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  • 51. Hyberg, P.
    et al.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Array interpolation and bias reduction2004In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 52, no 10, p. 2711-2720Article in journal (Refereed)
    Abstract [en]

    Interpolation (mapping) of data from a given antenna array onto the output of a virtual array of more suitable configuration is well known in array signal processing. This operation allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. Conditions for preserving DOA error variance under such mappings have been derived by several authors. However, in many cases, such as omnidirectional signal surveillance over multiple octaves, systematic mapping errors will dominate over noise effects and cause significant bias in the DOA estimates. To prevent mapping errors from unduly affecting the DOA estimates, this paper uses a geometrical interpretation of a Taylor series expansion of the DOA estimator criterion function to derive an alternative design of the mapping matrix. Verifying simulations show significant bias reduction in the DOA estimates compared with previous designs. The key feature of the proposed design is that it takes into account the orthogonality between the manifold mapping errors and certain gradients of the estimator criterion function. With the new design, mapping of narrowband signals between dissimilar array geometries over wide sectors and large frequency ranges becomes feasible.

  • 52.
    Hyberg, Per
    et al.
    Swedish Defence Research Agency (FOI), SE-172 90, Stockholm, Sweden.
    Jansson, Magnus
    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.
    Array interpolation and DOA MSE reduction2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, no 12, p. 4464-4471Article in journal (Refereed)
    Abstract [en]

    Interpolation or mapping of data from a given real array to data from a virtual array of more suitable geometry is well known in array signal processing. This operation allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. In an earlier companion paper [21], a first-order condition for zero DOA bias under such mapping was derived and was also used to construct a design algorithm for the mapping matrix that minimized the DOA estimate bias. This bias-minimizing theory is now extended to minimize not only bias, but also to consider finite sample effects due to noise and reduce the DOA mean-square error (MSE). An analytical first-order expression for mapped DOA MSE is derived, and a design algorithm for the transformation matrix that minimizes this MSE is proposed. Generally, DOA MSE is not reduced by minimizing the size of the mapping errors but instead by rotating these errors and the associated noise subspace into optimal directions relative to a certain gradient of the DOA estimator criterion function. The analytical MSE expression and the design algorithm are supported by simulations that show not only conspicuous MSE,improvements in relevant scenarios, but also a more robust preprocessing for low signal-to-noise ratios (SNRs) as compared with the pure bias-minimizing design developed in the previous paper.

  • 53.
    Händel, Peter
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Frequency selective adaptive time delay estimation1999In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 47, no 2, p. 532-535Article in journal (Refereed)
    Abstract [en]

    A frequency-selective algorithm for time delay estimation between two channel data is proposed, and its performance is studied. The adaptive stochastic gradient scheme pro\ides a direct estimate of the time delay, It has a low numerical complexity, and it is an alternative to data prefiltering and time delay estimation when the source and noise components have different spectral characteristics.

  • 54. Händel, Peter
    On the performance of the weighted linear predictor frequency estimator1995In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 43, no 12, p. 3070-3071Article in journal (Refereed)
    Abstract [en]

    In a recent paper, a statistical analysis of Kay's weighted linear predictor frequency estimator was carried out. Here, using a different analysis technique to that employed in this same paper, the asymptotic variance of the weighted linear predictor frequency estimator is derived.

  • 55.
    Händel, Peter
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Predictive digital filtering of sinusoidal signals1998In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 46, no 2, p. 364-374Article in journal (Refereed)
    Abstract [en]

    p-step ahead predictive filters for narrowband waveforms with rr, distinct spectral peaks are considered, By minimization of the noise gain, the coefficients of the optimal Lth order FIR predictor is derived, where L > 2m -1, The minimum-length FIR predictor is given by L = 2m -1, and the feedback extension of this predictor is studied. Design of feedback gains subject to different optimization criteria is studied in detail, The generalization to complex-valued signals, cascaded predictors, and adaptive predictors are also included, Several design and simulation examples are presented.

  • 56. Händel, Peter
    et al.
    Nehorai, A.
    Tracking analysis of an adaptive notch filter with constrained poles and zeros1994In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 42, no 2, p. 281-291Article in journal (Refereed)
    Abstract [en]

    This paper analyzes the asymptotic tracking properties of an adaptive notch filter (ANF) with pole-zero constraints [1] for the cancellation or retrieval of multiple time-varying sine waves in additive noise. The asymptotic mean square error (MSE) is analyzed using the methods of Ljung and Gunnarsson [2] when the variations in the underlying frequencies are assumed to be sufficiently small. Closed-form expressions for the MSE are derived as functions of the tuning variables of the algorithm. The results give insight into the operational properties of the algorithm and are used in order to minimize the MSE with respect to the tuning variables. Computer simulations confirm the validity of the derived results.

  • 57. Händel, Peter
    et al.
    Tichavsky, P.
    Frequency rate estimation at high SNR1997In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 45, no 8, p. 2101-2105Article in journal (Refereed)
    Abstract [en]

    The problem of estimating the frequency rate-of-change of complex-valued frequency-modulated signals from noisy observations is considered. The performance of four related estimators is studied, both analytically and by means of simulations, and their relationship to the estimators proposed by Djuric/Kay and Lang/Musicus is established.

  • 58. Jakobsson, Andreas
    et al.
    Glentis, George-Othon
    Gudmundson, Erik
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Computationally Efficient Time-Recursive IAA-Based Blood Velocity Estimation2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 7, p. 3853-3858Article in journal (Refereed)
    Abstract [en]

    High-resolution spectral Doppler is an important and powerful noninvasive tool for estimation of velocities in blood vessels using medical ultrasound scanners. Such estimates are typically formed using an averaged periodogram technique, resulting in well-known limitations in the resulting spectral resolution. Recently, we have proposed techniques to instead form high-resolution data-adaptive estimates exploiting measurements along both depth and emission. The resulting estimates gives noticeably superior velocity estimates as compared to the standard technique, but suffers from a high computational complexity, making it interesting to formulate computationally efficient implementations of the estimators. In this work, by exploiting the rich structure of the iterative adaptive approach (IAA) based estimator, we examine how these estimates can be efficiently implemented in a time-recursive manner using both exact and approximate formulations of the method. The resulting algorithms are shown to reduce the necessary computational load with several orders of magnitude without noticeable loss of performance.

  • 59.
    Jaldén, Joakim
    et al.
    Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology.
    Barbero, Luis G.
    Institute for Digital Communications, Joint Research Institute for Signal and Image Processing, The University of Edinburgh, UK.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Thompson, John S.
    Institute for Digital Communications, Joint Research Institute for Signal and Image Processing, The University of Edinburgh, UK.
    The Error Probability of the Fixed-Complexity Sphere Decoder2009In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 57, no 7, p. 2711-2720Article in journal (Refereed)
    Abstract [en]

    The fixed-complexity sphere decoder (FSD) has been previously proposed for multiple-input multiple-output (MIMO) detection in order to overcome the two main drawbacks of the sphere decoder (SD), namely its variable complexity and its sequential structure. Although the FSD has shown remarkable quasi-maximum-likelihood (ML) performance and has resulted in a highly optimized real-time implementation, no analytical study of its performance existed for an arbitrary MIMO system. Herein, the error probability of the FSD is analyzed, proving that it achieves the same diversity as the maximum-likelihood detector (MLD) independent of the constellation used. In addition, it can also asymptotically yield ML performance in the high-signal-to-noise ratio (SNR) regime. Those two results, together with its fixed complexity, make the FSD a very promising algorithm for uncoded MIMO detection.

  • 60.
    Jaldén, Joakim
    et al.
    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.
    On the complexity of sphere decoding in digital communications2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, ISSN 1053-587X, Vol. 53, no 4, p. 1474-1484Article in journal (Refereed)
    Abstract [en]

    Sphere decoding has been suggested by a number of authors as an efficient algorithm to solve various detection problems in digital communications. In some cases, the algorithm is referred to as an algorithm of polynomial complexity without clearly specifying what assumptions are made about the problem structure. Another claim is that although worst-case complexity is exponential, the expected complexity of the algorithm is polynomial. Herein, we study the expected complexity where the problem size is defined to be the number of symbols jointly detected, and our main result is that the expected complexity is exponential for fixed signal-to-noise ratio (SNR), contrary to previous claims. The sphere radius, which is a parameter of the algorithm, must be chosen to ensure a nonvanishing probability of solving the detection problem. This causes the exponential complexity since the squared radius must grow linearly with problem size. The rate of linear increase is, however, dependent on the noise variance, and thus, the rate of the exponential function is strongly dependent on the SNR. Therefore sphere decoding can be efficient for some SNR and problems of moderate size, even though the number of operations required by the algorithm strictly speaking always grows as an exponential function of the problem size.

  • 61.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Swindlehurst, Andrew Lee
    Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602 USA.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Weighted subspace fitting for general array error models1998In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 46, no 9, p. 2484-2498Article in journal (Refereed)
    Abstract [en]

    Model error sensitivity is an issue common to all high-resolution direction-of-arrival estimators. Much attention has been directed to the design of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to army calibration errors have also appeared in the literature. Herein, one such approach is adopted that assumes that the errors due to finite samples and model errors are of comparable size. A weighted subspace fitting method for very general array perturbation models is derived. This method provides minimum variance estimates under the assumption that the prior distribution of the perturbation model is known. Interestingly, the method reduces to the WSF (MODE) estimator if no model errors are present, Vice versa, assuming that model errors dominate, the method specializes to the corresponding "model-errors-only subspace fitting method." Unlike previous techniques for model errors, the estimator can be implemented using a two-step procedure if the nominal array is uniform and linear, and it is also consistent even if the signals are fully correlated. The paper also contains a large sample analysis of one of the alternative methods, namely, MAPprox, It is shown that MAPprox also provides minimum variance estimates under reasonable assumptions.

  • 62.
    Jarmyr, Simon
    et al.
    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.
    Jorswieck, Eduard A.
    Dresden University of Technology (TUD), Germany.
    Statistical Precoding With Decision Feedback Equalization Over a Correlated MIMO Channel2010In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 58, no 12, p. 6298-6311Article in journal (Refereed)
    Abstract [en]

    The decision feedback (DF) transceiver, combining linear precoding and DF equalization, can establish point-to-point communication over a wireless multiple-input multiple-output channel. Matching the DF-transceiver design parameters to the channel characteristics can improve system performance, but requires channel knowledge. We consider the fast-fading channel scenario, with a receiver capable of tracking the channel-state variations accurately, while the transmitter only has long-term, channel-distribution information. The receiver design problem given channel-state information is well studied in the literature. We focus on transmitter optimization, which amounts to designing a statistical precoder to assist the channel-tailored DF equalizer. We develop a design framework that encompasses a wide range of performance metrics. Common cost functions for precoder optimization are analyzed, thereby identifying a structure of typical cost functions. Transmitter design is approached for typical cost functions in general, and we derive a precoder design formulation as a convex optimization problem. Two important subclasses of cost functions are considered in more detail. First, we explore a symmetry of DF transceivers with a uniform subchannel rate allocation, and derive a simplified convex optimization problem, which can be efficiently solved even as system dimensions grow. Second, we explore the tractability of a certain class of mean square error based cost functions, and solve the transmitter design problem with a simple algorithm that identifies the convex hull of a set of points in R-2. The behavior of DF transceivers with optimal precoders is investigated by numerical means.

  • 63. Joshi, Satya Krishna
    et al.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Codreanu, Marian
    Latva-aho, Matti
    Weighted Sum-Rate Maximization for MISO Downlink Cellular Networks via Branch and Bound2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 4, p. 2090-2095Article in journal (Refereed)
    Abstract [en]

    The problem of weighted sum-rate maximization (WSRMax) in multicell downlink multiple-input single-output (MISO) systems is considered. The problem is known to be NP-hard. We propose a method, based on branch and bound technique, which solves globally the nonconvex WSRMax problem with an optimality certificate. Specifically, the algorithm computes a sequence of asymptotically tight upper and lower bounds and it terminates when the difference between them falls below a pre-specified tolerance. Novel bounding techniques via conic optimization are introduced and their efficiency is demonstrated by numerical simulations. The proposed method can be used to provide performance benchmarks by back-substituting it into many existing network design problems which relies on WSRMax problem. The method proposed here can be easily extended to maximize any system performance metric that can be expressed as a Lipschitz continuous and increasing function of signal-to-interference-plus-noise ratio.

  • 64.
    Järmyr, Simon
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Jorswieck, Eduard A.
    Statistical Framework for Optimization in the Multi-User MIMO Uplink With ZF-DFE2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 10, p. 2730-2745Article in journal (Refereed)
    Abstract [en]

    We consider performance optimization in the uplink of a multiuser multiantenna communication system. Each user multiplexes data onto several independently encoded data streams, which are spatially precoded and conveyed over a fading narrowband multiple-input multiple-output (MIMO) channel. All users' data streams are decoded successively at the receiving base station using zero-forcing decision feedback equalization (ZF-DFE). We target the joint optimization of a decoding order and linear precoders for all users based on long-term channel information. For a class of general MIMO channel models, including the separable-correlation and double-scattering models, we show that the choice of precoder for a certain user does not affect the performance of the others. This leads to a particularly straightforward characterization of general user utility regions as a polyblock, or a convex polytope if time-sharing is allowed. We formulate the decoding-ordering problem under transmit-correlated Rayleigh fading as a linear assignment problem, enabling the use of existing efficient algorithms. Combining decoding ordering with single-user precoder optimization by means of alternating optimization, we propose an efficient iterative scheme that is verified numerically to converge fast and perform close to optimally, successfully reaping the benefits of both precoding and ordering in the MIMO uplink.

  • 65.
    Jöngren, George
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Automatic design of orthogonal or near-orthogonal linear dispersive space-time block codes2006In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 7, p. 2511-2528Article in journal (Refereed)
    Abstract [en]

    This paper considers a wireless multiple-input multiple-output (MIMO) communication system in a frequency-nonselective scenario with spatially uncorrelated Rayleigh fading channel coefficients and investigates the design of linear dispersive (LD) space-time block codes. Efficient LD codes are obtained by optimizing the constituent weight matrices so that an upper bound on the union bound of the codeword error probability is minimized. Interestingly, the proposed design procedure automatically generates LD codes that either correspond to, or are close to, the well-known class of orthogonal space-time block (OSTB) codes. A theoretical analysis confirms this by proving that OSTB codes are indeed optimal, when the setup under study permits their existence. Simulation results demonstrate the excellent performance of the designed codes. In particular, the importance of the codes' near-orthogonal property is illustrated by showing that low-complexity linear equalizer techniques can be used for decoding purposes while incurring a relatively moderate performance loss compared with optimal maximum-likelihood (NIL) decoding.

  • 66.
    Kim, Su Min
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Do, Tan Tai
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Peters, Gunnar
    On the Entropy Computation of Large Complex Gaussian Mixture Distributions2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 17, p. 4710-4723Article in journal (Refereed)
    Abstract [en]

    The entropy computation of Gaussian mixture distributions with a large number of components has a prohibitive computational complexity. In this paper, we propose a novel approach exploiting the sphere decoding concept to bound and approximate such entropy terms with reduced complexity and good accuracy. Moreover, we propose an SNR region-based enhancement of the approximation method to reduce the complexity even further. Using Monte-Carlo simulations, the proposed methods are numerically demonstrated for the computation of the mutual information including the entropy term of various channels with finite constellation modulations such as binary and quadratic amplitude modulation (QAM) inputs for communication applications.

  • 67.
    Kim, Tung Tung
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Caire, Giuseppe
    On Source Transmission Over MIMO Channels With Limited Feedback2009In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 57, no 1, p. 324-341Article in journal (Refereed)
    Abstract [en]

    The problem of source-channel coding over a multiple-antenna (MIMO) channel with quantized channel state information at the transmitter (CSIT) is considered. Upper bounds on the distortion exponents achieved with partial CSIT under a long-term power constraint are developed. It is shown that the distortion exponent with perfect CSIT grows unbounded as the ratio between the channel and source bandwidth increases, while the exponent achieved with any feedback link of fixed, finite resolution is bounded above by a polynomial of the product between the number of transmit and number of receive antennas. The resolution of the feedback link should grow with the bandwidth ratio to make the distortion exponent scale as fast as that in the perfect-CSIT case. We show that in order to achieve the optimal scaling the CSIT feedback resolution must grow logarithmically with the bandwidth ratio for MIMO channels, and faster than linear for the single-input single-output channel. The achievable distortion exponent of some hybrid schemes with heavily quantized feedback is also derived. The results demonstrate that dramatic performance improvement over the case of no CSIT can be achieved by combining simple schemes with a very coarse CSIT feedback.

  • 68.
    Klang, Göran
    et al.
    Ericsson AB, Ericsson Research, SE-164 80 Stockholm, Sweden.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Interference robustness aspects of space-time block code-based transmit diversity2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, ISSN 1053-587X, Vol. 53, no 4, p. 1299-1309Article in journal (Refereed)
    Abstract [en]

    Transmit diversity, which was initially developed for noise-limited environments, has been promoted as a viable candidate for improving the link quality in both existing and future systems for wireless communication. However, to ensure efficient spectrum utilization, receivers operating within wireless multiuser networks must be robust not only to fading and noise but to interference from other system users as well. This work considers interference robustness aspects when transmit diversity, in the form of space-time block coding, is used in multiuser systems. Properties of the space-time block encoded signals such as code rate, block structure, diversity order, etc., and their implications on detection and interference rejection by means of noise whitening are discussed. To handle the presence of space-time block encoded interference, a space-time processing-based extension of an interference rejection combining algorithm is proposed. Results are presented indicating that transmit diversity based on space-time block codes (STBCs) of the linear dispersion type improve robustness against interference in terms of an increased diversity advantage. This can be achieved either by increasing the number of transmit antennas or by reducing the rate of the code. It is also shown, by analysis and by simulation examples, that the performance improvements obtained by using transmit diversity in multiuser systems may rapidly subside as the signal-to-interference ratio decreases. However, by using the proposed interference rejection scheme tailored to the space-time encoded structure, performance improvements of transmit diversity are also obtained in a multiuser environment.

  • 69.
    Klang, Göran
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Structured Semi-Blind Interference Rejection in Dispersive Multichannel Systems2002In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 50, no 8, p. 2027-2036Article in journal (Refereed)
    Abstract [en]

    The impact of interference is one of the most capacity limiting factors in cellular networks today. In order to host more users in existing systems and maximize the capacity of future networks, the use of interference suppression techniques are instrumental. In this paper, we propose a structured semi-blind parameter estimation procedure to support rejection, of unknown cochannel interference (CCI) in systems facing multipath channels with non-negligible delay spread. Employing spatio-temporal processing, the proposed scheme exploits the temporal correlation present in the output signal from an antenna array and the training data of a desired user in order to find parameter estimates that fit the range space of a structured CCI model to the signal subspace of the interfering signals. Simulation results illustrating the behavior of a spatio-temporal receiver using the proposed scheme with respect to both signal-to-noise and signal-to-interference ratios (SNRs and SIRs) are presented. In addition, results highlighting the impact of the training sequence length as well as the impact of the channel model order of the CCI users are also included.

  • 70.
    Klejsa, Janusz
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Guoqiang, Zhang
    Delft University of Technology.
    Li, Minyue
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Kleijn, W. Bastiaan
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Multiple Description Distribution Preserving Quantization2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 24, p. 6410-6422Article in journal (Refereed)
    Abstract [en]

    The notion of a multiple description quantizer (MDQ) includes providing multiple distortion levels. If a human observer is involved, the design of MDQ requires a suitable distortion measure to achieve a graceful quality degradation in the case of description losses. While the mean squared error is a ubiquitous distortion measure for many classic MDQ schemes, it is known to be perceptually relevant only at low distortions. We propose a new MDQ designed according to an unconventional distortion criterion that combines the mean squared error with a constraint on the probability distribution of the reconstructed signal. The performance of the new MDQ is shown to approach that of the classic MDQ asymptotically as rate increases. However, once applied in the context of transform audio coding, the new MDQ significantly outperforms a classic MDQ in perceptual tests. The new scheme is suitable for a wide range of distortions and renders a seamless transition between coding that preserves signal features and coding of a waveform.

  • 71. Krishnamurthy, Vikram
    et al.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Reduced Complexity HMM Filtering With Stochastic Dominance Bounds: A Convex Optimization Approach2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 23, p. 6309-6322Article in journal (Refereed)
    Abstract [en]

    This paper uses stochastic dominance principles to construct upper and lower sample path bounds for Hidden Markov Model (HMM) filters. We consider an HMM consisting of an X-state Markov chain with transition matrix P. By using convex optimization methods for nuclear norm minimization with copositive constraints, we construct low rank stochastic matrices (P) under bar and (P) over bar so that the optimal filters using (P) under bar, (P) over bar provably lower and upper bound (with respect to a partially ordered set) the true filtered distribution at each time instant. Since (P) under bar and (P) over bar are low rank (say R), the computational cost of evaluating the filtering bounds is O(XR) instead of O(X-2). A Monte-Carlo importance sampling filter is presented that exploits these upper and lower bounds to estimate the optimal posterior. Finally, explicit bounds are given on the variational norm between the true posterior and the upper and lower bounds in terms of the Dobrushin coefficient.

  • 72. Kristensson, Martin
    et al.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Further Results and Insights on Subspace Based Sinusoidal Frequency Estimation2001In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 49, no 12, p. 2962-2974Article in journal (Refereed)
    Abstract [en]

    Subspace-based methods for parameter identification have received considerable attention in the literature. Starting with a scalar-valued process, it is well known that subspace-based identification of sinusoidal frequencies is possible if the scalar valued data is windowed to form a low-rank vector-valued process. MUSIC and ESPRIT-like estimators have, for some time, been applied to this vector model. In addition, a statistically attractive Markov-like procedure for this class of methods has been proposed. Herein, the Markov-like procedure is reinvestigated. Several results regarding rank, performance, and structure are given in a compact manner. The large sample equivalence with the approximate maximum likelihood method by Stoica et al. (1988) is also established

  • 73.
    Kristensson, Martin
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    A statistical approach to subspace based blind identification1998In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 46, no 6, p. 1612-1623Article in journal (Refereed)
    Abstract [en]

    Blind identification of single input multiple output systems is considered herein. The low-rank structure of the output signal is exploited to blindly identify the channel using a subspace fitting framework. Two approaches based on a minimal linear parameterization of a subspace are presented and analyzed. The asymptotically best consistent estimate is derived for the class of blind subspace-based techniques. The asymptotic estimation error covariance of the subspace estimates is derived, and the corresponding covariance of the statistically optimal estimates provides a lower bound on the estimation error covariance of subspace methods. A two-step procedure involving only linear systems of equations is presented that asymptotically achieves the bound. Simulations and numerical examples are provided to compare the two approaches.

  • 74. Larsson, Erik G.
    et al.
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Fixed-complexity soft MIMO detection via partial marginalization2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 8, p. 3397-3407Article in journal (Refereed)
  • 75.
    Larsson, Erik G.
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Selén, Yngve
    Linear regression with a sparse parameter vector2007In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 55, no 2, p. 451-460Article in journal (Refereed)
    Abstract [en]

    We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive the minimum mean-square error (MMSE) estimate of the parameter vector and a computationally efficient approximation of it. We also derive an empirical-Bayesian version of the estimator, which does not need any a priori information, nor does it need the selection of any user parameters. As a byproduct, we obtain a powerful model ("basis") selection tool for sparse models. The performance and robustness of our new estimators are illustrated via numerical examples.

  • 76. Law, Ka Lung
    et al.
    Wen, Xin
    Vu, Minh Thanh
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Pesavento, Marius
    General Rank Multiuser Downlink Beamforming With Shaping Constraints Using Real-Valued OSTBC2015In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 21, p. 5758-5771Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider optimal multiuser downlink beamforming in the presence of a massive number of arbitrary quadratic shaping constraints. We combine beamforming with full-rate high dimensional real-valued orthogonal space time block coding (OSTBC) to increase the number of beamforming weight vectors and associated degrees of freedom in the beamformer design. The original multi-constraint beamforming problem is converted into a convex optimization problem using semidefinite relaxation (SDR) which can be solved efficiently. In contrast to conventional (rank-one) beamforming approaches in which an optimal beamforming solution can be obtained only when the SDR solution (after rank reduction) exhibits the rank-one property, in our approach optimality is guaranteed when a rank of eight is not exceeded. We show that our approach can incorporate up to 79 additional shaping constraints for which an optimal beamforming solution is guaranteed as compared to a maximum of two additional constraints that bound the conventional rank-one downlink beamforming designs. Simulation results demonstrate the flexibility of our proposed beamformer design.

  • 77.
    Lindquist, Anders
    KTH, Superseded Departments, Mathematics.
    A new approach to spectral estimation: A tunable high-resolution spectral estimator2000In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 48, no 11, p. 3189-3205Article in journal (Refereed)
    Abstract [en]

    Traditional maximum entropy spectral estimation determines a power spectrum from covariance estimates. Here, we present a new approach to spectral estimation, which is based on the use of filter banks as a means of obtaining spectral interpolation data, Such data replaces standard covariance estimates, A computational procedure for obtaining suitable pole-zero (ARMA) models from such data is presented. The choice of the zeros (MA-part) of the model is completely arbitrary. By suitably choices of filterbank poles and spectral zeros, the estimator can be tuned to exhibit high resolution in targeted regions of the spectrum.

  • 78.
    Lundin, Henrik
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Optimal index-bit allocation for dynamic post-correction of analog-to-digital converters2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, no 2, p. 660-671Article in journal (Refereed)
    Abstract [en]

    Signal processing methods for digital post-correction of analog-to-digital converters (ADCs) are, considered. ADC errors are in general signal dependent, and in addition, they often exhibit dynamic dependence. A novel dynamic post-correction scheme based on look-up tables is proposed. In order to reduce the table size and, thus, the hardware requirements, bit-masking is, introduced. This. is to limit the length of the table index by deselecting index bits. At this point, the problem of which bits to use arises. A mathematical analysis tool is derived, enabling the allocation of index bits to be analyzed. This analysis tool is applied in two optimization problems, optimizing the total harmonic distortion and the signal-to-noise and distortion ratio, respectively, of a corrected ADC. The correction scheme and the optimization problems are illustrated and exemplified using experimental ADC data. The results show that the proposed correction scheme improves the performance of the ADC. They also indicate that the allocation of index bits has a significant impact on the ADC performance, motivating the analysis tool. Finally, the optimization results show that performance improvements compared with static look-up table correction can be achieved, even at a comparable table size.

  • 79.
    Mai, Vien V.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Noisy Accelerated Power Method for Eigenproblems With Applications2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 12, p. 3287-3299Article in journal (Refereed)
    Abstract [en]

    This paper introduces an efficient algorithm for finding the dominant generalized eigenvectors of a pair of symmetric matrices. Combining tools from approximation theory and convex optimization, we develop a simple scalable algorithm with strong theoretical performance guarantees. More precisely, the algorithm retains the simplicity of the well-knownpower method but enjoys the asymptotic iteration complexity of the powerful Lanczos method. Unlike these classic techniques, our algorithm is designed to decompose the overall problem into a series of subproblems that only need to be solved approximately. The combination of good initializations, fast iterative solvers, and appropriate error control in solving the subproblems lead to a linear running time in the input sizes compared to the superlinear time for the traditional methods. The improved running time immediately offers acceleration for several applications. As an example, we demonstrate how the proposed algorithm can be used to accelerate canonical correlation analysis, which is a fundamental statistical tool for learning of a low-dimensional representation of high-dimensional objects. Numerical experiments on real-world datasets confirm that our approach yields significant improvements over the current state of the art.

  • 80.
    Malek Mohammadi, Mohammadreza
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Wahlberg, Bo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Class of Nonconvex Penalties Preserving OverallConvexity in Optimization-Based Mean Filtering2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 24, p. 6650-6664Article in journal (Refereed)
    Abstract [en]

    l1 mean filtering is a conventional, optimizationbasedmethod to estimate the positions of jumps in a piecewiseconstant signal perturbed by additive noise. In this method, the l1 norm penalizes sparsity of the first-order derivative of the signal.Theoretical results, however, show that in some situations, whichcan occur frequently in practice, even when the jump amplitudes tend to , the conventional method identifies false change points.This issue is referred to as stair-casing problem in this paper andrestricts practical importance of l1 mean filtering. In this paper, sparsity is penalized more tightly than the l1 norm by exploiting a certain class of nonconvex functions, while the strict convexity ofthe consequent optimization problem is preserved. This results in a higher performance in detecting change points. To theoretically justify the performance improvements over l1 mean filtering, deterministic and stochastic sufficient conditions for exact changepoint recovery are derived. In particular, theoretical results show that in the stair-casing problem, our approach might be able to exclude the false change points, while l1 mean filtering may fail. A number of numerical simulations assist to show superiorityof our method over l1 mean filtering and another state-of-theart algorithm that promotes sparsity tighter than the l1 norm. Specifically, it is shown that our approach can consistently detectchange points when the jump amplitudes become sufficiently large, while the two other competitors cannot.

  • 81.
    Malek Mohammadi, Mohammareza
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Babaie-Zadeh, Massoud
    Amini, Arash
    Jutten, Cristian
    Recovery of Low-Rank Matrices Under Affine Constraints via a Smoothed Rank Function2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 4, p. 981-992Article in journal (Refereed)
    Abstract [en]

    In this paper, the problem of matrix rank minimization under affine constraints is addressed. The state-of-the-art algorithms can recover matrices with a rank much less than what is sufficient for the uniqueness of the solution of this optimization problem. We propose an algorithm based on a smooth approximation of the rank function, which practically improves recovery limits on the rank of the solution. This approximation leads to a non-convex program; thus, to avoid getting trapped in local solutions, we use the following scheme. Initially, a rough approximation of the rank function subject to the affine constraints is optimized. As the algorithm proceeds, finer approximations of the rank are optimized and the solver is initialized with the solution of the previous approximation until reaching the desired accuracy. On the theoretical side, benefiting from the spherical section property, we will show that the sequence of the solutions of the approximating programs converges to the minimum rank solution. On the experimental side, it will be shown that the proposed algorithm, termed SRF standing for smoothed rank function, can recover matrices, which are unique solutions of the rank minimization problem and yet not recoverable by nuclear norm minimization. Furthermore, it will be demonstrated that, in completing partially observed matrices, the accuracy of SRF is considerably and consistently better than some famous algorithms when the number of revealed entries is close to the minimum number of parameters that uniquely represent a low-rank matrix.

  • 82.
    Malek Mohammadi, Mohammareza
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Babaie-Zadeh, Massoud
    Sharif University of Technology, Iran.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Iterative Concave Rank Approximation for Recovering Low-Rank Matrices2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 60, p. 5213-5226Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a new algorithm for recovery of low-rank matrices from compressed linear measurements. The underlying idea of this algorithm is to closely approximate the rank function with a smooth function of singular values, and then minimize the resulting approximation subject to the linear constraints. The accuracy of the approximation is controlled via a scaling parameter δ, where a smaller δ corresponds to a more accurate fitting. The consequent optimization problem for any finite δ is nonconvex. Therefore, to decrease the risk of ending up in local minima, a series of optimizations is performed, starting with optimizing a rough approximation (a large δ) and followed by successively optimizing finer approximations of the rank with smaller δ's. To solve the optimization problem for any δ > 0, it is converted to a new program in which the cost is a function of two auxiliary positive semidefinite variables. The paper shows that this new program is concave and applies a majorize-minimize technique to solve it which, in turn, leads to a few convex optimization iterations. This optimization scheme is also equivalent to a reweighted Nuclear Norm Minimization (NNM). For any δ > 0, we derive a necessary and sufficient condition for the exact recovery which are weaker than those corresponding to NNM. On the numerical side, the proposed algorithm is compared to NNM and a reweighted NNM in solving affine rank minimization and matrix completion problems showing its considerable and consistent superiority in terms of success rate.

  • 83.
    Malek-Mohammadi, Mohammadreza
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Sharif University of Technology, Iran.
    Koochakzadeh, Ali
    Babaie-Zadeh, Massoud
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Successive Concave Sparsity Approximation for Compressed Sensing2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 21, p. 5657-5671Article in journal (Refereed)
    Abstract [en]

    In this paper, based on a successively accuracy-increasing approximation of the l(0) norm, we propose a new algorithm for recovery of sparse vectors from underdetermined measurements. The approximations are realized with a certain class of concave functions that aggressively induce sparsity and their closeness to the l(0) norm can be controlled. We prove that the series of the approximations asymptotically coincides with the l(1) and l(0) norms when the approximation accuracy changes from the worst fitting to the best fitting. When measurements are noise-free, an optimization scheme is proposed that leads to a number of weighted l(1) minimization programs, whereas, in the presence of noise, we propose two iterative thresholding methods that are computationally appealing. A convergence guarantee for the iterative thresholding method is provided, and, for a particular function in the class of the approximating functions, we derive the closed-form thresholding operator. We further present some theoretical analyses via the restricted isometry, null space, and spherical section properties. Our extensive numerical simulations indicate that the proposed algorithm closely follows the performance of the oracle estimator for a range of sparsity levels wider than those of the state-of-the-art algorithms.

  • 84.
    Maurer, Johannes
    et al.
    Technical University of Vienna.
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Seethaler, Dominik
    ETHZ Zürich.
    Matz, Gerald
    Technical University of Vienna.
    Vector Perturbation Precoding Revisited2011In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 1, p. 315-328Article in journal (Refereed)
    Abstract [en]

    We consider the downlink of a multiuser system with multiple antennas at the base station. Vector perturbation (VP) precoding is a promising variant of transmit-side channel inversion allowing the users to detect their data in a simple, noncooperative manner. VP precoding has so far been developed and analyzed under the assumptions that the transmitter has perfect channel state information (CSI) and that the receivers know perfectly a channel-dependent transmit power normalization factor and have infinite dynamic range. We demonstrate that the violation of any of these idealizing assumptions degrades the performance of VP significantly and almost always results in an error floor. Motivated by this observation, we propose a novel scheme which we term transmit outage precoding (TOP). With TOP, the transmitter uses a prearranged power scaling known by the receivers and refrains from transmitting when channel conditions are poor. We further show how to augment TOP and conventional VP to deal with a finite dynamic range at the receiver. The performance of the proposed schemes under various levels of transmit CSI is studied in terms of a theoretical diversity analysis and illustrated by numerical results.

  • 85.
    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

  • 86.
    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.

  • 87.
    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.

  • 88. 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.

  • 89. Nasir, A. A.
    et al.
    Mehrpouyan, H.
    Durrani, S.
    Blostein, S. D.
    Kennedy, R. A.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    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.

  • 90.
    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.

  • 91.
    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.

  • 92. 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.

  • 93.
    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.

  • 94.
    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.

  • 95.
    Owrang, Arash
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), 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), 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.

  • 96.
    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.

  • 97.
    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.

  • 98.
    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.

  • 99. 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.

  • 100. 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.

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