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  • 201.
    Mishra, Deepak
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Larsson, Erik G
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Optimal Channel Estimation for Reciprocity-Based Backscattering With a Full-Duplex MIMO Reader2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 6, p. 1662-1677Article in journal (Refereed)
    Abstract [en]

    Backscatter communication (BSC) technology can enable ubiquitous deployment of low-cost sustainable wireless devices. In this paper, we investigate the efficacy of a full-duplex multiple-input-multiple-output reader for enhancing the limited communication range of monostatic BSC systems. As this performance is strongly influenced by the channel estimation (CE) quality, we first derive a novel least-squares estimator for the forward and backward links between the reader and the tag, assuming that reciprocity holds and K orthogonal pilots are transmitted from the first K antennas of an N antenna reader. We also obtain the corresponding linear minimum-mean square-error estimate for the backscattered channel. After defining the transceiver design at the reader using these estimates, we jointly optimize the number of orthogonal pilots and energy allocation for the CE and information decoding phases to maximize the average backscattered signal-to-noise ratio (SNR) for efficiently decoding the tags messages. The unimodality of this SNR in optimization variables along with a tight analytical approximation for the jointly global optimal design is also discoursed. Lastly, the selected numerical results validate the proposed analysis, present key insights into the optimal resource utilization at reader, and quantify the achievable gains over the benchmark schemes.

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  • 202.
    Mochaourab, Rami
    et al.
    Dresden University of Technology, Germany.
    Jorswieck, E. A.
    Optimal Beamforming in Interference Networks with Perfect Local Channel Information2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 3, p. 1128-1141Article in journal (Refereed)
    Abstract [en]

    We consider settings in which T multi-antenna transmitters and K single-antenna receivers concurrently utilize the available communication resources. Each transmitter sends useful information only to its intended receivers and can degrade the performance of unintended systems. Here, we assume the performance measures associated with each receiver are monotonic with the received power gains. In general, the joint performance of the systems is desired to be Pareto optimal. However, designing Pareto optimal resource allocation schemes is known to be difficult. In order to reduce the complexity of achieving efficient operating points, we show that it is sufficient to consider rank-1 transmit covariance matrices and propose a framework for determining the efficient beamforming vectors. These beamforming vectors are thereby also parameterized by T(K-1)real-valued parameters each between zero and one. The framework is based on analyzing each transmitter's power gain-region which is composed of all jointly achievable power gains at the receivers. The efficient beamforming vectors are on a specific boundary section of the power gain-region, and in certain scenarios it is shown that it is necessary to perform additional power allocation on the beamforming vectors. Two examples which include broadcast and multicast data as well as a cognitive radio application scenario illustrate the results

  • 203.
    Mochaourab, Rami
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jorswieck, Eduard A.
    Coalitional Games in MISO Interference Channels: Epsilon-Core and Coalition Structure Stable Set2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 24, p. 6507-6520Article in journal (Refereed)
    Abstract [en]

    The multiple-input single-output interference channel is considered. Each transmitter is assumed to know the channels between itself and all receivers perfectly and the receivers are assumed to treat interference as additive noise. In this setting, noncooperative transmission does not take into account the interference generated at other receivers which generally leads to inefficient performance of the links. To improve this situation, we study cooperation between the links using coalitional games. The players ( links) in a coalition either perform zero forcing transmission or Wiener filter precoding to each other. The epsilon-core is a solution concept for coalitional games that takes into account the overhead required in coalition deviation. We provide necessary and sufficient conditions for the strong and weak epsilon-core of our coalitional game not to be empty with zero forcing transmission. Since, the epsilon-core only considers the possibility of joint cooperation of all links, we study coalitional games in partition form in which several distinct coalitions can form. We propose a polynomial-time distributed coalition formation algorithm based on coalition merging and prove that its solution lies in the coalition structure stable set of our coalition formation game. Simulation results reveal the cooperation gains for different coalition formation complexities and deviation overhead models.

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  • 204.
    Mohammadiha, Nasser
    et al.
    University of Oldenburg, Germany.
    Smaragdis, Paris
    University of Illinois .
    Panahandeh, Ghazaleh
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Doclo, Simon
    University of Oldenburg, Germany.
    A state-space approach to dynamic nonnegative matrix factorization2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 4, p. 949-959Article in journal (Refereed)
    Abstract [en]

    Nonnegative matrix factorization (NMF) has been actively investigated and used in a wide range of problems in the past decade. A significant amount of attention has been given to develop NMF algorithms that are suitable to model time series with strong temporal dependencies. In this paper, we propose a novel state-space approach to perform dynamic NMF (D-NMF). In the proposed probabilistic framework, the NMF coefficients act as the state variables and their dynamics are modeled using a multi-lag nonnegative vector autoregressive (N-VAR) model within the process equation. We use expectation maximization and propose a maximum-likelihood estimation framework to estimate the basis matrix and the N-VAR model parameters. Interestingly, the N-VAR model parameters are obtained by simply applying NMF. Moreover, we derive a maximum a posteriori estimate of the state variables (i.e., the NMF coefficients) that is based on a prediction step and an update step, similarly to the Kalman filter. We illustrate the benefits of the proposed approach using different numerical simulations where D-NMF significantly outperforms its static counterpart. Experimental results for three different applications show that the proposed approach outperforms two state-of-the-art NMF approaches that exploit temporal dependencies, namely a nonnegative hidden Markov model and a frame stacking approach, while it requires less memory and computational power.

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  • 205.
    Molavipour, Sina
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Bassi, Germán
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Neural Estimators for Conditional Mutual Information Using Nearest Neighbors Sampling2021In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 69, p. 766-780Article in journal (Refereed)
    Abstract [en]

    The estimation of mutual information (MI) or conditional mutual information (CMI) from a set of samples is a long-standing problem. A recent line of work in this area has leveraged the approximation power of artificial neural networks and has shown improvements over conventional methods. One important challenge in this new approach is the need to obtain, given the original dataset, a different set where the samples are distributed according to a specific product density function. This is particularly challenging when estimating CMI. In this paper, we introduce a new technique, based on k nearest neighbors (k-NN), to perform the resampling and derive high-confidence concentration bounds for the sample average. Then the technique is employed to train a neural network classifier and the CMI is estimated accordingly. We propose three estimators using this technique and prove their consistency, make a comparison between them and similar approaches in the literature, and experimentally show improvements in estimating the CMI in terms of accuracy and variance of the estimators.

  • 206.
    Moradi, Ashkan
    et al.
    Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway.
    Venkategowda, Naveen
    Linköping University, Department of Science and Technology, Physics, Electronics and Mathematics. Linköping University, Faculty of Science & Engineering.
    Talebi, Sayed Pouria
    Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway.
    Werner, Stefan
    Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway.
    Privacy-Preserving Distributed Kalman Filtering2022In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, ISSN 1053-587X, Vol. 70, p. 3074-3089Article in journal (Refereed)
    Abstract [en]

    Distributed Kalman filtering techniques enable agents of a multiagent network to enhance their ability to track a system and learn from local cooperation with neighbors. Enabling this cooperation, however, requires agents to share information, which raises the question of privacy. This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) that protects local agent information by restricting and obfuscating the information exchanged. The derived PP-DKF embeds two state-of-the-art average consensus techniques that guarantee agent privacy. The resulting PP-DKF utilizes noise injection-based and decomposition-based privacy-preserving techniques to implement a robust distributed Kalman filtering solution against perturbation. We characterize the performance and convergence of the proposed PP-DKF and demonstrate its robustness against the injected noise variance. We also assess the privacy-preserving properties of the proposed algorithm for two types of adversaries, namely, an external eavesdropper and an honest-but-curious (HBC) agent, by providing bounds on the privacy leakage for both adversaries. Finally, several simulation examples illustrate that the proposed PP-DKF achieves better performance and higher privacy levels than the distributed Kalman filtering solutions employing contemporary privacy-preserving techniques.

  • 207.
    Mossberg, M
    et al.
    Dept. of Electr. Eng., Karlstad Univ., Sweden.
    Larsson, Erik K.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Mossberg, E
    Karlstad Univ, Dept Math, SE-65188 Karlstad, Sweden.
    Fast estimators for large-scale fading channels from irregularly sampled data2006In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 7, p. 2803-2808Article in journal (Refereed)
    Abstract [en]

    The problem of estimating the power attenuation dynamics for large-scale lognormal fading channels in wireless communication systems, when the model is described as a mean reverting Ornstein-Uhlenbeck process, is studied in the paper. Fast and accurate estimators for the model parameters from irregularly sampled data are suggested for both offline and online applications. The Cramer-Rao bound for the estimation of the model parameters is derived, and the qualities of the proposed estimators are evaluated with respect to the bound.

  • 208.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Analysis of moments based methods for fractional Gaussian noise estimation2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 7, p. 3823-3827Article in journal (Refereed)
    Abstract [en]

    Fractional Gaussian noise, given as the increment of fractional Brownian motion, is a stationary Gaussian process characterized by the Hurst parameter. In the paper, moments based estimators of the Hurst parameter are presented and analyzed with respect to asymptotic variance

  • 209.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Estimation of continuous-time stochastic signals from sample covariances2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 2, p. 821-825Article in journal (Refereed)
    Abstract [en]

    The problem of estimating the parameters in stochastic continuous-time signals, represented as continuous-time autoregressive moving average (ARMA) processes, from discrete-time data is considered. The proposed solution is to fit the covariance function of the process, parameterized by the unknown parameters, to sample covariances. It is shown that the method is consistent, and an expression for the approximate covariance matrix of the estimated parameter vector is derived. The derived variances are compared with empirical variances from a Monte Carlo simulation, and with the Cramer-Rao bound. It turns out that the variances are close to the Cramer-Rao bound for certain choices of the sampling interval and the number of covariance elements used in the criterion function.

  • 210.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    High-accuracy instrumental variable identification of continuous-time autoregressive parameters from irregularly sampled noisy data2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 8, p. 4087-4091Article in journal (Refereed)
    Abstract [en]

    A computationally efficient estimator of continuous-time autoregressive (AR) process parameters from irregularly sampled data affected by discrete-time white measurement noise is presented. It is described how an instrumental variable approach can be used for estimating the AR process parameters with high accuracy. Possible estimators of the incremental variance of the driving continuous-time white noise source and of the variance of the discrete-time white measurement noise are also discussed.

  • 211.
    Mossberg, Magnus
    et al.
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Larsson, Erik K.
    Mossberg, Eva
    Karlstad University, Faculty of Technology and Science, Department of Mathematics.
    Fast estimators for large-scale fading channels from irregularly sampled data2006In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 7, p. 2803-2808Article in journal (Refereed)
  • 212. Mueller, Axel
    et al.
    Couillet, Romain
    Björnson, Emil
    KTH, School of Electrical Engineering (EES), Signal Processing. Linköping University, Sweden.
    Wagner, Sebastian
    Debbah, Merouane
    Interference-Aware RZF Precoding for Multicell Downlink Systems2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 15, p. 3959-3973, article id 7086330Article in journal (Refereed)
    Abstract [en]

    Recently, a structure of an optimal linear precoder for multi cell downlink systems has been described, and many other references have used simplified versions of this precoder to obtain promising performance gains. These gains have been hypothesized to stem from the additional degrees of freedom that allow for interference mitigation through interference relegation to orthogonal subspaces. However, no conclusive or rigorous understanding has yet been developed. In this paper, we build on an intuitive interference induction trade-off and the aforementioned preceding structure to propose an interference aware RZF (iaRZF) preceding scheme for multi cell downlink systems, and we analyze its rate performance. Special emphasis is placed on the induced interference mitigation mechanism of iaRZF. For example, we will verify the intuitive expectation that the precoder structure can either completely remove induced inter-cell or intra-cell interference. We state new results from large-scale random matrix theory that make it possible to give more intuitive and insightful explanations of the precoder behavior, also for cases involving imperfect channel state information (CSI). We remark especially that the interference-aware precoder makes use of all available information about interfering channels to improve performance. Even very poor CSI allows for significant sum-rate gains. Our obtained insights are then used to propose heuristic precoder parameters for arbitrary systems, whose effectiveness are shown in more involved system scenarios. Furthermore, calculation and implementation of these parameters does not require explicit inter base station cooperation.

  • 213.
    Müller, Axel
    et al.
    CentraleSupelec, France.
    Couillet, Romain
    CentraleSupelec, France.
    Björnson, Emil
    KTH Royal Institute Technology, Sweden; Supelec, France.
    Wagner, Sebastian
    Universität Dresden, Germany.
    Debbah, Merouane
    CentraleSupelec, France.
    Interference-Aware RZF Precoding for Multi-Cell Downlink Systems2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 15, p. 3959-3973Article in journal (Refereed)
    Abstract [en]

    Recently, a structure of an optimal linear precoder for multi cell downlink systems has been described, and many other references have used simplified versions of this precoder to obtain promising performance gains. These gains have been hypothesized to stem from the additional degrees of freedom that allow for interference mitigation through interference relegation to orthogonal subspaces. However, no conclusive or rigorous understanding has yet been developed. In this paper, we build on an intuitive interference induction trade-off and the aforementioned preceding structure to propose an interference aware RZF (iaRZF) precoding scheme for multi celldownlink systems, and we analyze its rate performance. Special emphasis is placed on the induced interference mitigation mechanism of iaRZF. For example, we will verify the intuitive expectation that the precoder structure can either completely remove induced inter-cell or intra-cell interference. We state new results from large-scale random matrix theory that make it possible to give more intuitive and insightful explanations of the precoder behavior, also for cases involving imperfect channel state information (CSI). We remark especially that the interference-aware precoder makes use of all available information about interfering channels to improve performance. Even very poor CSI allows for significant sum-rate gains. Our obtained insights are then used to propose heuristic precoder parameters for arbitrary systems, whose effectiveness are shown in more involved system scenarios. Furthermore, calculation and implementation of these parameters does not require explicit inter base station cooperation.

  • 214.
    Naesseth, Christian A.
    et al.
    Linkoping Univ, Div Stat & Machine Learning, S-58183 Linkoping, Sweden.
    Lindsten, Fredrik
    Linkoping Univ, Div Stat & Machine Learning, S-58183 Linkoping, Sweden.
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    High-Dimensional Filtering Using Nested Sequential Monte Carlo2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 16, p. 4177-4188Article in journal (Refereed)
    Abstract [en]

    Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without a good proposal distribution can perform poorly, in particular in high dimensions. We propose nested sequential Monte Carlo, a methodology that generalizes the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correctSMCalgorithm. This way, we can compute an "exact approximation" of, e. g., the locally optimal proposal, and extend the class of models forwhichwe can perform efficient inference using SMC. We showimproved accuracy over other state-of-the-art methods on several spatio-temporal state-space models.

  • 215.
    Naghsh, Mohammad Mahdi
    et al.
    Isfahan Univ Technol, Dept Elect & Comp Engn, Esfahan 8415683111, Iran.
    Masjedi, Maryatn
    Isfahan Univ Technol, Dept Elect & Comp Engn, Esfahan 8415683111, Iran.
    Adibi, Annan
    Isfahan Univ Technol, Dept Elect & Comp Engn, Esfahan 8415683111, Iran.
    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.
    Max-Min Fairness Design for MIMO Interference Channels: A Minorization-Maximization Approach2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 18, p. 4707-4719Article in journal (Refereed)
    Abstract [en]

    We address the problem of linear precoder (beam-former) design in a multiple-input multiple-output interference channel. The aim is to design the transmit covariance matrices in order to achieve max-min utility fairness for all users. The corresponding optimization problem is non-convex and NP-hard in general. We devise an efficient algorithm based on the minorization-maximization technique to obtain quality solutions to this problem. The proposed method solves a second-order cone convex program at each iteration. We prove that the devised method converges to stationary points of the problem. We also extend our algorithm to the case where there are uncertainties in the noise covariance matrices or channel state information. Simulation results show the effectiveness of the proposed method compared with its main competitor.

  • 216.
    Naghsh, Mohammad Mahdi
    et al.
    Isfahan University of Technology.
    Modarres-Hashemi, Mahmoud
    Isfahan University of Technology.
    ShahbazPanahi, Shahram
    University of Ontario Institute of Technology.
    Soltanalian, Mojtaba
    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.
    Unified Optimization Framework for Multi-Static Radar Code Design using Information-Theoretic Criteria2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 21, p. 5401-5416Article in journal (Refereed)
    Abstract [en]

     In this paper, we study the problem of code design to improve the detection performance of multi-static radar in the presence of clutter (i.e., a signal-dependent interference). To this end, we briefly present a discrete-time formulation of the problem as well as the optimal detector in the presence of Gaussian clutter. Due to the lack of analytical expression for receiver operation characteristic (ROC), code design based on ROC is not feasible. Therefore, we consider several popular information-theoretic criteria including Bhattacharyya distance, Kullback-Leibler (KL) divergence, J-divergence, andmutual information (MI) as design metrics. The codeoptimization problems associated with different information-theoretic criteria are obtained and cast under a unified framework. We propose two general methods based on Majorization-Minimization to tackle the optimization problems in the framework. The first method provides optimal solutions via successive majorizations whereas the second one consists of a majorization step, a relaxation, and a synthesis stage. Moreover, derivations of the proposed methods are extended to tackle the code design problems with a peak-to-average ratio power (PAR) constraint. Usingnumerical investigations, a general analysis of the coded system performance, computational efficiency of the proposed methods, and the behavior of the information-theoretic criteria is provided.

  • 217. Naghsh, Mohammad Mahdi
    et al.
    Soltanalian, Mojtaba
    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.
    Modarres-Hashemi, Mahmoud
    De Maio, Antonio
    Aubry, Augusto
    A Doppler robust design of transmit sequence and receive filter in the presence of signal-dependent interference2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 4, p. 772-785Article in journal (Refereed)
    Abstract [en]

    In this paper, we study the joint design of Doppler robust transmit sequence and receive filter to improve the performance of an active sensing system dealing with signal-dependent interference. The signal-to-noise-plus-interference (SINR) of the filter output is considered as the performance measure of the system. The design problem is cast as a max-min optimization problem to robustify the system SINR with respect to the unknown Doppler shifts of the targets. To tackle the design problem, which belongs to a class of NP-hard problems, we devise a novel method (which we call DESIDE) to obtain optimized pairs of transmit sequence and receive filter sharing the desired robustness property. The proposed method is based on a cyclic maximization of SINR expressions with relaxed rank-one constraints, and is followed by a novel synthesis stage. We devise synthesis algorithms to obtain high quality pairs of transmit sequence and receive filter that well approximate the behavior of the optimal SINR (of the relaxed problem) with respect to target Doppler shift. Several numerical examples are provided to analyze the performance obtained by DESIDE.

  • 218. Nasir, A. A.
    et al.
    Mehrpouyan, H.
    Durrani, S.
    Blostein, S. D.
    Kennedy, R. A.
    Ottersten, Björn
    Department of Interdisciplinary, Center for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg.
    Transceiver Design for Distributed STBC Based AF Cooperative Networks in the Presence of Timing and Frequency Offsets2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 12, p. 3143-3158Article in journal (Refereed)
    Abstract [en]

    In multi-relay cooperative systems, the signal at the destination is affected by impairments such as multiple channel gains, multiple timing offsets (MTOs), and multiple carrier frequency offsets (MCFOs). In this paper we account for all these impairments and propose a new transceiver structure at the relays and a novel receiver design at the destination in distributed space-time block code (DSTBC) based amplify-and-forward (AF) cooperative networks. The Cramer-Rao lower bounds and a least squares (LS) estimator for the multi-parameter estimation problem are derived. In order to significantly reduce the receiver complexity at the destination, a differential evolution (DE) based estimation algorithm is applied and the initialization and constraints for the convergence of the proposed DE algorithm are investigated. In order to detect the signal from multiple relays in the presence of unknown channels, MTOs, and MCFOs, novel optimal and sub-optimal minimum mean-square error receiver designs at the destination node are proposed. Simulation results show that the proposed estimation and compensation methods achieve full diversity gain in the presence of channel and synchronization impairments in multi-relay AF cooperative networks.

  • 219.
    Niu, Steve S.
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Björk, Åke
    Linköping University, Department of Mathematics, Scientific Computing. Linköping University, The Institute of Technology.
    Decomposition Methods for Solving Least-Squares Parameter Estimation1996In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 44, no 11, p. 2847-2852Article in journal (Refereed)
    Abstract [en]

    A multiple model least-squares method based on matrix decomposition is proposed. Compared with the conventional implementation of the least-squares method, the proposed method is simpler and more flexible in implementation and produces more information. An application example in parameter estimation is included. As a basic numerical tool, the proposed method can be used in many different application areas.

  • 220.
    Nurminen, Henri
    et al.
    Tampere Univ Technol, Finland; HERE Technol, Finland.
    Ardeshiri, Tohid
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Univ Cambridge, England.
    Piche, Robert
    Tampere Univ Technol, Finland.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Skew-t Filter and Smoother With Improved Covariance Matrix Approximation2018In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 21, p. 5618-5633Article in journal (Refereed)
    Abstract [en]

    Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t-distributed measurement noise are proposed. The algorithms use a variational Bayes based posterior approximation with coupled location and skewness variables to reduce the error caused by the variational approximation. Although the variational update is done suboptimally using an expectation propagation algorithm, our simulations show that the proposed method gives a more accurate approximation of the posterior covariance matrix than an earlier proposed variational algorithm. Consequently, the novel filter and smoother outperform the earlier proposed robust filter and smoother and other existing low-complexity alternatives in accuracy and speed. We present both simulations and tests based on real-world navigation data, in particular the global positioning system data in an urban area, to demonstrate the performance of the novel methods. Moreover, the extension of the proposed algorithms to cover the case where the distribution of the measurement noise is multivariate skew-t is outlined. Finally, this paper presents a study of theoretical performance bounds for the proposed algorithms.

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  • 221.
    Oechtering, Tobias J.
    et al.
    Fraunhofer German-Sino Lab for Mobile Communications, Berlin, Germany.
    Wyrembelski, Rafael F.
    Boche, Holger
    Multiantenna Bidirectional Broadcast Channels-Optimal Transmit Strategies2009In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 57, no 5, p. 1948-1958Article in journal (Refereed)
    Abstract [en]

    We consider a three-node network where a relay node establishes a bidirectional communication between the two other nodes using a spectrally efficient decode-and-forward protocol. In the first phase we have the classical multiple-access channel where both nodes transmit a message to the relay node, which then decodes the messages. In the second phase the relay broadcasts a re-encoded composition of them based on the network coding idea. This means that each receiving node uses the same data stream to infer on its intended message. We characterize the optimal transmit strategy for the broadcast phase where either the relay node or the two other nodes are equipped with multiple antennas. Our main result shows that beamforming into the subspace spanned by the channels is always an optimal transmit strategy for the multiple-input single-output bidirectional broadcast channel. Thereby, it shows that correlation between the channels is advantageous. Moreover, this leads to a parametrization of the optimal transmit strategy which specifies the whole capacity region. In retrospect the results are intuitively clear since the single-beam transmit strategy reflects the single stream processing due to the network coding approach.

  • 222.
    Ohlsson, Henrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Univ Calif Berkeley, CA 94720 USA .
    Eldar, Yonina C.
    Technion Israel Institute Technology, Israel .
    Yang, Allen Y.
    University of Calif Berkeley, CA 94720 USA .
    Shankar Sastry, S.
    University of Calif Berkeley, CA 94720 USA .
    Compressive Shift Retrieval2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, p. 4105-4113Article in journal (Refereed)
    Abstract [en]

    The classical shift retrieval problem considers two signals in vector form that are related by a shift. This problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals. Inspired by compressive sensing, in this paper, we seek to estimate the shift directly from compressed signals. We show that under certain conditions, the shift can be recovered using fewer samples and less computation compared to the classical setup. We also illustrate the concept of superresolution for shift retrieval. Of particular interest is shift estimation from Fourier coefficients. We show that under rather mild conditions only one Fourier coefficient suffices to recover the true shift.

  • 223.
    Olfat, Ehsan
    et al.
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Joint Channel and Clipping Level Estimation for OFDM in IoT-based Networks2017In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 18, p. 4902-4911Article in journal (Refereed)
    Abstract [en]

    We consider scenarios such as IoT-based 5G or IoTbased machine type communication, where a low-cost low-power transmitter communicates with a high-quality receiver. Then, digital predistortion of the nonlinear power amplifier may be too expensive. In order to investigate the feasibility of receiver-side compensation of the transmitter RF impairments, we study joint maximum-likelihood estimation of channel and clipping level in multipath fading OFDM systems. In particular, we propose an alternative optimization algorithm, which uses frequency-domain block-type training symbols, and prove that this algorithm always converges, at least to a local optimum point. Then, we calculate the Cramer-Rao lower bound, and show that the proposed estimator attains it for high signal-to-noise ratios. Finally, we perform numerical evaluations to illustrate the performance of the estimator, and show that iterative decoding can be done using the estimated channel and clipping level with almost the same performance as a genie-aided scenario, where the channel and clipping level are perfectly known.

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  • 224.
    Olofsson, Tomas
    et al.
    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.
    Gidlund, M
    Modeling of the fading statistics of wireless sensor network channels in industrial environments2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 12, p. 3021-3034Article in journal (Refereed)
    Abstract [en]

    This paper presents an investigation of how to model the statistical properties of radio channels arising in industrial environments over long time horizons, e.g., hours and days. Based on extensive measurement campaigns, conducted at three different factory buildings, it is shown that for mobile transceivers the fading characteristics are Rayleigh or close to Rayleigh. However, for transceivers mounted at fixed locations, the use of conventional single fading distributions is not sufficient. It is shown that a suitable model structure for describing the fading properties of the radio channels, as measured by power, is a mixture of gamma and compound gamma-lognormal distributions. Furthermore, the complexity of the model generally increases with the observation interval. A model selection approach based on a connection between Kullback's mean discrimination information and the log-likelihood provides a robust choice of model structure. We show that while a (semi)-Markov chain constitute a suitable model for the channel dynamics the time dependence of the data can be neglected in the estimation of the parameters of the mixture distributions. Neglecting the time dependence in the data leads to a more efficient parametrization. Moreover, it is shown that the considered class of mixture distributions is identifiable for both continuous and quantized data under certain conditions and under those conditions a maximum likelihood under independence assumption estimator is shown to give consistent parameters also for data which are not independent. The parameter estimates are obtained by maximizing the log likelihood using a genetic and a local interior point algorithm.

  • 225.
    Olofsson, Tomas
    et al.
    Division of Signals and Systems, Department of Engineering Sciences, Uppsala University, Uppsala, Sweden .
    Ahlén, Anders
    Division of Signals and Systems, Department of Engineering Sciences, Uppsala University, Uppsala, Sweden .
    Gidlund, Mikael
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems.
    Modeling of the Fading Statistics of Wireless Sensor Network Channels in Industrial Environments2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 12, p. 3021-3034Article in journal (Refereed)
    Abstract [en]

    This paper presents an investigation of how to model the statistical properties of radio channels arising in industrial environments over long time horizons, e.g., hours and days. Based on extensive measurement campaigns, conducted at three different factory buildings, it is shown that for mobile transceivers the fading characteristics are Rayleigh or close to Rayleigh. However, for transceivers mounted at fixed locations, the use of conventional single fading distributions is not sufficient. It is shown that a suitable model structure for describing the fading properties of the radio channels, as measured by power, is a mixture of gamma and compound gamma-lognormal distributions. Furthermore, the complexity of the model generally increases with the observation interval. A model selection approach based on a connection between Kullback's mean discrimination information and the log-likelihood provides a robust choice of model structure. We show that while a (semi)-Markov chain constitute a suitable model for the channel dynamics the time dependence of the data can be neglected in the estimation of the parameters of the mixture distributions. Neglecting the time dependence in the data leads to a more efficient parametrization. Moreover, it is shown that the considered class of mixture distributions is identifiable for both continuous and quantized data under certain conditions and under those conditions a maximum likelihood under independence assumption estimator is shown to give consistent parameters also for data which are not independent. The parameter estimates are obtained by maximizing the log likelihood using a genetic and a local interior point algorithm.

  • 226. Olsson, Jimmy
    et al.
    Rydén, Tobias
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Rao-Blackwellization of Particle Markov Chain Monte Carlo Methods Using Forward Filtering Backward Sampling2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 10, p. 4606-4619Article in journal (Refereed)
    Abstract [en]

    Smoothing in state-space models amounts to computing the conditional distribution of the latent state trajectory, given observations, or expectations of functionals of the state trajectory with respect to this distribution. In recent years there has been an increased interest in Monte Carlo-based methods, often involving particle filters, for approximate smoothing in nonlinear and/or non-Gaussian state-space models. One such method is to approximate filter distributions using a particle filter and then to simulate, using backward kernels, a state trajectory backwards on the set of particles. We show that by simulating multiple realizations of the particle filter and adding a Metropolis-Hastings step, one obtains a Markov chain Monte Carlo scheme whose stationary distribution is the exact smoothing distribution. This procedure expands upon a similar one recently proposed by Andrieu, Doucet, Holenstein, and Whiteley. We also show that simulating multiple trajectories from each realization of the particle filter can be beneficial from a perspective of variance versus computation time, and illustrate this idea using two examples.

  • 227.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Variational Measurement Update for Extended Target Tracking With Random Matrices2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 7, p. 3827-3834Article in journal (Refereed)
    Abstract [en]

    This correspondence proposes a new measurement update for extended target tracking under measurement noise when the target extent is modeled by random matrices. Compared to the previous measurement update developed by Feldmann et al., this work follows a more rigorous path to derive an approximate measurement update using the analytical techniques of variational Bayesian inference. The resulting measurement update, though computationally more expensive, is shown via simulations to be better than the earlier method in terms of both the state estimates and the predictive likelihood for moderate amounts of prediction errors.

  • 228.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Demirekler, Mübeccel
    Middle East Technical University, Turkey.
    Maximum Likelihood Estimation of Transition Probabilities of Jump Markov Linear Systems2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 10 II, p. 5093-5108Article in journal (Refereed)
    Abstract [en]

    This paper describes an online maximum likelihood estimator for the transition probabilities associated with a jump Markov linear system (JMLS). The maximum likelihood estimator is derived using the reference probability method, which exploits an hypothetical probability measure to find recursions for complex expectations. Expectation maximization (EM) procedure is utilized for maximizing the likelihood function. In order to avoid the exponential increase in the number of statistics of the optimal EM algorithm, we make interacting multiple model (IMM)-type approximations. The resulting method needs the mode weights of an IMM filter with N3 components, where N is the number of models in the JMLS. The algorithm can also supply base-state estimates and covariances as a by-product. The performance of the estimator is illustrated on two simulated examples and compared to a recently proposed alternative.

  • 229.
    Orguner, Umut
    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.
    Risk-Sensitive Particle Filters for Mitigating Sample Impoverishment2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 10 II, p. 5001-5012Article in journal (Refereed)
    Abstract [en]

    Risk-sensitive filters (RSF) put a penalty to higher-order moments of the estimation error compared to conventional filters as the Kalman filter minimizing the mean square error (MSE). The result is a more cautious filter, which can be interpreted as an implicit and automatic way to increase the state noise covariance. On the other hand, the process of jittering, or roughening, is well known in particle filters to mitigate sample impoverishment. The purpose of this contribution is to introduce risk-sensitive particle filters (RSPF) as an alternative approach to mitigate sample impoverishment based on constructing explicit risk functions from a general class of factorizable functions. It is first shown that RSF can be done in nonlinear systems using a recursion of an infinite dimensional information state which involves general risk functions. Then, this information state calculation is carried out using particle approximations. Some alternative approaches, generalizations, specific cases, comparison to existing methods of sample impoverishment mitigation and issues related to the selection of risk functions and parameters are examined. Performance of the resulting filter using various risk functions is illustrated on a simulated scenario and compared with the roughening method.

  • 230.
    Orguner, Umut
    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.
    Target Tracking With Particle Filters Under Signal Propagation Delays2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 6, p. 2485-2495Article in journal (Refereed)
    Abstract [en]

    Signal propagation delays are hardly a problem for target tracking with standard sensors such as radar and vision due to the fact that the speed of light is much higher than the speed of the target. This contribution studies the case where the ratio of the target and the propagation speed is not negligible, as in the case of sensor networks with microphones, geophones or sonars for instance, where the signal speed in air, ground and water causes a state dependent and stochastic delay of the observations. The proposed approach utilizes an augmentation of the state vector with the propagation delay in a particle filtering framework to compensate for the negative effects of the delays. The model of the physics rules governing the propagation delays is used in interaction with the target motion model to yield an iterative prediction update step in the particle filter which is called the propagation delayed measurement particle filter (PDM-PF). The performance of PDM-PF is illustrated in a challenging target tracking scenario by making comparisons to alternative particle filters that can be used in similar cases.

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  • 231.
    Ottersten, Björn
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    VIBERG, M
    KAILATH, T
    ANALYSIS OF SUBSPACE FITTING AND ML TECHNIQUES FOR PARAMETER-ESTIMATION FROM SENSOR ARRAY DATA1992In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 40, no 3, p. 590-600Article in journal (Refereed)
    Abstract [en]

    Signal parameter estimation from sensor array data is a problem that is encountered in many engineering applications. Under the assumption of Gaussian distributed emitter signals, the so-called stochastic maximum likelihood (ML) technique is known to be statistically efficient, i.e., the estimation error covariance attains the Cramer-Rao bound (CRB) asymptotically. Herein, it is shown that also the multi-dimensional signal subspace method, termed weighted subspace fitting (WSF), is asymptotically efficient. This also results in a novel, compact matrix expression for the CRB on the estimation error variance. The asymptotic analysis of the ML and WSF methods is extended to deterministic emitter signals. The asymptotic properties of the estimates for this case are shown to be identical to the Gaussian emitter signal case, i.e., independent of the actual signal waveforms. Conclusions, concerning the modeling aspect of the sensor array problem are drawn.

  • 232.
    Ottersten, Björn
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    VIBERG, M
    KAILATH, T
    PERFORMANCE ANALYSIS OF THE TOTAL LEAST-SQUARES ESPRIT ALGORITHM1991In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 39, no 5, p. 1122-1135Article in journal (Refereed)
    Abstract [en]

    Estimation of signal parameters via rotational invariance techniques (ESPRIT) is a recently developed algorithm for high resolution signal parameter estimation. This method provides estimates of the signal parameters based only on eigendecompositions and no search over the parameter space is necessary. In this paper, the asymptotic distribution of the estimation error for the total least squares (TLS) version of the algorithm is derived. The application to a uniform linear array is treated in some detail, and a generalization of ESPRIT to include row weighting is discussed. The Cramer-Rao bound (CRB) for the ESPRIT problem formulation is derived and found to coincide with the asymptotic variance of the TLS ESPRIT estimates through numerical examples. A comparison of this method to least squares ESPRIT, MUSIC, and Root-MUSIC as well as to the CRB for a calibrated array is also presented. TLS ESPRIT is found to be competitive with the other methods, and the performance is close to the calibrated CRB for many cases of practical interest. For highly correlated signals, however, the performance deviates significantly from the calibrated CRB. Simulations are included to illustrate the applicability of the theoretical results for finite number of data.

  • 233.
    Ottersten, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Viberg, Mats
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Kailath, Thomas
    Stanford University, USA.
    Analysis of Subspace Fitting and ML Techniques for Parameter Estimation from Sensor Array Data1992In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 40, no 3, p. 590-600Article in journal (Refereed)
    Abstract [en]

    It is shown that the multidimensional signal subspace method, termed weighted subspace fitting (WSF), is asymptotically efficient. This results in a novel, compact matrix expression for the Cramer-Rao bound (CRB) on the estimation error variance. The asymptotic analysis of the maximum likelihood (ML) and WSF methods is extended to deterministic emitter signals. The asymptotic properties of the estimates for this case are shown to be identical to the Gaussian emitter signal case, i.e. independent of the actual signal waveforms. Conclusions concerning the modeling aspect of the sensor array problem are drawn.

  • 234.
    Ottersten, Björn
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Viberg, Mats
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Kailath, Tomas
    Stanford University, USA.
    Performance Analysis of the Total Least Squares ESPRIT Algorithm1991In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 39, no 5, p. 1122-1135Article in journal (Refereed)
    Abstract [en]

    The asymptotic distribution of the estimation error for the total least squares (TLS) version of ESPRIT is derived. The application to a uniform linear array is treated in some detail, and a generalization of ESPRIT to include row weighting is discussed. The Cramer-Rao bound (CRB) for the ESPRIT problem formulation is derived and found to coincide with the asymptotic variance of the TLS ESPRIT estimates through numerical examples. A comparison of this method to least squares ESPRIT, MUSIC, and Root-MUSIC as well as to the CRB for a calibrated array is also presented. TLS ESPRIT is found to be competitive with the other methods, and the performance is close to the calibrated CRB for many cases of practical interest. For highly correlated signals, however, the performance deviates significantly from the calibrated CRB. Simulations are included to illustrate the applicability of the theoretical results to a finite number of data.

  • 235.
    Ovacikli, Kubilay
    et al.
    Rubico Vibration Analysis AB.
    Pääjärvi, Patrik
    Rubico Vibration Analysis AB.
    Leblanc, James
    Swedish Rifle AB.
    Carlson, Johan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Recovering Periodic Impulsive Signals Through Skewness Maximization2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 6, p. 1586-1596Article in journal (Refereed)
    Abstract [en]

    Maximizing the skewness of a measured signal by adaptive filtering to reveal hidden periodic impulses is proposed as a pre-processing method. Periodic impulsive signals are modelled by harmonically related sinusoids to prove that amplitude and phase distortion from a transfer function, effects of sinusoidal interferences and noise can be compensated for by a linear filter. The convergence behaviour of the skewness maximization algorithm is analysed to show that it is possible to recover the original harmonic structure with an unknown fundamental frequency by achieving maximum skewness in the given signal. It is shown that maximizing the skewness always results in a sub-space containing only a single harmonic family. Defect detection in rolling element bearings is presented as an application example and as a comparative study against kurtosis maximization.

  • 236.
    Owrang, Arash
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    A Model Selection Criterion for High-Dimensional Linear Regression2018In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 13, p. 3436-3446Article in journal (Refereed)
    Abstract [en]

    Statistical model selection is a great challenge when the number of accessible measurements is much smaller than the dimension of the parameter space. We study the problem of model selection in the context of subset selection for high-dimensional linear regressions. Accordingly, we propose a new model selection criterion with the Fisher information that leads to the selection of a parsimonious model from all the combinatorial models up to some maximum level of sparsity. We analyze the performance of our criterion as the number of measurements grows to infinity, as well as when the noise variance tends to zero. In each case, we prove that our proposed criterion gives the true model with a probability approaching one. Additionally, we devise a computationally affordable algorithm to conduct model selection with the proposed criterion in practice. Interestingly, as a side product, our algorithm can provide the ideal regularization parameter for the Lasso estimator such that Lasso selects the true variables. Finally, numerical simulations are included to support our theoretical findings.

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  • 237.
    Palomar, Daniel Pèrez
    et al.
    Princeton University, United States.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Minimum BER linear transceivers for MIMO channels via primal decomposition2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, no 8, p. 2866-2882Article in journal (Refereed)
    Abstract [en]

    This paper considers the employment of linear transceivers for communication through multiple-input multiple-output (MIMO) channels with channel state information (CSI) at both sides of the link. The design of linear MIMO transceivers has been studied since the 1970s by optimizing simple measures of the quality of the system, such as the trace of the mean-square error matrix, subject to a power constraint. Recent results showed how to solve the problem in an optimal way for the family of Schur-concave and Schur-convex cost functions. In particular, when the constellations used on the different transmit dimensions are equal, the bit-error rate (BER) averaged over these dimensions happens to be a Schur-convex function, and therefore, it can be optimally solved. In a more general case, however, when different constellations are used, the average BER is not a Schur-convex function, and the optimal design in terms of minimum BER is an open problem. This paper solves the minimum BER problem with arbitrary constellations by first reformulating the problem in convex form and then proposing two solutions. One is a heuristic and suboptimal solution, which performs remarkably well in practice. The other one is the optimal solution obtained by decomposing the convex problem into several subproblems controlled by a master problem (a technique borrowed from optimization theory), for which extremely simple algorithms exist. Thus, the minimum BER problem can be optimally solved in practice with very simple algorithms.

  • 238.
    Pan, Jiaxian
    et al.
    The Chinese University of Hong Kong.
    Ma, Wing-Kin
    The Chinese University of Hong Kong.
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    MIMO Detection by Lagrangian Dual Maximum-Likelihood Relaxation: Reinterpreting Regularized Lattice Decoding2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 2, p. 511-524Article in journal (Refereed)
    Abstract [en]

    This paper considers lattice decoding for multi-input multi-output (MIMO) detection under PAM constellations. A key aspect of lattice decoding is that it relaxes the symbol bound constraints in the optimal maximum-likelihood (ML) detector for faster implementations. It is known that such a symbol bound relaxation may lead to a damaging effect on the system performance. For this reason, regularization was proposed to mitigate the out-of-bound symbol effects in lattice decoding. However, minimum mean square error (MMSE) regularization is the only method of choice for regularization in the present literature. We propose a systematic regularization optimization approach considering a Lagrangian dual relaxation (LDR) of the ML detection problem. As it turns out, the proposed LDR formulation is to find the best diagonally regularized lattice decoder to approximate the ML detector, and all diagonal regularizations, including the MMSE regularization, can be subsumed under the LDR formalism. We show that for the 2-PAM case, strong duality holds between the LDR and ML problems. Also, for general PAM, we prove that the LDR problem yields a duality gap no worse than that of the well-known semidefinite relaxation method. To physically realize the proposed LDR, the projected subgradient method is employed to handle the LDR problem so that the best regularization can be found. The resultant method can physically be viewed as an adaptive symbol bound control wherein regularized lattice decoding is recursively performed to correct the decision. Simulation results show that the proposed LDR approach can outperform the conventional MMSE-based lattice decoding approach.

  • 239. Patel, A
    et al.
    Biswas, Sinchan
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Optimal GLRT-based robust spectrum sensing for MIMO cognitive radio networks with CSI uncertainty2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476Article in journal (Refereed)
  • 240. Patel, M
    et al.
    Biswas, Sinchan
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Jagannatham, A K
    Optimal GLRT-based robust spectrum sensing for MIMO cognitive radio networks with CSI uncertainty2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 99Article in journal (Refereed)
  • 241.
    Pellaco, Lissy
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Jaldén, Joakim
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    A Matrix-Inverse-Free Implementation of the MU-MIMO WMMSE Beamforming Algorithm2022In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 70, p. 6360-6375Article in journal (Refereed)
    Abstract [en]

    The WMMSE beamforming algorithm is a popular approach to address the NP-hard weighted sum rate (WSR) maximization beamforming problem. Although it efficiently finds a local optimum, it requires matrix inverses, eigendecompositions, and bisection searches, operations that are problematic for real-time implementation. In our previous work, we considered the MU-MISO case with single-antenna receivers and effectively replaced such operations by resorting to a first-order method. Here, we consider the more general and challenging MU-MIMO case with multiple-antenna receivers. Our earlier approach does not generalize to this scenario and cannot be applied to replace all the hard-to-parallelize operations that appear in the MU-MIMO case. Thus, we propose to leverage a reformulation of the auxiliary WMMSE function given by Hu et al. By applying gradient descent and Schulz iterations, we formulate the first variant of the WMMSE algorithm applicable to the MU-MIMO case that is free from matrix inverses and other serial operations and hence amenable to both real-time implementation and deep unfolding. From a theoretical viewpoint, we establish its convergence to a stationary point of the WSR maximization problem. From a practical viewpoint, we show that in a deep-unfolding-based implementation, the matrix-inverse-free WMMSE algorithm attains, within a fixed number of iterations, a WSR comparable to the original WMMSE algorithm truncated to the same number of iterations, yet with significant implementation advantages in terms of parallelizability and real-time execution.

  • 242.
    Persson, Daniel
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Kron, Johannes
    School of Electrical Engineering, KTH.
    Skoglund, Mikael
    School of Electrical Engineering, KTH.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Joint Source-Channel Coding for the MIMO Broadcast Channel2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 4, p. 2085-2090Article in journal (Refereed)
    Abstract [en]

    We investigate the problem of broadcasting analog sources to several users using short codes,employing several antennas at both the transmitter and the receiver, and channel-optimized quantization.Our main objective is to minimize the sum mean square error distortion. A joint multi-user encoder, aswell as a structured encoder with separate encoders for the different users, are proposed. The first encoderoutperforms the latter, which in turn offers large improvements compared to state-of-the-art, over a widerange of channel signal-to-noise ratios. Our proposed methods handle bandwidth expansion, i.e., usageof more channel than source dimensions, automatically. We also derive a lower bound on the distortion.

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  • 243.
    Persson, Daniel
    et al.
    Linköpings universitet.
    Kron, Johannes
    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.
    Larsson, Erik G.
    Linköpings universitet.
    Joint Source-Channel Coding for the MIMO Broadcast Channel2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 4, p. 2085-2090Article in journal (Refereed)
    Abstract [en]

    We investigate the problem of broadcasting analog sources to several users using short codes, employing several antennas at both the transmitter and the receiver, and channel-optimized quantization. Our main objective is to minimize the sum mean-square error distortion. A joint multi-user encoder, as well as a structured encoder with separate encoders for the different users, are proposed. The first encoder outperforms the latter, which, in turn, offers large improvements compared to state-of-the-art, over a wide range of channel signal-to-noise ratios. Our proposed methods handle bandwidth expansion, i.e., usage of more channel than source dimensions, automatically. We also derive a lower bound on the distortion.

  • 244.
    Persson, Daniel
    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.
    Partial Marginalization Soft MIMO Detection with Higher Order Constellations2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 1, p. 453-458Article in journal (Refereed)
    Abstract [en]

    A new method for multiple-input multiple-output (MIMO) detection with soft-output, the partial marginalization (PM) algorithm, was recently proposed. Advantages of the method are that it is straightforward to parallelize, and that it offers a fully predictable runtime. PM trades performance for computational complexity via a user-defined parameter. In the limit of high computational complexity, the algorithm becomes the MAP demodulator. The PM algorithm also works with soft-input, but until now it has been unclear how to apply it for other modulation formats than binary phase-shift keying (BPSK) per real dimension. In this paper, we explain how to extend PM with soft-input to general signaling constellations, while maintaining the low complexity advantage of the original algorithm.

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  • 245. Persson, Daniel
    et al.
    Larsson, Erik G.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Joint Source-Channel Decoding Over MIMO Channels Based on Partial Marginalization2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 12, p. 6734-6739Article in journal (Refereed)
    Abstract [en]

    We investigate fast joint source-channel decoding employed for communication over frequency-flat and frequency-selective block-fading multiple-input multiple-output channels. Our setting has applications for communication with short codes under low-latency constraints. The case of no transmitter channel state information is considered. We propose a partial marginalization decoder that allows performance to be traded for computational complexity, by adjusting a user parameter. By tuning this parameter to its maximum value, the minimum mean square error (MMSE) decoder is obtained. In the conducted simulations, the proposed scheme almost achieves the MMSE performance for a wide range of the channel signal-to-noise ratios, with significant reductions in computational complexity.

  • 246.
    Persson, Daniel
    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, Stockholm, Sweden.
    Joint Source-Channel Decoding over MIMOChannels Based on Partial Marginalization2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 12, p. 6734-6739Article in journal (Refereed)
    Abstract [en]

    We investigate fast joint source-channel decoding employed for communication over frequency-flat and frequency selective block-fading multiple-input multiple-output channels. Our setting has applications for communication with short codes under low-latency constraints. The case of no transmitter channel state information is considered.

    We propose a partial marginalization decoder that allows performance to be traded for computational complexity, by adjusting a user parameter. By tuning this parameter to its maximum value, the minimum mean square error (MMSE)decoder is obtained. In the conducted simulations, the proposed scheme almost achieves the MMSE performance for a wide range of the channel signal-to-noise ratios, with significant reductions in computational complexity.

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    fulltext
  • 247. Persson, Per
    et al.
    Nordebo, Sven
    Claesson, Ingvar
    Design of Discrete Coefficient FIR Filters by a Fast Entropy-Directed Deterministic Annealing Algorithm2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, no 3, p. 1006-1014Article in journal (Refereed)
  • 248.
    Pezeshki, Ali
    et al.
    Program in Applied and Computational Mathematics, Princeton University.
    van Veen, Barry D.
    University of Wisconsin.
    Scharf, Louis L.
    Colorado State University.
    Cox, Harry
    Lockheed Martin–Orincon Defense.
    Nordenvaad, Magnus Lundberg
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Eigenvalue beamforming using a multirank MVDR beamformer and subspace selection2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 5, p. 1954-1967Article in journal (Refereed)
    Abstract [en]

    We derive eigenvalue beamformers to resolve an unknown signal of interest whose spatial signature lies in a known subspace, but whose orientation in that subspace is otherwise unknown. The unknown orientation may be fixed, in which case the signal covariance is rank-1, or it may be random, in which case the signal covariance is multirank. We present a systematic treatment of such signal models and explain their relevance for modeling signal uncertainties. We then present a multirank generalization of the MVDR beamformer. The idea is to minimize the power at the output of a matrix beamformer, while enforcing a data dependent distortionless constraint in the signal subspace, which we design based on the type of signal we wish to resolve. We show that the eigenvalues of an error covariance matrix are fundamental for resolving signals of interest. Signals with rank-1 covariances are resolved by the largest eigenvalues of the error covariance, while signals with multirank covariances are resolved by the smallest eigenvalues. Thus, the beamformers we design are eigenvalue beamformers, which extract signal information from eigenmodes of an error covariance. We address the tradeoff between angular resolution of eigenvalue beamformers and the fraction of the signal power they capture.

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    fulltext
  • 249. Piazza, R.
    et al.
    Shankar, M. R. B.
    Ottersten, Björn
    University of Luxembourg.
    Data predistortion for multicarrier satellite channels based on direct learning2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 22, p. 5868-5880Article in journal (Refereed)
    Abstract [en]

    Satellite communication is facing the urgent need of improving data rate and efficiency to compete with the quality of service offered by terrestrial communication systems. An imminent gain, achievable without the need of upgrading current satellite technology, can be obtained by exploiting multicarrier operation at the transponder and using highly efficient modulation schemes. However, on-board multicarrier joint amplification of high order modulation schemes is a critical operation as it brings severe non-linear distortion effects. These distortions increase as the on-board High Power Amplifier (HPA) is operated to yield higher power efficiencies. In this work, we propose novel techniques to implement on ground predistortion that enable multicarrier transmission of highly efficient modulation schemes over satellite channels without impacting infrastructure on the downlink.

  • 250.
    Pillai, Anu Kalidas Muralidharan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Communication Systems.
    Johansson, Håkan
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Communication Systems.
    Efficient Recovery of Sub-Nyquist Sampled Sparse Multi-Band Signals Using Reconfigurable Multi-Channel Analysis and Modulated Synthesis Filter Banks2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 19, p. 5238-5249Article in journal (Refereed)
    Abstract [en]

    Sub-Nyquist cyclic nonuniform sampling (CNUS) of a sparse multi-band signal generates a nonuniformly sampled signal. Assuming that the corresponding uniformly sampled signal satisfies the Nyquist sampling criterion, the sequence obtained via CNUS can be passed through a reconstructor to recover the missing uniform-grid samples. In order to recover the missing uniform-grid samples, the sequence obtained via CNUS is passed through a reconstructor. At present, these reconstructors have very high design and implementation complexity that offsets the gains obtained due to sub-Nyquist sampling. In this paper, we propose a scheme that reduces the design and implementation complexity of the  reconstructor. In contrast to the existing reconstructors which use only a multi-channel synthesis filter bank (FB), the proposed reconstructor utilizes both analysis and synthesis FBs which makes it feasible to achieve an order-of-magnitude reduction of the complexity. The analysis filters are implemented using polyphase networks whose branches are allpass filters with distinct fractional delays and phase shifts. In order to reduce both the design and the implementation complexity of the  synthesis FB, the synthesis filters are implemented using a cosine-modulated FB. In addition to the reduced complexity of the reconstructor, the proposed multi-channel recovery scheme also supports online reconfigurability which is required in flexible (multi-mode) systems where the user subband locations vary with time.

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