Person: Tarokh, Vahid
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Tarokh
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Vahid
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Tarokh, Vahid
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Publication Distributed Dynamic Spectrum Allocation for Secondary Users in a Vertical Spectrum Sharing Scenario(Japan Science and Technology Agency (JST), 2012) Babadi, Behtash; Tarokh, VahidIn this paper, we study the problem of distributed spectrum allocation under a vertical spectrum sharing scenario in a cognitive radio network. The secondary users share the spectrum licensed to the primary user by observing the activity statistics of the primary users, and regulate their transmission strategy in order to abide by the spectrum sharing etiquette. When the primary user is inactive in a subset of the available frequency bands, from the perspective of the secondary users the problem reduces to a distributed horizontal spectrum sharing. For a specific class of networks, the latter problem is addressed by the recently proposed GADIA algorithm [1]. In this paper, we present analytical and numerical results on the performance of the GADIA algorithm in conjunction with the above-mentioned vertical spectrum sharing scenario. These results reveal near-optimal performance guarantees for the overall vertical spectrum sharing scenario.Publication Semidefinite Programming-Based Localization Algorithm in Networks with Inhomogeneous Media(ACM Press, 2012) Tarokh, Vahid; Blanes-Vidal, Victoria; Nadimi, EsmaeilIn this paper, we study the asymptotic properties of a semidefinite programming (SDP) based localization algorithm in a network with inhomogeneous RF transmission medium given incomplete and inaccurate pairwise distance measurements between sensors-sensors and sensors-anchors. We proposed a novel relaxed SDP approach based on a graph realization problem with noisy time-of-arrival (TOA) measurements with additive Gaussian noise and inaccurate transmission permittivity and permeability coefficients both with additive standard Gaussian noise (varying dielectric constant). Modeling the inhomogeneous RF transmission medium as a series of homogeneous transmission mediums between any two given points and given the true distances between a pair of sensors and the set of known pair-wise distances between sensors-sensors and sensors-anchors, an upper bound for the expected value of the optimal objective relaxed SDP problem is obtained, showing that its asymptotic properties potentially grows as fast as the summation of true distances between the pair of sensors-sensors and sensor-anchors and the TOA noisy measurements mean and standard deviation.Publication Regularized recursive least squares for anomaly detection in sparse channel tracking applications(ACM Press, 2011) Babadi, Behtash; Tarokh, VahidIn this paper, we study the problem of anomaly detection in sparse channel tracking applications via the \(l_1\)-regularized least squares adaptive filter (SPARLS). Anomalies arise due to unexpected adversarial changes in the channel and quick detection of these anomalies is desired. We first prove analytically that the prediction error of the SPARLS algorithm can be substantially lower than that of the widely-used Recursive Least Squares (RLS) algorithm. Furthermore, we present Receiver Operating Characteristic (ROC) curves for the detection/false alarm trade-off of anomaly detection in a sparse multi-path fading channel tracking scenario. These curves reveal the considerable advantage of the SPARLS algorithm over the RLS algorithm.Publication An Augmented Lagrangian Based Compressed Sensing Reconstruction for Non-Cartesian Magnetic Resonance Imaging without Gridding and Regridding at Every Iteration(Public Library of Science, 2014) Akcakaya, Mehmet; Nam, Seunghoon; Basha, Tamer A.; Kawaji, Keigo; Tarokh, Vahid; Nezafat, RezaBackground: Non-Cartesian trajectories are used in a variety of fast imaging applications, due to the incoherent image domain artifacts they create when undersampled. While the gridding technique is commonly utilized for reconstruction, the incoherent artifacts may be further removed using compressed sensing (CS). CS reconstruction is typically done using conjugate-gradient (CG) type algorithms, which require gridding and regridding to be performed at every iteration. This leads to a large computational overhead that hinders its applicability. Methods: We sought to develop an alternative method for CS reconstruction that only requires two gridding and one regridding operation in total, irrespective of the number of iterations. This proposed technique is evaluated on phantom images and whole-heart coronary MRI acquired using 3D radial trajectories, and compared to conventional CS reconstruction using CG algorithms in terms of quantitative vessel sharpness, vessel length, computation time, and convergence rate. Results: Both CS reconstructions result in similar vessel length (P = 0.30) and vessel sharpness (P = 0.62). The per-iteration complexity of the proposed technique is approximately 3-fold lower than the conventional CS reconstruction (17.55 vs. 52.48 seconds in C++). Furthermore, for in-vivo datasets, the convergence rate of the proposed technique is faster (60±13 vs. 455±320 iterations) leading to a ∼23-fold reduction in reconstruction time. Conclusions: The proposed reconstruction provides images of similar quality to the conventional CS technique in terms of removing artifacts, but at a much lower computational complexity.Publication Peak Power Reduction of OFDM Signals with Sign Adjustment(Institute of Electrical & Electronics Engineers, 2009) Sharif, M.; Tarokh, Vahid; Hassibi, B.It has recently been shown that significant reduction in the peak to mean envelope power (PMEPR) can be obtained by altering the sign of each subcarrier in a multicarrier system with n subcarriers. However, finding the best sign not only requires a search over \(2^n\) possible signs but also may lead to a substantial rate loss for small size constellations. In this paper, we first propose a greedy algorithm to choose the signs based on p-norm minimization and prove that the resulting PMEPR is guaranteed to be less than c log n where c is a constant independent of n for any n. This approach has lower complexity in each iteration compared to the derandomization approach of while achieving similar PMEPR reduction. We further improve the performance of the proposed algorithm by enlarging the search space using pruning. Simulation results show that PMEPR of a multicarrier signal with 128 subcarriers can be reduced to within 1.6 dB of the PMEPR of a single carrier system. In the second part of the paper, we address the rate loss by proposing a block coding scheme in which only one sign vector is chosen for K different modulating vectors. The sign vector can be computed using the greedy algorithm in n iterations. We show that the multi-symbol encoding approach can reduce the rate loss by a factor of K while achieving the PMEPR of c logKn, i.e., only logarithmic growth in K. Simulation results show that the rate loss can be made smaller than %10 at the cost of only 1 db increase in the resulting PMEPR for a system with 128 subcarriers.Publication Bayesian-based localization of wireless capsule endoscope using received signal strength(IEEE, 2014) Nadimi, Esmaeil S.; Blanes-Vidal, Victoria; Tarokh, Vahid; Johansen, Per MichaelIn wireless body area sensor networking (WBASN) applications such as gastrointestinal (GI) tract monitoring using wireless video capsule endoscopy (WCE), the performance of out-of-body wireless link propagating through different body media (i.e. blood, fat, muscle and bone) is still under investigation. Most of the localization algorithms are vulnerable to the variations of path-loss coefficient resulting in unreliable location estimation. In this paper, we propose a novel robust probabilistic Bayesian-based approach using received-signal-strength (RSS) measurements that accounts for Rayleigh fading, variable path-loss exponent and uncertainty in location information received from the neighboring nodes and anchors. The results of this study showed that the localization root mean square error of our Bayesian-based method was 1.6 mm which was very close to the optimum Cramer-Rao lower bound (CRLB) and significantly smaller than that of other existing localization approaches (i.e. classical MDS (64.2mm), dwMDS (32.2mm), MLE (36.3mm) and POCS (2.3mm)).Publication Improved accelerated breath-hold radial cine image reconstruction by acquiring additional free-breathing data between breath-holds(BioMed Central, 2012) Nam, Seunghoon; Akcakaya, Mehmet; Kwak, Yongjun; Goddu, Beth; Kissinger, Kraig V; Manning, Warren; Tarokh, Vahid; Nezafat, RezaPublication SPARLS: The Sparse RLS Algorithm(Institute of Electrical and Electronics Engineers, 2010) Babadi, Behtash; Kalouptsidis, Nicholas; Tarokh, VahidWe develop a recursive ${cal L}_{1}$-regularized least squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an expectation-maximization type algorithm. We prove the convergence of the SPARLS algorithm to a near-optimal estimate in a stationary environment and present analytical results for the steady state error. Simulation studies in the context of channel estimation, employing multipath wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely used recursive least squares (RLS) algorithm in terms of mean squared error (MSE). Moreover, these simulation studies suggest that the SPARLS algorithm (with slight modifications) can operate with lower computational requirements than the RLS algorithm, when applied to tap-weight vectors with fixed support.Publication GADIA: A Greedy Asynchronous Distributed Interference Avoidance Algorithm(Institute of Electrical and Electronics Engineers, 2010) Babadi, Behtash; Tarokh, VahidIn this paper, the problem of distributed dynamic frequency allocation is considered for a canonical communication network, which spans several networks such as cognitive radio networks and digital subscriber lines (DSLs). A greedy asynchronous distributed interference avoidance (GADIA) algorithm for horizontal spectrum sharing has been proposed that achieves performance close to that of a centralized optimal algorithm. The convergence of the GADIA algorithm to a near-optimal frequency allocation strategy is proved and several asymptotic performance bounds have been established for various spatial configurations of the network nodes. Furthermore, the near-equilibrium dynamics of the GADIA algorithm has been studied using the Glauber dynamics, by identifying the problem with the antiferromagnetic inhomogeneous long-range Potts model. Using the near-equilibrium dynamics and methods from stochastic analysis, the robustness of the algorithm with respect to time variations in the activity of network nodes is studied. These analytic results along with simulation studies reveal that the performance is close to that of an optimum centralized frequency allocation algorithm. Further simulation studies confirm that our proposed algorithm outperforms the iterative water-filling algorithm in the low signal-to-interference-plus-noise ratio (SINR) regime, in terms of achieved sum rate, complexity, convergence rate, and robustness to time-varying node activities.Publication Adaptive Greedy Algorithm With Application to Nonlinear Communications(Institute of Electrical and Electronics Engineers, 2010) Mileounis, Garasimos; Babadi, Behtash; Kalouptsidis, Nicholas; Tarokh, VahidGreedy algorithms form an essential tool for compressed sensing. However, their inherent batch mode discourages their use in time-varying environments due to significant complexity and storage requirements. In this paper two existing powerful greedy schemes developed in the literature are converted into an adaptive algorithm which is applied to estimation of a class of nonlinear communication systems. Performance is assessed via computer simulations on a variety of linear and nonlinear channels; all confirm significant improvements over conventional methods.