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  • 101.
    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.

  • 102.
    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.

  • 103.
    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), 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.

  • 104.
    Saritas, Serkan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), 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.

  • 105. 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.

  • 106.
    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.

  • 107.
    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.

  • 108. 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)
  • 109.
    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.

  • 110.
    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.

  • 111.
    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.

  • 112.
    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.

  • 113.
    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)
  • 114.
    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)
  • 115. 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.

  • 116. 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.

  • 117. 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.

  • 118. 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.

  • 119.
    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.

  • 120.
    Stoica, Petre
    et al.
    Department of Systems and Control, Uppsala University, Uppsala, Sweden.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    MIMO System Identification: State-space and Subspace Approximations versus Transfer Function and Instrumental Variables2000In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 48, no 11, p. 3087-3099Article in journal (Refereed)
    Abstract [en]

    The identification of multi-input multi-output (MIMO) linear systems has previously received a new impetus with the introduction of the state-space (SS) approach based on subspace approximations. This approach has immediately gained popularity, owing to the fact that it avoids the use of canonical forms, requires the determination of only one structural parameter, and has been empirically shown to yield MIMO models with good accuracy in many cases, However, the SS approach suffers from several drawbacks: there is no well-established rule tied to this approach for determining the structural parameter, and, perhaps more important the SS parameter estimates depend on the data in a rather complicated way, which renders almost futile any attempt to analyze and optimize the performance of the estimator. In this paper, we consider a transfer function (TF) approach based on instrumental variables (IV), as an alternative to the SS approach. We use the simplest canonical TF parameterization in which the denominator is equal to a scalar polynomial times the identity matrix. The analysis and optimization of the statistical accuracy of the TF approach is straightforward. Additionally, a simple test tailored to this approach is devised for estimating the single structural parameter needed. A simulation study, in which we compare the performances of the SS and the TF approaches, shows that the latter can provide more accurate models than the former at a lower computational cost.

  • 121.
    Stoica, Petre
    et al.
    Department of Systems and Control, Uppsala University, Uppsala, Sweden.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Comments on "Min-Norm interpretations and consistency of MUSIC, MODE, and ML"1998In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 46, no 8, p. 2262-2263Article in journal (Refereed)
    Abstract [en]

    The results and interpretations obtained in the above referred paper are shown to be well known or obvious. Additionally, corrections to some misleading statements in the aforementioned paper are presented.

  • 122.
    Stoica, Petre
    et al.
    Department of Control and Computers, Bucharest Polytechnic Institute, Bucharest, Romania.
    Viberg, Mats
    Department of Electrical Engineering, Linkoping University, Linkoping, Sweden..
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Instrumental Variable Approach to Array Processing in Spatially Correlated Noise Fields1994In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 42, no 1, p. 121-133Article in journal (Refereed)
    Abstract [en]

    High-performance signal parameter estimation from sensor array data is a problem which has received much attention. A number of so-called eigenvector (EV) techniques such as MUSIC, ESPRIT, WSF, and MODE have been proposedin the literature. The EV techniques for array processing require knowledge of the spatial noise correlation matrix that constitutes a significant drawback. A novel instrumental variable (IV) approach to the sensor array problem is proposed. The IV technique relies on the same basic geometric properties as the EV methods to obtain parameter estimates. However, by exploiting the temporal correlation of the source signals, no knowledge of the spatial noisecovariance is required. The asymptotic properties of the IV estimator are examined and an optimal IV method is derived. Computer simulations are presented to study the properties of the IV estimators in samples of practical length. The proposed algorithm is also shown to perform better than MUSIC on a full-scale passive sonar experiment

  • 123. Stokes, V. P.
    et al.
    Händel, Peter
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Comments on "On amplitude and frequency demodulation using energy operators"1998In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 46, no 2, p. 506-507Article in journal (Refereed)
  • 124.
    Sundin, Martin
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Relevance Singular Vector Machine for Low-Rank Matrix Reconstruction2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 20, p. 5327-5339Article in journal (Refereed)
    Abstract [en]

    We develop Bayesian learning methods for low-rank matrix reconstruction and completion from linear measurements. For under-determined systems, the developed methods reconstruct low-rank matrices when neither the rank nor the noise power is known a priori. We derive relations between the proposed Bayesian models and low-rank promoting penalty functions. The relations justify the use of Kronecker structured covariance matrices in a Gaussian-based prior. In the methods, we use expectation maximization to learn the model parameters. The performance of the methods is evaluated through extensive numerical simulations on synthetic and real data.

  • 125. Sundman, Dennis
    et al.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Design and Analysis of a Greedy Pursuit for Distributed Compressed Sensing2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 11, p. 2803-2818Article in journal (Refereed)
    Abstract [en]

    We consider a distributed compressed sensing scenario where many sensors measure correlated sparse signals and the sensors are connected through a network. Correlation between sparse signals is modeled by a partial common support-set. For such a scenario, the main objective of this paper is to develop a greedy pursuit algorithm. We develop a distributed parallel pursuit (DIPP) algorithm based on exchange of information about estimated support-sets at sensors. The exchange of information helps to improve estimation of the partial common support-set, that in turn helps to gradually improve estimation of support-sets in all sensors, leading to a better quality reconstruction performance. We provide restricted isometry property (RIP) based theoretical analysis on the algorithm's convergence and reconstruction performance. Under certain theoretical requirements (i.e., under certain assumptions) on the quality of information exchange over the network and RIP parameters of sensor nodes, we show that the DIPP algorithm converges to a performance level that depends on a scaled additive measurement noise power (convergence in theory) where the scaling coefficient is a function of RIP parameters and information processing quality parameters. Using simulations, we show practical reconstruction performance of DIPP vis-a-vis amount of undersampling, signal-to-measurement-noise ratios and network-connectivity conditions.

  • 126.
    Sundman, Dennis
    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.
    Greedy Pursuits for Distributed Compressed SensingIn: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476Article in journal (Other academic)
  • 127. Swindlehurst, A. L.
    et al.
    Stoica, P.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Exploiting arrays with multiple invariances using MUSIC and MODE2001In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 49, no 11, p. 2511-2521Article in journal (Refereed)
    Abstract [en]

    This paper describes several new techniques for direction of arrival (DOA) estimation using arrays composed of multiple translated and uncalibrated subarrays. The new algorithms can be thought of as generalizations of the MUSIC, Root-MUSIC, and MODE techniques originally developed for fully calibrated arrays. The advantage of these new approaches is that the DOAs can be estimated using either a simple one-dimensional (I-D) search or by rooting a polynomial, as opposed to the multidimensional search required by multiple invariance (MI)-ESPRIT. When it can be applied, the proposed MI-MODE algorithm shares the statistical optimality of MI-ESPRIT. While MI-MUSIC and Root-MI-MUSIC are only optimal for uncorrelated sources, they perform better than a single invariance implementation of ESPRIT and are thus better suited for finding the initial conditions required by the MI-ESPRIT search.

  • 128. SWINDLEHURST, AL
    et al.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    ROY, R
    KAILATH, T
    MULTIPLE INVARIANCE ESPRIT1992In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 40, no 4, p. 867-881Article in journal (Refereed)
    Abstract [en]

    ESPIRIT is a recently developed technique for high-resolution signal parameter estimation with applications to direction-of-arrival estimation and time series analysis. By exploiting invariances designed into the sensor array, parameter estimates are obtained directly, without knowledge of the array response and without computation or search of some spectral measure. The original formulation of ESPIRIT assumes there is only one invariance in the array associated with each dimension of the parameter space. However, in many applications, arrays that possess multiple invariances (e.g., uniform linear arrays, uniformly sampled time series) are employed, and the question of which invariance to use naturally arises. More importantly, it is desirable to exploit the entire invariance structure simultaneously in estimating the signal parameters. Herein, a subspace-fitting formulation of the ESPIRIT problem is presented that provides a framework for extending the algorithm to exploit arrays with multiple invariances. In particular, a multiple invariance (MI) ESPIRIT algorithm is developed and the asymptotic distribution of the estimates obtained. Simulations are conducted to verify the analysis and to compare the performance of MI ESPIRIT with that of several other approaches. The excellent quality of the MI ESPIRIT estimates is explained by recent results which state that, under certain conditions, subspace-fitting methods of this type are asymptotically efficient.

  • 129.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES). Combient AB.
    Gross, James
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks2017In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 18, p. 4862-4873Article in journal (Refereed)
    Abstract [en]

    We consider the problem of decentralized hypothesis testing in a network of energy harvesting sensors, where sensors make noisy observations of a phenomenon and send quantized information about the phenomenon towards a fusion center. The fusion center makes a decision about the present hypothesis using the aggregate received data during a time interval. We explicitly consider a scenario under which the messages are sent through parallel access channels towards the fusion center. To avoid limited lifetime issues, we assume each sensor is capable of harvesting all the energy it needs for the communication from the environment. Each sensor has an energy buffer (battery) to save its harvested energy for use in other time intervals. Our key contribution is to formulate the problem of decentralized detection in a sensor network with energy harvesting devices. Our analysis is based on a queuing-theoretic model for the battery and we propose a sensor decision design method by considering long term energy management at the sensors. We show how the performance of the system changes for different battery capacities. We then numerically show how our findings can be used in the design of sensor networks with energy harvesting sensors.

  • 130.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Jalden, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Bayesian Design of Tandem Networks for Distributed Detection With Multi-bit Sensor Decisions2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 7, p. 1821-1831Article in journal (Refereed)
    Abstract [en]

    We consider the problem of decentralized hypothesis testing under communication constraints in a topology where several peripheral nodes are arranged in tandem. Each node receives an observation and transmits a message to its successor, and the last node then decides which hypothesis is true. We assume that the observations at different nodes are, conditioned on the true hypothesis, independent and the channel between any two successive nodes is considered error-free but rate-constrained. We propose a cyclic numerical design algorithm for the design of nodes using a person-by-person methodology with the minimum expected error probability as a design criterion, where the number of communicated messages is not necessarily equal to the number of hypotheses. The number of peripheral nodes in the proposed method is in principle arbitrary and the information rate constraints are satisfied by quantizing the input of each node. The performance of the proposed method for different information rate constraints, in a binary hypothesis test, is compared to the optimum rate-one solution due to Swaszek and a method proposed by Cover, and it is shown numerically that increasing the channel rate can significantly enhance the performance of the tandem network. Simulation results for $M$-ary hypothesis tests also show that by increasing the channel rates the performance of the tandem network significantly improves.

  • 131.
    Tarighati, Alla
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Jalden, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Optimality of Rate Balancing in Wireless Sensor Networks2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 14Article in journal (Refereed)
    Abstract [en]

    We consider the problem of distributed binary hypothesis testing in a parallel network topology where sensors independently observe some phenomenon and send a finite rate summary of their observations to a fusion center for the final decision. We explicitly consider a scenario under which (integer) rate messages are sent over an error free multiple access channel, modeled by a sum rate constraint at the fusion center. This problem was previously studied by Chamberland and Veeravalli, who provided sufficient conditions for the optimality of one bit sensor messages. Their result is however crucially dependent on the feasibility of having as many one bit sensors as the (integer) sum rate constraint of the multiple access channel, an assumption that can often not be satisfied in practice. This prompts us to consider the case of an a-priori limited number of sensors and we provide sufficient condition under which having no two sensors with rate difference more than one bit, so called rate balancing, is an optimal strategy with respect to the Bhattacharyya distance between the hypotheses at the input to the fusion center. We further discuss explicit observation models under which these sufficient conditions are satisfied.

  • 132. Teixeira, Andre
    et al.
    Ghadimi, Euhanna
    Shames, Iman
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Mikael
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    The ADMM Algorithm for Distributed Quadratic Problems: Parameter Selection and Constraint Preconditioning2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 2, p. 290-305Article in journal (Refereed)
    Abstract [en]

    This paper presents optimal parameter selection and preconditioning of the alternating direction method of multipliers (ADMM) algorithm for a class of distributed quadratic problems, which can be formulated as equality-constrained quadratic programming problems. The parameter selection focuses on the ADMM step-size and relaxation parameter, while the preconditioning corresponds to selecting the edge weights of the underlying communication graph. We optimize these parameters to yield the smallest convergence factor of the iterates. Explicit expressions are derived for the step-size and relaxation parameter, as well as for the corresponding convergence factor. Numerical simulations justify our results and highlight the benefits of optimal parameter selection and preconditioning for the ADMM algorithm.

  • 133. Tervo, O.
    et al.
    Pennanen, H.
    Christopoulos, D.
    Chatzinotas, S.
    Ottersten, Björn
    University of Luxembourg.
    Distributed Optimization for Coordinated Beamforming in Multicell Multigroup Multicast Systems: Power Minimization and SINR Balancing2018In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 1, p. 171-185Article in journal (Refereed)
    Abstract [en]

    This paper considers coordinated multicast beamforming in a multicell multigroup multiple-input single-output system. Each base station (BS) serves multiple groups of users by forming a single beam with common information per group. We propose centralized and distributed beamforming algorithms for two different optimization targets. The first objective is to minimize the total transmission power of all the BSs while guaranteeing the user-specific minimum quality-of-service targets. The semidefinite relaxation (SDR) method is used to approximate the nonconvex multicast problem as a semidefinite program (SDP), which is solvable via centralized processing. Subsequently, two alternative distributed methods are proposed. The first approach turns the SDP into a two-level optimization via primal decomposition. At the higher level, intercell interference powers are optimized for fixed beamformers, whereas the lower level locally optimizes the beamformers by minimizing BS-specific transmit powers for the given intercell interference constraints. The second distributed solution is enabled via an alternating direction method of multipliers, where the intercell interference optimization is divided into a local and a global optimization by forcing the equality via consistency constraints. We further propose a centralized and a simple distributed beamforming design for the signal-to-interference-plus-noise ratio (SINR) balancing problem in which the minimum SINR among the users is maximized with given per-BS power constraints. This problem is solved via the bisection method as a series of SDP feasibility problems. The simulation results show the superiority of the proposed coordinated beamforming algorithms over traditional noncoordinated transmission schemes, and illustrate the fast convergence of the distributed methods. Index Terms—Alternating direction method of multipliers, distributed optimization, multi-cell coordination, physical layer multigroup multicasting, primal decomposition, SINR balancing, sum power minimization.

  • 134. Tervo, O.
    et al.
    Tran, L.
    Pennanen, H.
    Chatzinotas, S.
    Ottersten, Björn
    Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg City, 2721, Luxembourg.
    Juntti, M.
    Energy-Efficient Multicell Multigroup Multicasting With Joint Beamforming and Antenna Selection2018In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 18, p. 4904-4919Article in journal (Refereed)
    Abstract [en]

    This paper studies the energy efficiency and sum rate tradeoff for coordinated beamforming in multicell multiuser multigroup multicast multiple-input single-output systems. We first consider a conventional network energy efficiency maximization (EEmax) problem by jointly optimizing the transmit beamformers and antennas selected to be used in transmission. We also account for per-antenna maximum power constraints to avoid nonlinear distortion in power amplifiers and user-specific minimum rate constraints to guarantee certain service levels and fairness. To be energy efficient, transmit antenna selection is employed. It eventually leads to a mixed-Boolean fractional program. We then propose two different approaches to solve this difficult problem. The first solution is based on a novel modeling technique that produces a tight continuous relaxation. The second approach is based on sparsity-inducing method, which does not require the introduction of any Boolean variable. We also investigate the tradeoff between the energy efficiency and sum rate by proposing two different formulations. In the first formulation, we propose a new metric, that is, the ratio of the sum rate and the so-called weighted power. Specifically, this metric reduces to EEmax when the weight is 1, and to sum rate maximization when the weight is 0. In the other method, we treat the tradeoff problem as a multiobjective optimization for which a scalarization approach is adopted. Numerical results illustrate significant achievable energy efficiency gains over the method where the antenna selection is not employed. The effect of antenna selection on the energy efficiency and sum rate tradeoff is also demonstrated.

  • 135. Tichavsky, P.
    et al.
    Händel, Peter
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Multicomponent polynomial phase signal analysis using a tracking algorithm1999In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 47, no 5, p. 1390-1395Article in journal (Refereed)
    Abstract [en]

    We describe an efficient technique analyzing signals that comprise a number of polynomial-phase components, The technique is based on a recently proposed "multiple frequency tracker," which is an algorithm for recursive estimation of parameters of multiple sine waves in noise, It has a relatively low SNR threshold and moderate computational complexity.

  • 136. Tichavsky, P.
    et al.
    Händel, Peter
    Two algorithms for adaptive retrieval of slowly time-varying multiple cisoids in noise1995In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 43, no 5, p. 1116-1127Article in journal (Refereed)
    Abstract [en]

    Two algorithms for tracking parameters of slowly varying multiple complex sine waves (cisoids) in noise (the multiple frequency tracker and the adaptive notch filter) are described, For high signal-to-noise ratio (SNR), the properties of the algorithms (i.e., stability, noise rejection, and tracking speed) are studied analytically using a linear filter approximation technique, The tradeoff between noise rejection and tracking error for both algorithms is shown to be similar, Different choices of the design variables are discussed, namely i) minimal mean-square estimation error for random walk modeled frequency variations and ii) minimal stationary estimation variance subject to a given tracking delay.

  • 137.
    Tsakonas, Efthymios
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Semidefinite Relaxations of Robust Binary Least Squares Under Ellipsoidal Uncertainty Sets2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 11, p. 5169-5180Article in journal (Refereed)
    Abstract [en]

    The problem of finding the least squares solution to a system of equations Hs = y is considered, when is a vector of binary variables and the coefficient matrix H is unknown but of bounded uncertainty. Similar to previous approaches to robust binary least squares, we explore the potential of a min-max design with the aim to provide solutions that are less sensitive to the uncertainty in H . We concentrate on the important case of ellipsoidal uncertainty, i.e., the matrix H is assumed to be a deterministic unknown quantity which lies in a given uncertainty ellipsoid. The resulting problem is NP-hard, yet amenable to convex approximation techniques: Starting from a convenient reformulation of the original problem, we propose an approximation algorithm based on semidefinite relaxation that explicitly accounts for the ellipsoidal uncertainty in the coefficient matrix. Next, we show that it is possible to construct a tighter relaxation by suitably changing the description of the feasible region of the problem, and formulate an approximation algorithm that performs better in practice. Interestingly, both relaxations are derived as Lagrange bidual problems corresponding to the two equivalent problem reformulations. The strength of the proposed tightened relaxation is demonstrated by pertinent simulations.

  • 138.
    Tsakonas, Efthymios
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sidiropoulos, Nicholas D.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg .
    Sparse Conjoint Analysis Through Maximum Likelihood Estimation2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 22, p. 5704-5715Article in journal (Refereed)
    Abstract [en]

    Conjoint analysis (CA) is a classical tool used in preference assessment, where the objective is to estimate the utility function of an individual, or a group of individuals, based on expressed preference data. An example is choice-based CA for consumer profiling, i.e., unveiling consumer utility functions based solely on choices between products. A statistical model for choice-based CA is investigated in this paper. Unlike recent classification-based approaches, a sparsity-aware Gaussian maximum likelihood (ML) formulation is proposed to estimate the model parameters. Drawing from related robust parsimonious modeling approaches, the model uses sparsity constraints to account for outliers and to detect the salient features that influence decisions. Contributions include conditions for statistical identifiability, derivation of the pertinent Cramer-Rao Lower Bound (CRLB), and ML consistency conditions for the proposed sparse nonlinear model. The proposed ML approach lends itself naturally to l(1)-type convex relaxations which are well-suited for distributed implementation, based on the alternating direction method of multipliers (ADMM). A particular decomposition is advocated which bypasses the apparent need for outlier communication, thus maintaining scalability. The performance of the proposed ML approach is demonstrated by comparing against the associated CRLB and prior state-of-the-art using both synthetic and real data sets.

  • 139.
    Tsakonas, Efthymios
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Sidiropoulos, Nikos
    University of Minessota, Mineapolis.
    Swami, Ananthram
    Army Research Lab (ARL), USA.
    Optimal Particle Filters for Tracking a Time- Varying Harmonic or Chirp Signal2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 10, p. 4598-4610Article in journal (Refereed)
    Abstract [en]

    We consider the problem of tracking the time-varying (TV) parameters of a harmonic or chirp signal using particle filtering (PF) tools. Similar to previous PF approaches to TV spectral analysis, we assume that the model parameters (complex amplitude, frequency, and frequency rate in the chirp case) evolve according to a Gaussian AR(1) model; but we concentrate on the important special case of a single TVharmonic or chirp.We show that the optimal importance function that minimizes the variance of the particle weights can be computed in closed form, and develop procedures to draw samples from it. We further employ Rao–Blackwellization to come up with reduced-complexity versions of the optimal filters. The end result is custom PF solutions that are considerably more efficient than generic ones, and can be used in a broad range of important applications that involve a single TV harmonic or chirp signal, e.g., TV Doppler estimation in communications, and radar.

  • 140. Valyrakis, Alexandros
    et al.
    Tsakonas, Efthymios
    Sidiropoulos, Nikos
    University of Minessota, Mineapolis.
    Swami, Ananthram
    Army Research Lab (ARL), USA.
    Stochastic Modeling and Particle Filtering Algorithms for Tracking a Frequency-Hopped Signal2009In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 57, no 8, p. 3108-3118Article in journal (Refereed)
    Abstract [en]

    The problem of tracking a frequency-hopped signal without knowledge of its hopping pattern is considered. The problem is of interest in military communications, where, in addition to frequency, hop timing can also be randomly shifted to guard against unauthorized reception and jamming. A conceptually simple nonlinear and non-Gaussian stochastic state-space model is proposed to capture the randomness in carrier frequency and hop timing. This model is well-suited for the application of particle filtering tools: it is possible to compute the optimal (weight variance-minimizing) importance function in closed-form. A convenient mixture representation of the latter is employed together with Rao-Blackwellization to derive a very simple optimal sampling procedure. This is representative of the state-of-art in terms of systematic design of particle filters. A heuristic design approach is also developed, using the mode of the spectrogram to localize hop particles. Performance is assessed in a range of experiments using both simulated and measured data. Interestingly, the results indicate that the heuristic design approach can outperform the systematic one, and both are robust to model assumptions.

  • 141. VIBERG, M
    et al.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    SENSOR ARRAY-PROCESSING BASED ON SUBSPACE FITTING1991In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 39, no 5, p. 1110-1121Article in journal (Refereed)
    Abstract [en]

    A large number of signal processing problems are concerned with estimating unknown signal parameters from sensor array measurements. This area has drawn much interest and many methods for parameter estimation based on array data have appeared in the literature. This paper presents some of these algorithms as variations of the same subspace fitting problem. The methods considered herein are the deterministic maximum likelihood method (ML), ESPRIT, and a recently proposed multidimensional signal subspace method. These methods are formulated in a subspace fitting based framework, which provides insight into their algebraic and asymptotic relations. It is shown that by introducing a specific weighting matrix, the multidimensional signal subspace method can achieve the same asymptotic properties as ML. The asymptotic distribution of the estimation error is derived for a general subspace weighting and the weighting that provides minimum variance estimates is identified. The resulting optimal technique is termed the weighted subspace fitting (WSF) method. Numerical examples indicate that the asymptotic variance of the WSF estimates coincides with the Cramer-Rao bound. The performance improvement compared to the other techniques is found to be most prominent for highly correlated signals. A simulation study is presented, indicating that the asymptotic variance expressions are valid for a wide range of scenarios.

  • 142. VIBERG, M
    et al.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    KAILATH, T
    DETECTION AND ESTIMATION IN SENSOR ARRAYS USING WEIGHTED SUBSPACE FITTING1991In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 39, no 11, p. 2436-2449Article in journal (Refereed)
    Abstract [en]

    This paper addresses the problem of signal parameter estimation of narrow-band emitter signals impinging on an array of sensors. A multidimensional estimation procedure is proposed, which applies to arbitrary array structures and signal correlation. The method is based on the recently introduced weighted subspace fitting (WSF) criterion, and includes schemes for both detecting the number of sources and estimating the signal parameters. A Gauss-Newton type algorithm is suggested for minimizing the WSF criterion. A new detection scheme is also formulated based on the asymptotic distribution of the WSF cost function. Strong consistency of the detection algorithm is proved for arbitrary signal correlation, including coherence. The WSF detection method is compared to a recently proposed information theoretic approach, and found to provide a significant improvement for high signal correlation scenarios. Simulations are carried out comparing the proposed WSF technique to the deterministic maximum likelihood (ML) method. The WSF scheme is found to be limited only by the estimation accuracy and not by the initialization or detection. This does not appear to be true for the ML method.

  • 143. Viberg, M
    et al.
    Stoica, P
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Maximum likelihood array processing in spatially correlated noise fields using parameterized signals1997In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 45, no 4, p. 996-1004Article in journal (Refereed)
    Abstract [en]

    This paper deals with the problem of estimating signal parameters using an array of sensors, This problem is of interest in a variety of applications, such as radar and sonar source localization, A vast number of estimation techniques have been proposed in the literature during the past two decades, Most of these can deliver consistent estimates only if the covariance matrix of the background noise is known. In many applications, the aforementioned assumption is unrealistic. Recently, a number of contributions have addressed the problem of signal parameter estimation in unknown noise environments based on various assumptions on the noise, Herein, a different approach is taken. We assume instead that the signals are partially known, The received signals are modeled as linear combinations of certain known basis functions, The exact maximum likelihood (ML) estimator for the problem at hand is derived, as well as a computationally more attractive approximation, The Cramer-Rao lower bound (CRB) on the estimation error variance is also derived and found to coincide with the CRB, assuming an arbitrary deterministic model and known noise covariance.

  • 144.
    Viberg, Mats
    et al.
    Department of Applied Electronics, Chalmers University of Technology, Gothenburg, Sweden..
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Nehorai, Arye
    Department of Electrical Engineering, Yale University, New Haven, CT 06520 USA..
    Performance analysis of direction finding with large arrays and finite data1995In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 43, no 2, p. 469-477Article in journal (Refereed)
    Abstract [en]

    This paper considers analysis of methods for estimating the parameters of narrow-band signals arriving at an array of sensors. This problem has important applications in, for instance, radar direction finding and underwater source localization. The so-called deterministic and stochastic maximum likelihood (ML) methods are the main focus of this paper. A performance analysis is carried out assuming a finite number of samples and that the array is composed of a sufficiently large number of sensors. Several thousands of antennas are not uncommon in, e.g., radar applications. Strong consistency of the parameter estimates is proved, and the asymptotic covariance matrix of the estimation error is derived. Unlike the previously studied large sample case, the present analysis shows that the accuracy is the same for the two ML methods. Furthermore, the asymptotic covariance matrix of the estimation error coincides with the deterministic Cramer-Rao bound. Under a certain assumption, the ML methods can be implemented by means of conventional beamforming for a large enough number of sensors. We also include a simple simulation study, which indicates that both ML methods provide efficient estimates for very moderate array sizes, whereas the beamforming method requires a somewhat larger array aperture to overcome the inherent bias and resolution problem.

  • 145.
    Viberg, Mats
    et al.
    Department of Applied Electronics, Chalmers Institute of Technology, Gothenburg, Sweden.
    Stoica, Petre
    Department of Control and Computers, Bucharest Polytechnic Institute, Bucharest, Romania.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Array Processing in Correlated Noise Fields Based on Instrumental Variables and Subspace Fitting1995In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 43, no 5, p. 1187-1199Article in journal (Refereed)
    Abstract [en]

    Accurate signal parameter estimation from sensor array data is a problem which has received much attention in the last decade. A number of parametric estimation techniques have been proposed in the literature. In general, these methods require knowledge of the sensor-to-sensor correlation of the noise, which constitutes a significant drawback. This difficulty can be overcome only by introducing alternative assumptions that enable separating the signals from the noise. In some applications, the raw sensor outputs can be preprocessed so that the emitter signals are temporally correlated with correlation length longer than that of the noise. An instrumental variable (IV) approach can then be used for estimating the signal parameters without knowledge of the spatial color of the noise. A computationally simple IV approach has recently been proposed by the authors. Herein, a refined technique that can give significantly better performance is derived. A statistical analysis of the parameter estimates is performed, enabling optimal selection of certain user-specified quantities. A lower bound on the attainable error variance is also presented. The proposed optimal IV method is shown to attain the bound if the signals have a quasideterministic character

  • 146. Volcker, B.
    et al.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Chirp parameter estimation from a sample covariance matrix2001In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 49, no 3, p. 603-612Article in journal (Refereed)
    Abstract [en]

    This paper considers the problem of estimating the bandwidth and the center frequency of a linear chirp signal. The nonstationarity property of chirp signals implies that the signal has high rank and reduces the applicability of subspace-based algorithms significantly. However, the special structure of the sample covariance matrix invites the use of regular frequency estimation algorithms. Herein, we show how subspace-type algorithms may be modified to provide accurate signal parameter estimates for linear chirp signals at reasonable complexity, The root-MUSIC algorithm will be used as an example.

  • 147.
    von Wrycza, Peter
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shankar, M. R. Bhavani
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Signal Processing.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Signal Processing.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Signal Processing.
    Properties of Iterative Water-Filling Algorithm for Flat-Fading Multi-User Environments2010In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476Article in journal (Other academic)
  • 148.
    Völcker, Björn
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Händel, Peter
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Frequency estimation from proper sets of correlations2002In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 50, no 4, p. 791-802Article in journal (Refereed)
    Abstract [en]

    As a complement to the periodogram, low-complexity frequency estimators are of interest. One such estimator is based on Prony's method and rely on phase information of the auto correlations. Without prior knowledge of the frequency (e.g., a given frequency interval), the frequency cannot be unambiguously estimated from a single correlation only. In this paper, we introduce a new method of phase unwrapping using an arbitrary number (more than one) of correlations. From this arbitrary set of correlations, we propose a weighted average estimator. We derive the asymptotic performance and show how the correlation lags should be properly chosen. From a design aspect, there is often a restriction of using a fixed number of computations. In addition, we therefore propose a strategy to find a proper set of correlation lags subject to a given computational complexity. Finally, simulation results that lend support to the theoretical findings are included.

  • 149.
    Wahlström, Jens
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Machine Design (Div.).
    Skog, Isaac
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    La Rosa, Patricio S.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Nehorai, Arye
    The beta-Model-Maximum Likelihood, Cramer-Rao Bounds, and Hypothesis Testing2017In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 12, p. 3234-3246Article in journal (Refereed)
    Abstract [en]

    We study the maximum-likelihood estimator in a setting where the dependent variable is a random graph and covariates are available on a graph level. The model generalizes the well-known beta-model for random graphs by replacing the constant model parameters with regression functions. Cramer-Rao bounds are derived for special cases of the undirected beta-model, the directed beta-model, and the covariate-based beta-model. The corresponding maximum-likelihood estimators are compared with the bounds by means of simulations. Moreover, examples are given on how to use the presented maximum-likelihood estimators to test for directionality and significance. Finally, the applicability of the model is demonstrated using temporal social network data describing communication among healthcare workers.

  • 150. Wang, Jiaheng
    et al.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg-Kirchberg L-1359, Luxembourg.
    Palomar, D. P.
    Robust MIMO precoding for several classes of channel uncertainty2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 12, p. 3056-3070Article in journal (Refereed)
    Abstract [en]

    The full potential of multi-input multi-output (MIMO) communication systems relies on exploiting channel state information at the transmitter (CSIT), which is, however, often subject to some uncertainty. In this paper, following the worst-case robust philosophy, we consider a robust MIMO precoding design with deterministic imperfect CSIT, formulated as a maximin problem, to maximize the worst-case received signal-to-noise ratio or minimize the worst-case error probability. Given different types of imperfect CSIT in practice, a unified framework is lacking in the literature to tackle various channel uncertainty. In this paper, we address this open problem by considering several classes of uncertainty sets that include most deterministic imperfect CSIT as special cases. We show that, for general convex uncertainty sets, the robust precoder, as the solution to the maximin problem, can be efficiently computed by solving a single convex optimization problem. Furthermore, when it comes to unitarily-invariant convex uncertainty sets, we prove the optimality of a channel-diagonalizing structure and simplify the complex-matrix problem to a real-vector power allocation problem, which is then analytically solved in a waterfilling manner. Finally, for uncertainty sets defined by a generic matrix norm, called the Schatten norm, we provide a fully closed-form solution to the robust precoding design, based on which the robustness of beamforming and uniform-power transmission is investigated.

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