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  • 251. Pérez-Neira, Ana I.
    et al.
    Lagunas, Miguel Angel
    Rojas, Miguel Angel
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Correlation matching approach for spectrum sensing in open spectrum communications2009In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 57, no 12, p. 4823-4836Article in journal (Refereed)
  • 252.
    Quevedo, Daniel E.
    et al.
    School of Electrical Engineering & Computer Science, The University of Newcastle, Callaghan, Australien.
    Ahlén, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Ostergaard, Jan
    Dept of Electronic Systems, Aalborg University, Aalborg, Danmark.
    Energy Efficient State Estimation With Wireless Sensors Through the Use of Predictive Power Control and Coding2010In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 58, no 9, p. 4811-4823Article in journal (Refereed)
    Abstract [en]

    We study state estimation via wireless sensors over fading channels. Packet loss probabilities depend upon time-varying channel gains, packet lengths and transmission power levels of the sensors. Measurements are coded into packets by using either independent coding or distributed zero-error coding. At the gateway, a time-varying Kalman filter uses the received packets to provide the state estimates. To trade sensor energy expenditure for state estimation accuracy, we develop a predictive control algorithm which, in an online fashion, determines the transmission power levels and codebooks to be used by the sensors. To further conserve sensor energy, the controller is located at the gateway and sends coarsely quantized power increment commands, only whenever deemed necessary. Simulations based on real channel measurements illustrate that the proposed method gives excellent results.

  • 253.
    Radnosrati, Kamiar
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Linköping University.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Exploring Positive Noise in Estimation Theory2020In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, p. 3590-3602Article in journal (Refereed)
    Abstract [en]

    Estimation of the mean of a stochastic variable observed in noise with positive support is considered. It is well known from the literature that order statistics gives one order of magnitude lower estimation variance compared to the best linear unbiased estimator (BLUE). We provide a systematic survey of some common distributions with positive support, and provide derivations of minimum variance unbiased estimators (MVUE) based on order statistics, including BLUE for comparison. The estimators are derived with or without knowledge of the hyperparameters of the underlying noise distribution. Though the uniform, exponential and Rayleigh distributions, respectively, we consider are standard in literature, the problem of estimating the location parameter with additive noise from these distribution seems less studied, and we have not found any explicit expressions for BLUE and MVUE for these cases. In addition to additive noise with positive support, we also consider the mixture of uniform and normal noise distribution for which an order statistics-based unbiased estimator is derived. Finally, an iterative global navigation satellite system (GNSS) localization algorithm with uncertain pseudorange measurements is proposed which relies on the derived estimators for receiver clock bias estimation. Simulation data for GNSS time estimation and experimental GNSS data for joint clock bias and position estimation are used to evaluate the performance of the proposed methods.

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    fulltext
  • 254.
    Ramakrishna, Raksha
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Scaglione, Anna
    Arizona State Univ ASU, Sch Elect Comp & Energy Engn ECEE, Tempe, AZ 85287 USA..
    Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid2021In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 69, p. 2725-2739Article in journal (Refereed)
    Abstract [en]

    The underlying theme of this paper is to explore the various facets of power systems data through the lens of graph signal processing (GSP), laying down the foundations of the Grid-GSP framework. Grid-GSP provides an interpretation for the spatio-temporal properties of voltage phasor measurements, by showing how the well-known power systems modeling supports a generative low-pass graph filter model for the state variables, namely the voltage phasors. Using the model we formalize the empirical observation that voltage phasor measurement data lie in a low-dimensional subspace and tie their spatio-temporal structure to generator voltage dynamics. The Grid-GSP generative model is then successfully employed to investigate the problems, pertaining to the grid, of data sampling and interpolation, network inference, detection of anomalies and data compression. Numerical results on a large synthetic grid that mimics the real-grid of the state of Texas, ACTIVSg2000, and on real-world measurements from ISO-New England verify the efficacy of applying Grid-GSP methods to electric grid data.

  • 255. Ren, Jiaying
    et al.
    Zhang, Tianyi
    Li, Jian
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Sinusoidal parameter estimation from signed measurements via majorization–minimization based RELAX2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 8, p. 2173-2186Article in journal (Refereed)
  • 256.
    Ren, Xiaoqiang
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore.
    Mo, Yilin
    Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore..
    Secure Detection: Performance Metric and Sensor Deployment Strategy2018In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 17, p. 4450-4460Article in journal (Refereed)
    Abstract [en]

    This paper studies how to deploy sensors in the context of detection in adversarial environments. A fusion center is performing a binary hypothesis testing based on measurements from remotely deployed heterogeneous sensors. An attacker may compromise some of the deployed sensors, which send arbitrary measurements to the fusion center. The problems of interest are: to characterize the performance of the system under attack and, thus, develop a performance metric; and to deploy sensors within a cost budget, such that the proposed performance metric is maximized. In this paper, we first present a performance metric by formulating the detection in adversarial environments in a game theoretic way. A Nash equilibrium pair of the detection algorithm and attack strategy, with the deployed sensors given, is provided and the corresponding detection performance is adopted as the performance metric. We then show that the optimal sensor deployment can be determined approximately by solving a group of unbounded knapsack problems. We also show that the performance metric gap between the optimal sensor deployment and the optimal one with sensors being identical is within a fixed constant for any cost budget. The main results are illustrated by numerical examples.

  • 257.
    Rojas, Cristian R.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Katselis, Dimitrios
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Note on the SPICE Method2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 18, p. 4545-4551Article in journal (Refereed)
    Abstract [en]

    In this article, we analyze the SPICE method developed in [1], and establish its connections with other standard sparse estimation methods such as the Lasso and the LAD-Lasso. This result positions SPICE as a computationally efficient technique for the calculation of Lasso-type estimators. Conversely, this connection is very useful for establishing the asymptotic properties of SPICE under several problem scenarios and for suggesting suitable modifications in cases where the naive version of SPICE would not work.

  • 258.
    Ronnow, Daniel
    et al.
    Univ Gävle, Dept Elect Math & Nat Sci, S-80176 Gävle, Sweden..
    Händel, Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Nonlinear Distortion Noise and Linear Attenuation in MIMO Systems-Theory and Application to Multiband Transmitters2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 20, p. 5203-5212Article in journal (Refereed)
    Abstract [en]

    Nonlinear static multiple-input multiple-output (MIMO) systems are analyzed. The matrix formulation of Bussgang's theorem for complex Gaussian signals is rederived and put in the context of the multivariate cumulant series expansion. The attenuation matrix is a function of the input signals' covariance and the covariance of the input and output signals. The covariance of the distortion noise is in addition a function of the output signal's covariance. The effect of the observation bandwidth is discussed. Models of concurrent multiband transmitters are analyzed. For a transmitter with dual non-contiguous hands expressions for the normalized mean square error (NMSE) vs input signal power are derived for uncorrelated, partially correlated, and correlated input signals. A transmitter with arbitrary number of non-contiguous hands is analysed for correlated and uncorrelated signals. In an example, the NMSE is higher when the input signals are correlated than when they are uncorrelated for the same input signal power and it increases with the number of frequency hands. A concurrent dual band amplifier with contiguous bands is analyzed; in this case the NMSE depends on the bandwidth of the aggregated signal.

  • 259.
    Rosato, Conor
    et al.
    Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3BX, Merseyside, England..
    Devlin, Lee
    Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3BX, Merseyside, England..
    Beraud, Vincent
    Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3BX, Merseyside, England..
    Horridge, Paul
    Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3BX, Merseyside, England..
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Artificial Intelligence.
    Maskell, Simon
    Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3BX, Merseyside, England..
    Efficient Learning of the Parameters of Non-Linear Models Using Differentiable Resampling in Particle Filters2022In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 70, p. 3676-3692Article in journal (Refereed)
    Abstract [en]

    It has been widelydocumented that the sampling and resampling steps in particle filters cannot be differentiated. The reparameterisation trick was introduced to allow the sampling step to be reformulated into a differentiable function. We extend the reparameterisation trick to include the stochastic input to resampling therefore limiting the discontinuities in the gradient calculation after this step. Knowing the gradients of the prior and likelihood allows us to run particle Markov Chain Monte Carlo (p-MCMC) and use the No-U-Turn Sampler (NUTS) as the proposal when estimating parameters. We compare the Metropolis-adjusted Langevin algorithm (MALA), Hamiltonian Monte Carlo with different number of steps and NUTS. We consider three state-space models and show that NUTS improves the mixing of the Markov chain and can produce more accurate results in less computational time.

  • 260.
    Rönnow, Daniel
    et al.
    University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electrical Engineering, Mathematics and Science, Electronics.
    Händel, Peter
    Signal Processing, Royal Institute of Technology, Stockholm, Sweden.
    Nonlinear distortion noise and linear attenuation in MIMO systems - theory and application to multiband transmitters2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 20, p. 5203-5212, article id 8805179Article in journal (Refereed)
    Abstract [en]

    Nonlinear static multiple-input multiple-output (MIMO) systems are analyzed. The matrix formulation of Bussgang's theorem for complex Gaussian signals is rederived and put in the context of the multivariate cumulant series expansion. The attenuation matrix is a function of the input signals' covariance and the covariance of the input and output signals. The covariance of the distortion noise is in addition a function of the output signal's covariance. The effect of the observation bandwidth is discussed. Models of concurrent multiband transmitters are analyzed. For a transmitter with dual non-contiguous bands expressions for the normalized mean square error (NMSE) vs input signal power are derived for uncorrelated, partially correlated, and correlated input signals. A transmitter with arbitrary number of non-contiguous bands is analysed for correlated and uncorrelated signals. In an example, the NMSE is higher when the input signals are correlated than when they are uncorrelated for the same input signal power and it increases with the number of frequency bands. A concurrent dual band amplifier with contiguous bands is analyzed; in this case the NMSE depends on the bandwidth of the aggregated signal.

  • 261.
    Saha, Saikat
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Particle Filtering With Dependent Noise Processes2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 9, p. 4497-4508Article in journal (Refereed)
    Abstract [en]

    Modeling physical systems often leads to discrete time state-space models with dependent process and measurement noises. For linear Gaussian models, the Kalman filter handles this case, as is well described in literature. However, for nonlinear or non-Gaussian models, the particle filter as described in literature provides a general solution only for the case of independent noise. Here, we present an extended theory of the particle filter for dependent noises with the following key contributions: i) The optimal proposal distribution is derived; ii) the special case of Gaussian noise in nonlinear models is treated in detail, leading to a concrete algorithm that is as easy to implement as the corresponding Kalman filter; iii) the marginalized (Rao-Blackwellized) particle filter, handling linear Gaussian substructures in the model in an efficient way, is extended to dependent noise; and, finally, iv) the parameters of a joint Gaussian distribution of the noise processes are estimated jointly with the state in a recursive way.

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    Particle Filtering With Dependent Noise Processes
  • 262.
    Saha, Saikat
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Mandal, Pranab K
    University of Twente, The Netherlands.
    Bagchi, Arunabha
    University of Twente, The Netherlands.
    Boers, Yvo
    Thales Nederland BV, The Netherlands.
    Driessen, Johannes N.
    Thales Nederland BV, The Netherlands.
    Particle Based Smoothed Marginal MAP Estimation For General State Space Models2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 2, p. 264-273Article in journal (Refereed)
    Abstract [en]

    We consider the smoothing problem for a general state space system using sequential Monte Carlo(SMC) methods. The marginal smoother is assumed to be available in the form of weighted randomparticles from the SMC output. New algorithms are developed to extract the smoothed marginal maximuma posteriori (MAP) estimate of the state from the existing marginal particle smoother. Our method doesnot need any kernel fitting to obtain the posterior density from the particle smoother. The proposedestimator is then successfully applied to find the unknown initial state of a dynamical system and toaddress the issue of parameter estimation problem in state space models

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    MMAP_IEEE_TSP
  • 263.
    Saritas, Serkan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Gezici, Sinan
    Bilkent Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey..
    Yuksel, Serdar
    Queens Univ, Dept Math & Stat, Kingston, ON K7L 3N6, Canada..
    Hypothesis Testing Under Subjective Priors and Costs as a Signaling Game2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 19, p. 5169-5183Article in journal (Refereed)
    Abstract [en]

    Many communication, sensor network, and networked control problems involve agents (decision makers) which have either misaligned objective functions or subjective probabilistic models. In the context of such setups, we consider binary signaling problems in which the decision makers (the transmitter and the receiver) have subjective priors and/or misaligned objective functions. Depending on the commitment nature of the transmitter to his policies, we formulate the binary signaling problem as a Bayesian game under either Nash or Stackelberg equilibrium concepts and establish equilibrium solutions and their properties. We show that there can be informative or non-informative equilibria in the binary signaling game under the Stackelberg and Nash assumptions, and derive the conditions under which an informative equilibrium exists for the Stackelberg and Nash setups. For the corresponding team setup, however, an equilibrium typically always exists and is always informative. Furthermore, we investigate the effects of small perturbations in priors and costs on equilibrium values around the team setup (with identical costs and priors), and show that the Stackelberg equilibrium behavior is not robust to small perturbations whereas the Nash equilibrium is.

  • 264.
    Scarlett, Jonathan
    et al.
    Department of Engineering, University of Cambridge, UK.
    Evans, Jamie S
    Department of Electrical and Electronic Engineering, The University of Melbourne, Australien.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Compressed sensing with prior information: Information-theoretic limits and practical decoders2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 2, p. 427-439Article in journal (Refereed)
  • 265.
    Schön, Thomas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nordlund, Per-Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Marginalized Particle Filters for Mixed Linear/Nonlinear State-Space Models2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, no 7, p. 2279-2289Article in journal (Refereed)
    Abstract [en]

    The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. The main contribution in this paper is the derivation of the details for the marginalized particle filter for a general nonlinear state-space model. Several important special cases occurring in typical signal processing applications will also be discussed. The marginalized particle filter is applied to an integrated navigation system for aircraft. It is demonstrated that the complete high-dimensional system can be based on a particle filter using marginalization for all but three states. Excellent performance on real flight data is reported.

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    fulltext
  • 266. Schüldt, Christian
    et al.
    Lindström, Fredric
    Claesson, Ingvar
    A Low-Complexity Delayless Selective Subband Adaptive Filtering Algorithm2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 12, p. 5840-5850Article in journal (Refereed)
    Abstract [en]

    Adaptive filters of significant order, requiring high computational complexity, are necessary in many applications such as acoustic echo cancellation and wideband active noise control. Successful approaches to lessen the computational complexity of such filters are subband methods, and partial updating schemes where only a part of the filter is updated at each instant. To avoid the time delay introduced by the subband-splitting, delayless structures which reconstructs a fullband filter, producing delayless output, from the adaptive subband filters have been proposed. This paper proposes a delayless subband adaptive filter partial updating scheme, where the general idea is to only update the most misadjusted subband filter(s). Analysis in terms of mean square deviation is presented and shows that the fullband filter convergence speed is significantly increased, even for flat spectrum signals, as compared to traditional periodic subband filter update with the same computational complexity. Echo cancellation simulations with an artificial system to verify the analysis, using both flat spectrum signals and speech, is also presented, as well as offline calculations using signals from a real system.

  • 267. Sedighi, S.
    et al.
    Mishra, K. V.
    Mysore R, B. S.
    Ottersten, Björn
    Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, Luxembourg.
    Localization with One-Bit Passive Radars in Narrowband Internet-of-Things using Multivariate Polynomial Optimization2021In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476Article in journal (Refereed)
  • 268. Sedighi, S.
    et al.
    Rao, B. S. M. R.
    Ottersten, Björn
    An Asymptotically Efficient Weighted Least Squares Estimator for Co-Array-Based DoA Estimation2020In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 68, p. 589-604, article id 8907451Article in journal (Refereed)
    Abstract [en]

    Co-array-based Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable interest in array processing thanks to its capability of providing enhanced degrees of freedom. Although the literature presents a variety of estimators in this context, none of them are proven to be statistically efficient. This work introduces a novel estimator for the co-array-based DoA estimation employing the Weighted Least Squares (WLS) method. An analytical expression for the large sample performance of the proposed estimator is derived. Then, an optimal weighting is obtained so that the asymptotic performance of the proposed WLS estimator coincides with the Cramér-Rao Bound (CRB), thereby ensuring asymptotic statistical efficiency of resulting WLS estimator. This implies that the proposed WLS estimator has a significantly better performance compared to existing methods. Numerical simulations are provided to validate the analytical derivations and corroborate the improved performance.

  • 269. Sedighi, Saeid
    et al.
    Mysore R., Bhavani Shankar
    Soltanalian, Mojtaba
    Ottersten, Björn
    Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg.
    On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays2021In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 69, p. 6165-6182Article in journal (Refereed)
    Abstract [en]

    Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to their capability to provide enhanced degrees of freedom in resolving uncorrelated source signals. Additionally, deployment of one-bit Analog-to-Digital Converters (ADCs) has emerged as an important topic in array processing, as it offers both a low-cost and a low-complexity implementation. In this paper, we study the problem of DoA estimation from one-bit measurements received by an SLA. Specifically, we first investigate the identifiability conditions for the DoA estimation problem from one-bit SLA data and establish an equivalency with the case when DoAs are estimated from infinite-bit unquantized measurements. Towards determining the performance limits of DoA estimation from one-bit quantized data, we derive a pessimistic approximation of the corresponding Cramér-Rao Bound (CRB). This pessimistic CRB is then used as a benchmark for assessing the performance of one-bit DoA estimators. We also propose a new algorithm for estimating DoAs from one-bit quantized data. We investigate the analytical performance of the proposed method through deriving a closed-form expression for the covariance matrix of the asymptotic distribution of the DoA estimation errors and show that it outperforms the existing algorithms in the literature. Numerical simulations are provided to validate the analytical derivations and corroborate the resulting performance improvement. 

  • 270. Sezgin, A.
    et al.
    Jorswieck, Eduard Axel
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Costa, E.
    LDC in MIMO Ricean channels: Optimal transmit strategy with MMSE detection2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 1, p. 313-328Article in journal (Refereed)
    Abstract [en]

    In this paper, we study a MIMO system with a transmitter using a linear dispersion code (LDC) and a linear minimum mean square-error (MMSE) detector at the receiver in a Ricean flat-fading environment. We assume that the receiver has perfect channel state information and the transmitter knows only the mean channel matrix either by feedback or channel estimation. The focus of our work is the analysis of the optimal transmit strategy using different types of LDC. On the one hand, we consider spatial multiplexing schemes that achieve high data rates, but sacrifice diversity. On the other hand, we have schemes that achieve full diversity like quasi-orthogonal space-time block codes or orthogonal space-time block code. Depending on the LDC in use, the optimization problem is either convex or nonconvex. For both of these classes of LDC, we first derive the properties of the average normalized MSE and then analyze the impact of the mean component on the MSE, the optimal transmit strategy and the optimal power allocation. Finally, we derive some bounds on the error rate performance for different scenarios with the MMSE receiver.

  • 271.
    Sezgin, Aydin
    et al.
    Information Systems Laboratory, Stanford University, USA.
    Jorswieck, Eduard A.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Henkel, Oliver
    Fraunhofer-Institute for Telecommunications, 10587 Berlin, Germany .
    Pereira, Stephanie
    Information Systems Laboratory, Stanford University, CA 94305 USA.
    Paulraj, Arogyaswami
    Information Systems Laboratory, Stanford University, CA 94305 USA .
    On the relation of OSTBC and code rate one QSTBC: Average rate, BER, and coding gain2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 10, p. 4879-4891Article in journal (Refereed)
    Abstract [en]

    Recently, the statistical properties of the equivalent channel representation of a multiple-input-multiple output (MIMO) system employing code rate one quasi-orthogonal space-time block codes (QSTBC), which are constructed by using orthogonal space-time block codes (OSTBC) as building elements, was characterized. Based on these characterizations we analyze the average rate (or mean mutual information), the bit-error-rate performance, and the coding gain achieved with QSTBC for any number of receive and n(T) = 2(n), n >= 2 transmit antennas. First, we study constellation rotation using a systematic approach in order to maximize the coding gain and to achieve full diversity QSTBC. Moreover, we present an upper bound on the coding gain. We derive a lower and upper bound on the BER-performance for QSTBC. Furthermore, we analyze the average rate achievable with QSTBC in case of an uninformed transmitter and also the case, in which the transmitter knows the mean channel matrix whereas the receiver has perfect CSI. Along with the analysis, we compare all the results of these performance measures with the results achieved with OSTBC, revealing important connections between OSTBC and QSTBC. For example, the coding gain of a QSTBC is upper bounded by the coding gain of the underlying OSTBC. Also, the BER of a QSTBC for n(T),T transmit and n(R) receive antennas is tightly lower bounded by the BER of a full-diversity providing intersymbol-interference free system. In addition to that, we show that gains in terms of average rate by using a QSTBC (and, thus, with higher n(T)) instead of the underlying OSTBC are only attainable, if the available channel state information at the transmitter (CSIT) is utilized. Finally, we illustrate our theoretical results using numerical simulations.

  • 272.
    Shariati, Nafiseh
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Wang, Jiaheng
    Southeast University, Nanjing.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Robust Training Sequence Design for Correlated MIMO Channel Estimation2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 1, p. 107-120Article in journal (Refereed)
    Abstract [en]

    We study how to design a worst-case robust training sequence for multiple-input multiple-output (MIMO) channel estimation. We consider mean-squared error of channel estimates as the figure of merit which is a function of second-order statistics of the MIMO channel, i.e., channel covariance matrix, in order to optimize training sequences under a total power constraint. In practical applications, the channel covariance matrix is not known perfectly. Thus the main aspect of our design is to improve robustness of the training sequences against possible uncertainties in the available channel covariance matrix. Using a deterministic uncertainty model, we formulate a robust training sequence design as a minimax optimization problem where we take such imperfections into account. We investigate the robust design problem assuming the general case of an arbitrarily correlated MIMO channel and a non-empty compact convex uncertainty set. We prove that such a problem admits a globally optimal solution by exploiting the convex-concave structure of the objective function, and propose numerical algorithms to address the robust training design problem. We proceed the analysis by considering multiple-input single-output (MISO) channels and Kronecker structured MIMO channels along with unitarily-invariant uncertainty sets. For these scenarios, we show that the problem is diagonalized by the eigenvectors of the nominal covariance matrices so that the robust design is significantly simplified from a complex matrix-variable problem to a real vector-variable power allocation problem. For the MISO channel, we provide closed-form solutions for the robust training sequences with the uncertainty sets defined by the spectral norm and nuclear norm.

  • 273. Sharma, S. K.
    et al.
    Chatzinotas, S.
    Ottersten, Björn
    University of Luxembourg.
    Compressive sparsity order estimation for wideband cognitive radio receiver2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 19, p. 4984-4996Article in journal (Refereed)
  • 274.
    Sheikh, Zaka Ullah
    et al.
    Linköping University, Department of Electrical Engineering, Electronics System. Linköping University, The Institute of Technology.
    Johansson, Håkan
    Linköping University, Department of Electrical Engineering, Electronics System. Linköping University, The Institute of Technology.
    A Technique for Efficient Realization of Wide-Band FIR LTI Systems2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 3, p. 1482-1486Article in journal (Refereed)
    Abstract [en]

    This correspondence introduces a technique for efficient realization of wide-band finite-length impulse response (FIR) linear and timeinvariant (LTI) systems. It divides the overall frequency region into three subregions through lowpass, bandpass, and highpass filters realized in terms of only one filter. The actual function to be approximated is in the low- and high-frequency regions realized using periodic subsystems. In this way, one can realize an overall wide-band LTI function in terms of three low-cost subblocks, leading to a reduced overall arithmetic complexity as compared to the regular realization. A systematic design technique is provided and a detailed example shows multiplication and addition savings of 62 and 48 percent, respectively, for a fractional-order differentiator with a 96 percent utilization of the bandwidth. Another example shows that the savings increase/decrease with increased/decreased bandwidth.

  • 275.
    Sheikh, Zaka Ullah
    et al.
    Linköping University, Department of Electrical Engineering, Electronics System. Linköping University, The Institute of Technology.
    Johansson, Håkan
    Linköping University, Department of Electrical Engineering, Electronics System. Linköping University, The Institute of Technology.
    Efficient Wide-Band FIR LTI Systems Derived Via Multi-Rate Techniques and Sparse Bandpass2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 7, p. 3859-3863Article in journal (Refereed)
    Abstract [en]

    This correspondence introduces efficient realizations of wide-band LTI systems. They are single-rate realizations but derived via multirate techniques and sparse bandpass filters. The realizations target mid-band systems with narrow don’t-care bands near the zero and Nyquist frequencies. Design examples for fractional-order differentiators demonstrate substantial complexity savings as compared to the conventional minimax-optimal direct-form realizations.

  • 276.
    Shi, S.
    et al.
    Department of Electrical Engineering (ISY), Linköping University.
    Larsson, Erik
    Department of Electrical Engineering (ISY), Linköping University.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Codebook Design and Hybrid Digital/Analog Coding for Parallel Rayleigh Fading Channels2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 10, p. 5091-5096Article in journal (Refereed)
    Abstract [en]

    Low-delay source-channel transmission over parallel fading channels is studied. In this scenario separate source and channel coding is in general highly suboptimal. A scheme based on hybrid digital/analog joint source-channel coding is therefore proposed, employing scalar quantization and polynomial-based analog bandwidth expansion. Simulations demonstrate substantial performance gains.

  • 277.
    Shi, Shuying
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Skoglund, Mikael
    Royal Institute of Technology (KTH), Stockholm.
    Codebook Design and Hybrid Digital/AnalogCoding for Parallel Rayleigh Fading Channels2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 10, p. 5091-5096Article in journal (Refereed)
    Abstract [en]

    Low-delay source-channel transmission over parallel fading channels is studied. In this scenario separate sourceand channel coding is highly suboptimal. A scheme based on hybrid digital/analog joint source-channel coding istherefore proposed, employing scalar quantization and polynomial-based analog bandwidth expansion. Simulationsdemonstrate substantial performance gains.

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  • 278.
    Shirazinia, Amirpasha
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Analysis-by-Synthesis Quantization for Compressed Sensing Measurements2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 22, p. 5789-5800Article in journal (Refereed)
    Abstract [en]

    We consider a resource-limited scenario where a sensor that uses compressed sensing (CS) collects a low number of measurements in order to observe a sparse signal, and the measurements are subsequently quantized at a low bit-rate followed by transmission or storage. For such a scenario, we design new algorithms for source coding with the objective of achieving good reconstruction performance of the sparse signal. Our approach is based on an analysis-by-synthesis principle at the encoder, consisting of two main steps: 1) the synthesis step uses a sparse signal reconstruction technique for measuring the direct effect of quantization of CS measurements on the final sparse signal reconstruction quality, and 2) the analysis step decides appropriate quantized values to maximize the final sparse signal reconstruction quality. Through simulations, we compare the performance of the proposed quantization algorithms vis-a-vis existing quantization schemes.

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  • 279.
    Shirazinia, Amirpasha
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Joint Source-Channel Vector Quantization for Compressed Sensing2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 14, p. 3667-3681Article in journal (Refereed)
    Abstract [en]

    We study joint source-channel coding (JSCC) of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a framework for realizing optimum JSCC schemes that enable encoding and transmitting CS measurements of a sparse source over discrete memoryless channels, and decoding the sparse source signal. For this purpose, the optimal design of encoder-decoder pair of a VQ is considered, where the optimality is addressed by minimizing end-to-end mean square error (MSE). We derive a theoretical lower bound on the MSE performance and propose a practical encoder-decoder design through an iterative algorithm. The resulting coding scheme is referred to as channel-optimized VQ for CS, coined COVQ-CS. In order to address the encoding complexity issue of the COVQ-CS, we propose to use a structured quantizer, namely low-complexity multistage VQ (MSVQ). We derive new encoding and decoding conditions for the MSVQ and then propose a practical encoder-decoder design algorithm referred to as channel-optimized MSVQ for CS, coined COMSVQ-CS. Through simulation studies, we compare the proposed schemes vis-a-vis relevant quantizers.

  • 280.
    Shirazinia, Amirpasha
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Power Constrained Sparse Gaussian Linear Dimensionality Reduction over Noisy Communication Channels2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 21, p. 5837-5852Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate power-constrained sensing matrix design in a sparse Gaussian linear dimensionality reduction framework. Our study is carried out in a single–terminal setup as well as in a multi–terminal setup consisting of orthogonal or coherent multiple access channels (MAC).We adopt the mean square error (MSE) performance criterion for sparse source reconstruction in a system where source-to-sensor channel(s) and sensor-to-decoder communication channel(s) are noisy. Our proposed sensing matrix design procedure relies upon minimizing a lower-bound on the MSE in single– and multiple–terminal setups. We propose a three-stage sensing matrix optimization scheme that combines semi-definite relaxation (SDR) programming, a low-rank approximation problem and power-rescaling. Under certain conditions, we derive closedform solutions to the proposed optimization procedure. Through numerical experiments, by applying practical sparse reconstruction algorithms, we show the superiority of the proposed scheme by comparing it with other relevant methods. This performance improvement is achieved at the price of higher computational complexity. Hence, in order to address the complexity burden, we present an equivalent stochastic optimization method to the problem of interest that can be solved approximately, while still providing a superior performance over the popular methods.

  • 281.
    Shirazinia, Amirpasha
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Dey, Subhrakanti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Ciuonzo, D
    Salvo-Rossi, P
    Massive MIMO for decentralized estimation of a correlated source2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 10, p. 2499-2512Article in journal (Refereed)
    Abstract [en]

    We consider a decentralized multi-sensor estimation problem where L sensor nodes observe noisy versions of a correlated random source vector. The sensors amplify and forward their observations over a fading coherent multiple access channel (MAC) to a fusion center (FC). The FC is equipped with a large array of N antennas and adopts a minimum mean-square error (MMSE) approach for estimating the source. We optimize the amplification factor (or equivalently transmission power) at each sensor node in two different scenarios: a) with the objective of total power minimization subject to mean square error (MSE) of source estimation constraint, and b) with the objective of minimizing MSE subject to total power constraint. For this purpose, based on the well-known favorable propagation condition (when L << N) achieved in massive multiple-input multiple-output (MIMO), we apply an asymptotic approximation on the MSE and use convex optimization techniques to solve for the optimal sensor power allocation in a) and b). In a), we show that the total power consumption at the sensors decays as 1/N, replicating the power savings obtained in massive MIMO mobile communications literature. We also show several extensions of the aforementioned scenarios to the cases where sensor-to-FC fading channels are correlated, and channel coefficients are subject to estimation error. Through numerical studies, we also illustrate the superiority of the proposed optimal power allocation methods over uniform power allocation.

  • 282.
    Skog, Isaac
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Synchronization by Two-Way Message Exchanges: Cramer-Rao Bounds, Approximate Maximum Likelihood, and Offshore Submarine Positioning2010In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 58, no 4, p. 2351-2362Article in journal (Refereed)
    Abstract [en]

    Accurate clock synchronization is vital to many applications of wireless sensor networks (WSNs). The availability of a mathematical tool that at an early design stage can provide insight into the theoretically achievable performance of the clock synchronization may accordingly be valuable in the initial design phase of the network. Therefore, the achievable clock synchronization accuracy is examined in a WSN employing a two-way message exchange model under a Gaussian assumption. The Cramer-Rao bound for the estimation of the clock parameters is derived for four different parameterizations (i. e., different nuisance parameters), reflecting different levels of prior knowledge concerning the system parameters. The results on the Cramer-Rao bound are transformed into a lower bound on the mean square error of the clock offset, a figure of merit often more relevant, characterizing the system performance. Further, by introducing a set of artificial observations through a linear combination of the observations originally obtained in the two-way message exchange, an approximate maximum likelihood estimator for the clock parameters is proposed. The estimator is shown to be of low complexity and it obeys near-optimal performance, that is, a mean square error in the vicinity of the Cramer-Rao bound. The applicability of the derived results is shown through a simulation study of an offshore engineering scenario, where a remotely operated underwater vehicle is used for operations at the seabed. The position of the vehicle is tracked using a WSN.

  • 283.
    Skog, Isaac
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Nilsson, John-Olof
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Handel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Nehorai, Arye
    Inertial Sensor Arrays, Maximum Likelihood, and Cramer-Rao Bound2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 16, p. 4218-4227Article in journal (Refereed)
  • 284.
    Soltanalian, Mojtaba
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Naghsh, Mohammad Mahdi
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    On meeting the peak correlation bounds2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 5, p. 1210-1220Article in journal (Refereed)
    Abstract [en]

    In this paper, we study the problem of meeting peak periodic or aperiodic correlation bounds for complex-valued sets of sequences. To this end, the Welch, Levenstein, and Exponential bounds on the peak inner-product of sequence sets are considered and used to provide compound peak correlation bounds in both periodic and aperiodic cases. The peak aperiodic correlation bound is further improved by using the intrinsic dimension deficiencies associated with its formulation. In comparison to the compound bound, the new aperiodic bound contributes an improvement of more than 35% for some specific values of the sequence length n and set cardinality m. We study the tightness of the provided bounds by using both analytical and computational tools. In particular, novel algorithms based on alternating projections are devised to approach a given peak periodic or aperiodic correlation bound. Several numerical examples are presented to assess the tightness of the provided correlation bounds as well as to illustrate the effectiveness of the proposed methods for meeting these bounds.

  • 285.
    Soltanalian, Mojtaba
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Computational Design of Sequences With Good Correlation Properties2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 5, p. 2180-2193Article in journal (Refereed)
    Abstract [en]

    In this paper, we introduce a computational framework based on an iterative twisted approximation (ITROX) and a set of associated algorithms for various sequence design problems. The proposed computational framework can be used to obtain sequences (or complementary sets of sequences) possessing good periodic or aperiodic correlation properties and, in an extended form, to construct zero (or low) correlation zone sequences. Furthermore, as constrained (e. g., finite) alphabets are of interest in many applications, we introduce a modified version of our general framework that can be useful in these cases. Several applications of ITROX are studied and numerical examples (focusing on the construction of real-valued and binary sequences) are provided to illustrate the performance of ITROX for each application.

  • 286.
    Soltanalian, Mojtaba
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Designing unimodular codes via quadratic optimization2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 5, p. 1221-1234Article in journal (Refereed)
    Abstract [en]

    The NP-hard problem of optimizing a quadratic form over the unimodular vector set arises in radar code design scenarios as well as other active sensing and communication applications. To tackle this problem (which we call unimodular quadratic program (UQP)), several computational approaches are devised and studied. Power method-like iterations are introduced for local optimization of UQP. Furthermore, a monotonically error-bound improving technique (MERIT) is proposed to obtain the global optimum or a local optimum of UQP with good sub-optimality guarantees. The provided sub-optimality guarantees are case-dependent and may outperform the pi/4 approximation guarantee of semi-definite relaxation. Several numerical examples are presented to illustrate the performance of the proposed method. The examples show that for several cases, including rank-deficient matrices, the proposed methods can solve UQPs efficiently in the sense of sub-optimality guarantee and computational time.

  • 287.
    Soltanalian, Mojtaba
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Stoica, Petre
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    On Prime Root-of-Unity Sequences with Perfect Periodic Correlation2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 20, p. 5458-5470Article in journal (Refereed)
    Abstract [en]

    In this paper, Perfect Root-of-Unity Sequences (PRUS) with entries in $\alpha_p = \{ x \in \complexC ~ |~ x^p =1\}$ (where $p$ is a prime) are studied. A lower bound on the number of distinct phases that are used in PRUS over $\alpha_p$ is derived. We show that PRUS of length $L \geq p(p-1)$ must use all phases in $\alpha_p$. Certain conditions on the lengths of PRUS are derived. Showing that the phase values of PRUS must follow a given difference multiset property, we derive a set of equations (which we call the principal equations) that give possible lengths of a PRUS over $\alpha_p$ together with their phase distributions. The usefulness of the principal equations is discussed, and guidelines for efficient construction of PRUS are provided. Through numerical results, also contributions are made to the current state-of-knowledge regarding the existence of PRUS. In particular, a combination of the developed ideas allowed us to numerically settle the problem of existence of PRUS with $(L,p)=(28,7)$ within about two weeks--- a problem whose solution (without using the ideas in this paper) would likely take more than three million years on a standard PC.

  • 288. Somasundaram, Samuel D.
    et al.
    Jakobsson, Andreas
    Gudmundson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Robust Nuclear Quadrupole Resonance Signal Detection Allowing for Amplitude Uncertainties2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 3, p. 887-894Article in journal (Refereed)
    Abstract [en]

    Nuclear quadrupole resonance (NQR) is a solid-state radio frequency spectroscopic technique that can be used to detect compounds which contain quadrupolar nuclei, a requirement fulfilled by many high explosives and narcotics. Unfortunately, the low signal-to-noise ratio (SNR) of the observed signals currently inhibits the widespread use of the technique, thus highlighting the need for intelligent processing algorithms. In earlier work, we proposed a set of maximum likelihood-based algorithms enabling detection of even very weak NQR signals. These algorithms are based on derived realistic NQR data models, assuming that the (complex) amplitudes of the NQR signal components are known to within a multiplicative constant. However, these amplitudes, which are obtained from experimental measurements, are typically prone to some level of uncertainty. For such-cases, these algorithms will experience a loss in performance. Herein, we develop a set of robust algorithms, allowing for uncertainties in the assumed amplitudes, showing that these offer a significant performance gain over the current state-of-the art techniques.

  • 289.
    Somasundaram, Samuel D.
    et al.
    King's College London.
    Jakobsson, Andreas
    Karlstad University.
    Gudmundson, Erik
    Dept. of IT, Uppsala University.
    Robust Nuclear Quadrupole Resonance Signal Detection Allowing for Amplitude Uncertainties2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 3, p. 887-894Article in journal (Refereed)
  • 290. Song, K.
    et al.
    Ji, B.
    Huang, Y.
    Xiao, Ming
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Yang, L.
    Performance Analysis of Antenna Selection in Two-Way Relay Networks2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 10, p. 2520-2532, article id 7064785Article in journal (Refereed)
    Abstract [en]

    We investigate the performance of multi-antenna two-way relay networks, where both amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies are considered. First an antenna selection scheme among all nodes is proposed based on maximizing the worse received signal-to-noise ratio (SNR) of two end users. Then, we derive the probability density function (PDF) and cumulative distribution function (CDF) of the received SNRs of both users. We also obtain the closed-form expressions of average bit error rates (BER) and the outage probability of our system. Furthermore, we study the asymptotic behavior of our system when transmitting SNR or the number of antennas is large. The results show that the proposed antenna selection scheme achieves full diversity, and the simulation results closely match to our theoretical analysis. To further improve the spectrum efficiency of the system, a hybrid selection antenna scheme is proposed. Finally, the numerical results show that our scheme outperforms the state of art.

  • 291.
    Souryal, Michael R.
    et al.
    National Institute of Standards and Technology, Gaithersburg, USA.
    Larsson, Erik G.
    Royal Institute of Technology.
    Peric, Bojan
    The George Washington University, USA.
    Vojcic, Branimir R.
    The George Washington University, USA.
    Soft-Decision Metrics for Coded Orthogonal Signaling in Symmetric Alpha-Stable Noise2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 1, p. 266-273Article in journal (Refereed)
    Abstract [en]

    This paper derives new soft-decision metrics for coded orthogonal signaling in impulsive noise, more specifically symmetric-stable noise. For the case of a known channel amplitude and known noise dispersion, exact metrics are derived both for Cauchy and Gaussian noise. For the case that the channel amplitude or the dispersion is unknown, approximate metrics are obtained in closed-form based on a generalized-likelihood ratio approach. The performance of the new metrics is compared numerically for a turbo-coded system, and the sensitivity to side information of the optimum receiver for Cauchy noise is considered. The gain that can be achieved by using a properly chosen decoding metric is quantified, and it is shown that this gain is significant. The application of the results to frequency hopping ad hoc networks is also discussed.

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  • 292. Souryal, Michael R.
    et al.
    Larsson, Erik G.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Peric, Bojan
    Vojcic, Branimir R.
    Soft-decision metrics for coded orthogonal signaling in symmetric alpha-stable noise2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 1, p. 266-273Article in journal (Refereed)
    Abstract [en]

    This paper derives new soft-decision metrics for coded orthogonal signaling in impulsive noise, more specifically symmetric alpha-stable noise. For the case of a known channel amplitude and known noise dispersion, exact metrics are derived both for Cauchy and Gaussian noise. For the case that the channel amplitude or the dispersion is unknown, approximate metrics are obtained in closed-form based on a generalized-likelihood ratio approach. The performance of the new metrics is compared numerically for a turbo-coded system, and the sensitivity to side information of the optimum receiver for Cauchy noise is considered. The gain that can be achieved by using a properly chosen decoding metric-is quantified, and it is shown that this gain is significant. The application of the results to frequency hopping ad hoc networks is also discussed.

  • 293. Spano, D.
    et al.
    Alodeh, M.
    Chatzinotas, S.
    Ottersten, Björn
    Interdisciplinary Centre for Security, Reliability, and Trust, University of Luxembourg, Luxembourg City, 4365, Luxembourg.
    Symbol-Level Precoding for the Nonlinear Multiuser MISO Downlink Channel2018In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 5, p. 1331-1345, article id 8170315Article in journal (Refereed)
    Abstract [en]

    This paper investigates the problem of the interference among multiple simultaneous transmissions in the downlink channel of a multiantenna wireless system. A symbol-level precoding scheme is considered, in order to exploit the multiuser interference and transform it into useful power at the receiver side, through a joint utilization of the data information and the channel state information. In this context, this paper presents novel strategies that exploit the potential of symbol-level precoding to control the per-antenna instantaneous transmit power. In particular, the power peaks among the transmitting antennas and the instantaneous power imbalances across the different transmitted streams are minimized. These objectives are particularly relevant with respect to the nonlinear amplitude and phase distortions induced by the per-antenna amplifiers, which are important sources of performance degradation in practical systems. More specifically, this paper proposes two different symbol-level precoding approaches. The first approach performs a weighted per-antenna power minimization, under quality-of-service constraints and under a lower bound constraint on the per-antenna transmit power. The second strategy performs a minimization of the spatial peak-to-average power ratio, evaluated among the transmitting antennas. Numerical results are presented in a comparative fashion to show the effectiveness of the proposed techniques, which outperform the state-of-the-art symbol-level precoding schemes in terms of spatial peak-to-average power ratio, spatial dynamic range, and symbol error rate over nonlinear channels.

  • 294. Stankovic, Srdjan S.
    et al.
    Ilic, Nemanja
    Stankovic, Milos S.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Distributed Change Detection Based on a Consensus Algorithm2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 12, p. 5686-5697Article in journal (Refereed)
    Abstract [en]

    In this paper a novel distributed recursive algorithm is proposed for real time change detection using sensor networks. The algorithm is based on a combination of geometric moving average control charts generating local statistics and a global consensus strategy; it does not require any fusion center, so that the final decision is made by testing the state of any node in the network with respect to a given common threshold. The mean-square error with respect to the centralized solution defined by a weighted sum of the local statistics is analyzed in the case of constant asymmetric consensus matrices with constant and time varying forgetting factors in the underlying recursions, assuming spatially and temporally correlated data. These results are consistently extended to the case of time varying random consensus matrices, encompassing asymmetric gossip schemes, lossy networks and intermittent measurements, proving that the algorithm can be an efficient tool for practice. The given simulation results illustrate the main characteristics of the proposed algorithm, including the consensus matrix design, the mean square error with respect to the centralized solution as a function of the forgetting factor, the obtained detection quality expressed using deflection and estimation of the instant of parameter change.

  • 295.
    Stathakis, Efthymios
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Joakim, Jaldén
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rasmussen, Lars K.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Uniformly Improving Maximum-Likelihood SNR Estimation of Known Signals in Gaussian Channels2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 1, p. 156-167Article in journal (Refereed)
    Abstract [en]

    The signal-to-noise ratio (SNR) estimation problem is considered for an amplitude modulated known signal in Gaussian noise. The benchmark method is the maximum-likelihood estimator (MLE), whose merits are well-documented in the literature. In this work, an affinely modified version of the MLE (AMMLE) that uniformly outperforms, over all SNR values, the traditional MLE in terms of the mean-square error (MSE) is obtained in closed-form. However, construction of an AMMLE whose MSE is lower, at every SNR, than the unbiased Cramer-Rao bound (UCRB), is shown to be infeasible. In light of this result, the AMMLE construction rule is modified to provision for an a priori known set, where the SNR lies, and the MSE enhancement target is pursued within. The latter is realized through proper extension of an existing framework, due to Eldar, which settles the design problem by solving a semidefinite program. The analysis is further extended to the general case of vector signal models. Numerical results show that the proposed design demonstrates enhancement of the MSE for all the considered cases.

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  • 296.
    Statovci, D.
    et al.
    FTW, Österrike.
    Wolkerstorfer, M.
    FTW, Österrike.
    Nordström, Tomas
    FTW, Österrike.
    Robust Dynamic Spectrum Management for DMT-Based Systems2010In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 58, no 6, p. 3238-3250Article in journal (Refereed)
    Abstract [en]

    In recent years an increasing effort was made to reduce the energy consumption in digital subscriber line equipment. Dynamic spectrum management (DSM) has been identified as one promising method to achieve energy-efficiency in discrete multitone based systems. An open research question is how to ensure system robustness when applying highly optimized energy-efficient spectrum management. In this paper, we study the problem of uncertainty in crosstalk noise and parameters, the knowledge of which is indispensable for many DSM algorithms. We introduce robust optimization for spectrum balancing as a technique to achieve feasibility of the optimal power-allocation under a deterministic parameter uncertainty model. This can be seen as an extension of current schemes for spectrum balancing. As a special case we consider the simple strategy of scaling the crosstalk parameters to their worst-case values, which corresponds to a specific uncertainty model and entails no changes to current DSM algorithms. Finally, we quantify the benefit in worst-case performance and the price in terms of energy by simulations. © 2010 IEEE.

  • 297.
    Sternad, Mikael
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Lindbom, Lars
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Ahlén, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Wiener design of adaptation algorithms with time-invariant gains2002In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 50, p. 1895-1907Article in journal (Refereed)
  • 298.
    Stoica, Peter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Babu, Prabhu
    Indian Inst Technol, Ctr Appl Res Elect, Delhi 110016, India..
    Low-Rank Covariance Matrix Estimation for Factor Analysis in Anisotropic Noise: Application to Array Processing and Portfolio Selection2023In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 71, p. 1699-1711Article in journal (Refereed)
    Abstract [en]

    Factor analysis (FA) or principal component analysis (PCA) models the covariance matrix of the observed data as R = SST + S, where SST is the low-rank covariance matrix corresponding to the factors (aka latent variables) and S is the diagonal matrix of the noise. When the noise is anisotropic (aka nonuniform in the signal processing literature and heteroscedastic in the statistical literature), the diagonal elements of S cannot be assumed to be identical and they must be estimated jointly with the elements of SST. The problem of estimating SST and S in the above covariance model is the central theme of the present article. After stating this problem in a more formal way, we review the main existing algorithms for solving it. We then go on to show that these algorithms have reliability issues (such as lack of convergence or convergence to infeasible solutions) and therefore they may not be the best possible choice for practical applications. Next we explain how to modify one of these algorithms to improve its convergence properties and we also introduce a new method that we call FAAN (Factor Analysis for Anisotropic Noise). FAAN is a coordinate descent algorithm that iteratively maximizes the normal likelihood function, which is easy to implement in a numerically efficient manner and has excellent convergence properties as illustrated by the numerical examples presented in the article. Out of the many possible applications of FAAN we focus on the following two: direction-of-arrival (DOA) estimation using array signal processing techniques and portfolio selection for financial asset management.

  • 299.
    Stoica, Peter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Babu, Prabhu
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Maximum-likelihood nonparametric estimation of smooth spectra from irregularly sampled data2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 12, p. 5746-5758Article in journal (Refereed)
  • 300.
    Stoica, Peter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Babu, Prabhu
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    On the Proper Forms of BIC for Model Order Selection2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 9, p. 4956-4961Article in journal (Refereed)
    Abstract [en]

    The Bayesian Information Criterion (BIC) is often presented in a form that is only valid in large samples and under a certain condition on the rate at which the Fisher Information Matrix (FIM) increases with the sample length. This form has been improperly used previously in situations in which the conditions mentioned above do not hold. In this correspondence, we describe the proper forms of BIC in several practically relevant cases that do not satisfy the above assumptions. In particular, we present a new form of BIC for high signal-to-noise ratio (SNR) cases. The conclusion of this study is that BIC remains one of the most successful existing rules for model order selection, if properly used.

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