decentralized techniques are developed for both cases of with and without channel knowledge. Simulations testify the effectiveness of the proposed cooperative sensing approach in multihop CR networks. Index Terms—Collaborative sensing, compressive sampling, consensus optimization, distributed fusion, spectrum sensing. I.
Compressed wideband sensing in cooperative cognitive radio networks, in
 Proc. of IEEE GLOBECOM
"... Abstract — In emerging cognitive radio (CR) networks with spectrum sharing, the first cognitive task preceding any dynamic spectrum access is the sensing and identification of spectral holes in wireless environments. This paper develops a distributed compressed spectrum sensing approach for (ultra) ..."
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Cited by 34 (1 self)
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Abstract — In emerging cognitive radio (CR) networks with spectrum sharing, the first cognitive task preceding any dynamic spectrum access is the sensing and identification of spectral holes in wireless environments. This paper develops a distributed compressed spectrum sensing approach for (ultra)wideband CR networks. Compressed sensing is performed at local CRs to scan the very wide spectrum at practical signalacquisition complexity. Meanwhile, spectral estimates from multiple local CR detectors are fused to collect spatial diversity gain, which improves the sensing quality especially under fading channels. New distributed consensus algorithms are developed for collaborative sensing and fusion. Using only onehop local communications, these distributed algorithms converge fast to the globally optimal solutions even for multihop CR networks, at low communication and computation load scalable to the network size.
Convergence rate analysis of distributed gossip (linear parameter) estimation: Fundamental limits and tradeoffs
 IEEE Journal of Selected Topics in Signal Processing
, 2011
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Optimal Motion Strategies for Rangeonly Constrained Multisensor Target Tracking
, 2006
"... Abstract—In this paper, we study the problem of optimal trajectory generation for a team of mobile sensors tracking a moving target using distanceonly measurements. This problem is shown to be NPHard, in general, when constraints are imposed on the speed of the sensors. We propose two algorithms, ..."
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Cited by 26 (8 self)
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Abstract—In this paper, we study the problem of optimal trajectory generation for a team of mobile sensors tracking a moving target using distanceonly measurements. This problem is shown to be NPHard, in general, when constraints are imposed on the speed of the sensors. We propose two algorithms, modified GaussSeidelrelaxation and LPrelaxation, for determining the set of feasible locations that each sensor should move to in order to collect the most informative measurements; i.e., distance measurements that minimize the uncertainty about the position of the target. Furthermore, we prove that the motion strategy that minimizes the trace of the position error covariance matrix is equivalent to the one that maximizes the minimum eigenvalue of its inverse. The two proposed algorithms are applicable regardless of the process model that is employed for describing the motion of the target, while the computational complexity of both methods is linear in the number of sensors. Extensive simulation results are presented demonstrating that the performance attained with the proposed methods is comparable to that obtained with gridbased exhaustive search, whose computational cost is exponential in the number of sensors, and significantly better than that of a random, towards the target, motion strategy.
Distributed InNetwork Channel Decoding
"... Abstract—Average loglikelihood ratios (LLRs) constitute sufficient statistics for centralized maximumlikelihood block decoding as well as for a posteriori probability evaluation which enables bitwise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to p ..."
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Cited by 24 (9 self)
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Abstract—Average loglikelihood ratios (LLRs) constitute sufficient statistics for centralized maximumlikelihood block decoding as well as for a posteriori probability evaluation which enables bitwise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to perform these decoding tasks in a lowcomplexity distributed fashion using wireless sensor networks. At affordable communication overhead, the resultant distributed decoders rely on local message exchanges among singlehop neighboring sensors to achieve iteratively consensus on the average LLRs per sensor. Furthermore, the decoders exhibit robustness to nonideal intersensor links affected by additive noise and random link failures. Pairwise error probability bounds benchmark the decoding performance as a function of the number of consensus iterations. Interestingly, simulated tests corroborating the analytical findings demonstrate that only a few consensus iterations suffice for the novel distributed decoders to approach the performance of their centralized counterparts. Index Terms—Channel coding, decoding, distributed detection, wireless sensor networks (WSNs). I.
Polynomial Filtering for Fast Convergence in Distributed Consensus
, 2008
"... In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the literature address the consensus averaging problem by distributed linear iterative algorithms, with asymptotic convergenc ..."
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Cited by 22 (1 self)
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In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the literature address the consensus averaging problem by distributed linear iterative algorithms, with asymptotic convergence of the consensus solution. The convergence rate of such distributed algorithms typically depends on the network topology and the weights given to the edges between neighboring sensors, as described by the network matrix. In this paper, we propose to accelerate the convergence rate for given network matrices by the use of polynomial filtering algorithms. The main idea of the proposed methodology is to apply a polynomial filter on the network matrix that will shape its spectrum in order to increase the convergence rate. Such an algorithm is equivalent to periodic updates in each of the sensors by aggregating a few of its previous estimates. We formulate the computation of the coefficients of the optimal polynomial as a semidefinite program that can be efficiently and globally solved for both static and dynamic network topologies. We finally provide simulation results that demonstrate the effectiveness of the proposed solutions in accelerating the convergence of distributed consensus averaging problems.