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324
Optimal linear cooperation for spectrum sensing in cognitive radio networks
 IEEE J. SEL. TOPICS SIGNAL PROCESS
, 2008
"... Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access underutilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect signal ..."
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Cited by 114 (8 self)
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Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access underutilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect signals from licensed primary radios to avoid harmful interference. However, due to the effects of channel fading/shadowing, individual cognitive radios may not be able to reliably detect the existence of a primary radio. In this paper, we propose an optimal linear cooperation framework for spectrum sensing in order to accurately detect the weak primary signal. Within this framework, spectrum sensing is based on the linear combination of local statistics from individual cognitive radios. Our objective is to minimize the interference to the primary radio while meeting the requirement of opportunistic spectrum utilization. We formulate the sensing problem as a nonlinear optimization problem. By exploiting the inherent structures in the problem formulation, we develop efficient algorithms to solve for the optimal solutions. To further reduce the computational complexity and obtain solutions for more general cases, we finally propose a heuristic approach, where we instead optimize a modified deflection coefficient that characterizes the probability distribution function of the global test statistics at the fusion center. Simulation results illustrate significant cooperative gain achieved by the proposed strategies. The insights obtained in this paper are useful for the design of optimal spectrum sensing in cognitive radio networks.
Networkbased wireless location
 IEEE Signal Process. Mag
, 2005
"... [Challenges faced in developing techniques for accurate wireless location information] Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety fea ..."
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Cited by 51 (1 self)
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[Challenges faced in developing techniques for accurate wireless location information] Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety feature of cellular systems in response to an order issued by the Federal Communications Commission (FCC) in 1996. The order mandated all wireless service providers to deliver accurate location information of an emergency 911 (E911) caller to public safety answering points (PSAPs). The FCC mandate aims to solve a serious public safety problem caused by the fact that, at present, a large proportion of all 911 calls originate from mobile phones, the location of which cannot be determined with existing technology. However, many difficulties intrinsic to the wireless environment make meeting the FCC objective challenging; these challenges include channel fading, low signaltonoise ratios (SNRs), multiuser interference, and multipath conditions. In addition to emergency services, there are many other applications for wireless location technology, including monitoring and tracking for security reasons, location sensitive
Prı́ncipe, “The kernel leastmeansquare algorithm
 IEEE Transactions on Signal Processing
, 2008
"... Abstract—The combination of the famed kernel trick and the leastmeansquare (LMS) algorithm provides an interesting samplebysample update for an adaptive filter in reproducing kernel Hilbert spaces (RKHS), which is named in this paper the KLMS. Unlike the accepted view in kernel methods, this pap ..."
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Cited by 50 (9 self)
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Abstract—The combination of the famed kernel trick and the leastmeansquare (LMS) algorithm provides an interesting samplebysample update for an adaptive filter in reproducing kernel Hilbert spaces (RKHS), which is named in this paper the KLMS. Unlike the accepted view in kernel methods, this paper shows that in the finite training data case, the KLMS algorithm is well posed in RKHS without the addition of an extra regularization term to penalize solution norms as was suggested by
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).
Multiuser twoway amplifyandforward relay processing andpower controlmethods for beamforming systems,” IEEETrans
 Signal Process
, 2010
"... Abstract—In this paper, multipleinput multipleoutput (MIMO) relay transceiver processing is proposed for multiuser twoway relay communications. The relay processing is optimized based on both zeroforcing (ZF) and minimum meansquareerror (MMSE) criteria under relay power constraints. Various tr ..."
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Cited by 44 (2 self)
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Abstract—In this paper, multipleinput multipleoutput (MIMO) relay transceiver processing is proposed for multiuser twoway relay communications. The relay processing is optimized based on both zeroforcing (ZF) and minimum meansquareerror (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 signaltonoise ratio (SNR). Numerical results show that the proposed multiuser twoway relay processing can efficiently eliminate both cochannel interference (CCI) and selfinterference (SI). Index Terms—Beamforming, minimum meansquare error (MMSE), multipleinput multipleoutput (MIMO), multiuser, power control, twoway relay, zeroforcing (ZF). I.
Variable StepSize NLMS and Affine Projection Algorithms
"... Abstract—This letter proposes two new variable stepsize algorithms for normalized least mean square and affine projection. The proposed schemes lead to faster convergence rate and lower misadjustment error. Index Terms—Adaptive filters, affine projection algorithm, normalized least mean square (NLM ..."
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Cited by 36 (0 self)
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Abstract—This letter proposes two new variable stepsize algorithms for normalized least mean square and affine projection. The proposed schemes lead to faster convergence rate and lower misadjustment error. Index Terms—Adaptive filters, affine projection algorithm, normalized least mean square (NLMS), variable stepsize. I.
Diffusion strategies for distributed Kalman filtering: formulation and performance analysis
 in Proceedings of the IAPR Workshop on Cognitive Information Processing
, 2008
"... We consider the problem of distributed Kalman filtering, where a set of nodes are required to collectively estimate the state of a linear dynamic system from their individual measurements. Our focus is on diffusion strategies, where nodes communicate with their direct neighbors only, and the informa ..."
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Cited by 34 (5 self)
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We consider the problem of distributed Kalman filtering, where a set of nodes are required to collectively estimate the state of a linear dynamic system from their individual measurements. Our focus is on diffusion strategies, where nodes communicate with their direct neighbors only, and the information is diffused across the network. We derive and analyze the mean and meansquare performance of the proposed algorithms and show by simulation that they outperform previous solutions. 1.
The quaternion LMS algorithm for adaptive filtering of hypercomplex real world processes
 IEEE Transactions on Signal Processing
"... Abstract—The quaternion least mean square (QLMS) algorithm is introduced for adaptive filtering of three and fourdimensional processes, such as those observed in atmospheric modeling (wind, vector fields). These processes exhibit complex nonlinear dynamics and coupling between the dimensions, whic ..."
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Cited by 30 (11 self)
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Abstract—The quaternion least mean square (QLMS) algorithm is introduced for adaptive filtering of three and fourdimensional processes, such as those observed in atmospheric modeling (wind, vector fields). These processes exhibit complex nonlinear dynamics and coupling between the dimensions, which make their componentwise processing by multiple univariate LMS, bivariate complex LMS (CLMS), or multichannel LMS (MLMS) algorithms inadequate. The QLMS accounts for these problems naturally, as it is derived directly in the quaternion domain. The analysis shows that QLMS operates inherently based on the so called “augmented ” statistics, that is, both the covariance and pseudocovariance of the tap input vector are taken into account. In addition, the operation in the quaternion domain facilitates fusion of heterogeneous data sources, for instance, the three vector dimensions of the wind field and air temperature. Simulations on both benchmark and real world data support the approach. Index Terms—Adaptive multistep ahead prediction, data fusion via vector spaces, multidimensional adaptive filters, quaternion signal processing, wind modeling. I.