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  • 1.
    Axell, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Leus, Geert
    Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Poor, H. Vincent
    Princeton University, Department of Electrical Engineering.
    Spectrum sensing for cognitive radio: State-of-the-art and recent advances2012In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 29, no 3, p. 101-116Article in journal (Refereed)
    Abstract [en]

    The ever-increasing demand for higher data rates in wireless communications in the face of limited or underutilized spectral resources has motivated the introduction of cognitive radio. Traditionally, licensed spectrum is allocated over relatively long time periods and is intended to be used only by licensees. Various measurements of spectrum utilization have shown substantial unused resources in frequency, time, and space [1], [2]. The concept behind cognitive radio is to exploit these underutilized spectral resources by reusing unused spectrum in an opportunistic manner [3], [4]. The phrase cognitive radio is usually attributed to Mitola [4], but the idea of using learning and sensing machines to probe the radio spectrum was envisioned several decades earlier (cf., [5]).

  • 2.
    Bergqvist, Göran
    et al.
    Linköping University, Department of Mathematics, Applied Mathematics. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Communication Systems.
    The Higher-Order Singular Value Decomposition Theory and an Application2010In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 27, no 3, p. 151-154Article in journal (Other academic)
    Abstract [en]

    Tensor modeling and algorithms for computing various tensor decompositions (the Tucker/HOSVD and CP decompositions, as discussed here, most notably) constitute a very active research area in mathematics. Most of this research has been driven by applications. There is also much software available, including MATLAB toolboxes [4]. The objective of this lecture has been to provide an accessible introduction to state of the art in the field, written for a signal processing audience. We believe that there is good potential to find further applications of tensor modeling techniques in the signal processing field.

  • 3.
    Björnson, Emil
    KTH Royal Institute Technology, Sweden; Supelec, France.
    Debbah, Merouane
    CentraleSupelec, France.
    Ottersten, Björn
    KTH Royal Institute of Technology.
    Multi-Objective Signal Processing Optimization: The Way to Balance Conflicting Metrics in 5G Systems2014In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 31, no 6, p. 14-23Article in journal (Refereed)
    Abstract [en]

    The evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity: any data service is instantly accessible everywhere. With each generation of cellular networks, we have moved closer to this wireless dream; first by delivering wireless access to voice communications, then by providing wireless data services, and recently by delivering a Wi-Fi-like experience with wide-area coverage and user mobility management. The support for high data rates has been the main objective in recent years [1], as seen from the academic focus on sum-rate optimization and the efforts from standardization bodies to meet the peak rate requirements specified in IMT-Advanced. In contrast, a variety of metrics/objectives are put forward in the technological preparations for fifth-generation (5G) networks: higher peak rates, improved coverage with uniform user experience, higher reliability and lower latency, better energy efficiency (EE), lower-cost user devices and services, better scalability with number of devices, etc. These multiple objectives are coupled, often in a conflicting manner such that improvements in one objective lead to degradation in the other objectives. Hence, the design of future networks calls for new optimization tools that properly handle the existence of multiple objectives and tradeoffs between them.

  • 4.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Reproducible Research: Best Practices and Potential Misuse2019In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 36, no 3, p. 106-+Article in journal (Other academic)
    Abstract [en]

    n/a

  • 5.
    Björnson, Emil
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. Dept. of Electrical Engineering, Linkoping University, Sweden.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure2014In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 31, no 4, p. 142-148Article in journal (Refereed)
    Abstract [en]

    Transmit beamforming is a versatile technique for signal transmission from an array of antennas to one or multiple users [1]. In wireless communications, the goal is to increase the signal power at the intended user and reduce interference to nonintended users. A high signal power is achieved by transmitting the same data signal from all antennas but with different amplitudes and phases, such that the signal components add coherently at the user. Low interference is accomplished by making the signal components add destructively at nonintended users. This corresponds mathematically to designing beamforming vectors (that describe the amplitudes and phases) to have large inner products with the vectors describing the intended channels and small inner products with nonintended user channels.

  • 6.
    Björnson, Emil
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Bengtsson, Mats
    KTH Royal Institute of Technology, Stockholm, Sweden .
    Ottersten, Björn
    KTH Royal Institute of Technology, Stockholm, Sweden; University of Luxembourg.
    Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure2014In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 31, no 4, p. 142-148Article in journal (Refereed)
    Abstract [en]

    Transmit beamforming is a versatile technique for signal transmission from an array of antennas to one or multiple users [1]. In wireless communications, the goal is to increase the signal power at the intended user and reduce interference to nonintended users. A high signal power is achieved by transmitting the same data signal from all antennas but with different amplitudes and phases, such that the signal components add coherently at the user. Low interference is accomplished by making the signal components add destructively at nonintended users. This corresponds mathematically to designing beamforming vectors (that describe the amplitudes and phases) to have large inner products with the vectors describing the intended channels and small inner products with nonintended user channels.

  • 7.
    Björnson, Emil
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jorswieck, Eduard
    Dresden University of Technology, Germany.
    Debbah, Merouane
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Multiobjective Signal Processing Optimization: The way to balance conflicting metrics in 5G systems2014In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 31, no 6, p. 14-23Article in journal (Refereed)
    Abstract [en]

    The evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity: any data service is instantly accessible everywhere. With each generation of cellular networks, we have moved closer to this wireless dream; first by delivering wireless access to voice communications, then by providing wireless data services, and recently by delivering a Wi-Fi-like experience with wide-area coverage and user mobility management. The support for high data rates has been the main objective in recent years [1], as seen from the academic focus on sum-rate optimization and the efforts from standardization bodies to meet the peak rate requirements specified in IMT-Advanced. In contrast, a variety of metrics/objectives are put forward in the technological preparations for fifth-generation (5G) networks: higher peak rates, improved coverage with uniform user experience, higher reliability and lower latency, better energy efficiency (EE), lower-cost user devices and services, better scalability with number of devices, etc. These multiple objectives are coupled, often in a conflicting manner such that improvements in one objective lead to degradation in the other objectives. Hence, the design of future networks calls for new optimization tools that properly handle the existence of multiple objectives and tradeoffs between them.

  • 8.
    Bollen, Math
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Gu, Irene Y.H.
    Eindhoven University of Technology.
    Santoso, Surya
    University of Texas, Austin.
    McGranaghan, Mark F.
    University of Manchester.
    Crossley, Peter A.
    University of Manchester.
    Ribeiro, Moises V
    Universidade Federal de Pernambuco, Recife.
    Ribeiro, Paulo F.
    Universidade Federal de Pernambuco, Recife.
    Bridging the gap between signal and power2009In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 26, no 4, p. 12-31Article in journal (Refereed)
    Abstract [en]

    Signal processing has been used in many different applications, including electric power systems. This is an important category, since a wide variety of digital measurements is available and data analysis is required to deliver diagnostic solutions and correlation with known behaviors. Measurements are taken at numerous locations, and the analysis of data applies to a variety of issues in ¿ power quality (PQ) and reliability ¿ power system and equipment diagnostics ¿ power system control ¿ power system protection. This article focuses on problems and issues related to PQ and power system diagnostics, in particular those where signal processing techniques are extremely important. PQ is a general term that describes the quality of voltage and current waveforms. PQ problems include all electric power problems or disturbances in the supply system that prevent end-user equipment from operating properly. Examples of voltage and current variations that can result in PQ problems include voltage interruptions, long- and short-duration voltage variations, steady-state research opportunities that use the measured voltages and currents to indicate possible equipment and system problems (referred to as equipment diagnostics).

  • 9.
    Brunnström, K
    et al.
    Acreo AB, Sweden.
    Hands, D
    BT Innovate, UK.
    Speranza, F
    Communications Research Centre, Canada.
    Webster, A
    NTIA/ITS, United States.
    VQEG Validation and ITU Standardisation of Objective Perceptual Video Quality Metrics2009In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 26, no 3, p. 96-101Article in journal (Refereed)
    Abstract [en]

    For industry, the need to access accurate and reliable objective video metrics has become more pressing with the advent of new video applications and services such as mobile broadcasting, Internet video, and Internet Protocol television (IPTV). Industry-class objective quality- measurement models have a wide range of uses, including equipment testing (e.g., codec evaluation), transmission- planning and network-dimensioning tasks, head-end quality assurance, in- service network monitoring, and client-based quality measurement. The Video Quality Experts Group (VQEG) is the primary forum for validation testing of objective perceptual quality models. The work of VQEG has resulted in International Telecommunication Union (ITU) standardization of objective quality models designed for standard- definition television and for multimedia applications. This article reviews VQEG's work, paying particular attention to the group's approach to validation testing.

  • 10. Engelke, Ulrich
    et al.
    Kaprykowsky, Hagen
    Zepernick, Hans-Jürgen
    Ndjiki-Nya, Patrik
    Visual Attention in Quality Assessment2011In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 28, no 6, p. 50-59Article in journal (Refereed)
    Abstract [en]

    Perceptual quality metrics are widely deployed in image and video processing systems. These metrics aim to emulate the integral mechanisms of the human visual system (HVS) to correlate well with visual perception of quality. One integral property of the HVS is, however, often neglected: visual attention (VA) [1]. The essential mechanisms associated with VA consist mainly of higher cognitive processing, deployed to reduce the complexity of scene analysis. For this purpose, a subset of the visual information is selected by shifting the focus of attention across the visual scene to the most relevant objects. By neglecting VA, perceptual quality models inherently assume that all objects draw the attention of the viewer to the same degree.

  • 11.
    Evangelista, Gianpaolo
    et al.
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Polotti, Pietro
    VIPS University of Verona, Italy.
    Fractal Additive Synthesis: a Deterministic/Stochastic Model for Sound Synthesis by Analysis2007In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 24, no 2, p. 105-115Article in journal (Refereed)
  • 12.
    Flierl, Markus
    et al.
    Stanford University.
    Girod, Bernd
    Stanford University.
    Multiview Video Compression: Exploiting Inter-Image Similarities2007In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 24, no 6, p. 66-76Article in journal (Refereed)
    Abstract [en]

    Due to the vast raw bit rate of multiview video, efficient compression techniques are essential for 3D scene communication. As the video data originate from the same scene, the inherent similarities of the multiview imagery are exploited for efficient compression. These similarities can be classified into two types, inter-view similarity between adjacent camera views and temporal similarity between temporally successive images of each video.

  • 13.
    Gerkmann, Timo
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. Siemens Corp Res, Princeton, NJ USA.
    Krawczyk-Becker, Martin
    Le Roux, Jonathan
    Phase Processing for Single-Channel Speech Enhancement2015In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 32, no 2, p. 55-66Article in journal (Refereed)
    Abstract [en]

    With the advancement of technology, both assisted listening devices and speech communication devices are becoming more portable and also more frequently used. As a consequence, users of devices such as hearing aids, cochlear implants, and mobile telephones, expect their devices to work robustly anywhere and at any time. This holds in particular for challenging noisy environments like a cafeteria, a restaurant, a subway, a factory, or in traffic. One way to making assisted listening devices robust to noise is to apply speech enhancement algorithms. To improve the corrupted speech, spatial diversity can be exploited by a constructive combination of microphone signals (so-called beamforming), and by exploiting the different spectro-temporal properties of speech and noise. Here, we focus on single-channel speech enhancement algorithms which rely on spectrotemporal properties. On the one hand, these algorithms can be employed when the miniaturization of devices only allows for using a single microphone. On the other hand, when multiple microphones are available, single-channel algorithms can be employed as a postprocessor at the output of a beamformer. To exploit the short-term stationary properties of natural sounds, many of these approaches process the signal in a time-frequency representation, most frequently the short-time discrete Fourier transform (STFT) domain. In this domain, the coefficients of the signal are complex-valued, and can therefore be represented by their absolute value (referred to in the literature both as STFT magnitude and STFT amplitude) and their phase. While the modeling and processing of the STFT magnitude has been the center of interest in the past three decades, phase has been largely ignored. In this article, we review the role of phase processing for speech enhancement in the context of assisted listening and speech communication devices. We explain why most of the research conducted in this field used to focus on estimating spectral magnitudes in the STFT domain, and why recently phase processing is attracting increasing interest in the speech enhancement community. Furthermore, we review both early and recent methods for phase processing in speech enhancement. We aim to show that phase processing is an exciting field of research with the potential to make assisted listening and speech communication devices more robust in acoustically challenging environments.

  • 14.
    Gershman, Alex B.
    et al.
    Darmstadt University of Technology, Germany.
    Sidiropoulos, Nicholas D.
    Technical University of Crete, Greece.
    Shahbazpanahi, Shahram
    University of Ontario Institute of Technology, Oshawa, Canada.
    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), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Convex Optimization-Based Beamforming: From Receive to Transmit and Network Designs2010In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 27, no 3, p. 62-75Article in journal (Refereed)
    Abstract [en]

    In this article, an overview of advanced convex optimization approaches to-multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that apply to a broad class of receive, transmit, and network beamformer design problems. It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution of both long-standing and emerging design problems.

  • 15.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Automotive Safety Systems2009In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 26, no 4, p. 32-47Article in journal (Refereed)
    Abstract [en]

    In this article, we have surveyed the main needs for signal processing development and argued for a sensor fusion approach where all tasks are considered jointly. First, the section "automotive safety systems" summarized a number of safety systems, and it was pointed out that a limited number of sensors can be sufficient to implement a variety of safety systems. Second, the active development of improved communication networks enables new sensor fusion strategies.

  • 16.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Mobile Positioning using Wireless Networks: Possibilities and Fundamental Limitations based on Available Wireless Network Measurements2005In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 22, no 4, p. 41-53Article in journal (Refereed)
    Abstract [en]

    Location in wireless networks is of increasing importance for safety, gaming, and commercial services. There are plenty of measurements available today, ranging from signal arrival times to maps of received power. It is demonstrated how fundamental the Fisher information matrix (FIM) for each measurement is to assess possible location performance. As one illustration, the FCC positioning requirements are transformed to requirements on sufficient information. Thus, it is possible to investigate whether specific sensor configurations would provide acceptable accuracy.

  • 17.
    Jorswieck, Eduard A.
    et al.
    Technical University of Dresden, Germany .
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Luise, Marco
    University of Pisa, Italy.
    Poor, H. Vincent
    Princeton University, USA.
    Game Theory in Signal Processing and Communications2009In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 26, no 5Article in journal (Other academic)
    Abstract [en]

    Game theory is a branch of mathematics aimed at the modeling and understanding of resource conflict problems. Essentially, the theory splits into two branches: noncooperative and cooperative game theory. The distinction between the two is whether or not the players in the game can make joint decisions regarding the choice of strategy. Noncooperative game theory is closely connected to minimax optimization and typically results in the study of various equilibria, most notably the Nash equilibrium. Cooperative game theory examines how strictly rational (selfish) actors can benefit from voluntary cooperation by reaching bargaining agreements. Another distinction is between static and dynamic game theory, where the latter can be viewed as a combination of game theory and optimal control. In general, the theory provides a structured approach to many important problems arising in signal processing and communications, notably resource allocation and robust transceiver optimization. Recent applications also occur in other emerging fields, such as cognitive radio, spectrum sharing, and in multihop-sensor and adhoc networks.

  • 18. Kaiser, Thomas
    et al.
    Bourdoux, André
    Choi, Seungwon
    Fuertes, Andy
    Mecklenbauker, Christoph
    Li, Qinghua
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Papadias, Constantinos
    Paul, Steffen
    Paulraj, Arogyaswami
    Rooyen, Pieter van
    Winters, Jack H.
    When will smart antennas be ready for the market?: Part I2005In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 22, no 2, p. 87-92Article in journal (Other academic)
    Abstract [en]

    This work aims to shed more light on the issue of why smart antennas haven't yet penetrated the market despite all the research efforts that have been put into the technology. The paper looks at this issue from various perspectives and subdivides the discussions into fixed or nomadic and high mobility applications because of the different types of markets, demands, and technological barriers.

  • 19. Kaiser, Thomas
    et al.
    Bourdoux, André
    Choi, Seungwon
    Fuertes, Andy
    Mecklenbauker, Christoph
    Li, Qinghua
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Papadias, Constantinos
    Paul, Steffen
    Paulraj, Arogyaswami
    Rooyen, Pieter van
    Winters, Jack H.
    When will smart antennas be ready for the market?: Part II - results2005In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 22, no 6, p. 174-176Article in journal (Other academic)
    Abstract [en]

    The aim of this two-part forum is to shed more light on the future of smartantennas (SA) through discussions among a balanced group of experts from academia and industry. In part I, which appeared in the March 2005 issue of IEEE Signal Processing Magazine, each of the experts stated his own opinion after exchanging some thoughts by e-mail. Then, a panel session took place at ICAS-SP'05 and a public poll followed. Now, in part II, the results are summarized by the experts. The central topic of the forum was the expectedmarket breakthrough of SA.

  • 20.
    Karlsson, Rickard
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control.
    The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime2017In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 34, no 2, p. 60-69Article in journal (Refereed)
    Abstract [en]

    Most navigation systems today rely on global navigation satellite systems (gnss), including in cars. With support from odometry and inertial sensors, this is a sufficiently accurate and robust solution, but there are future demands. Autonomous cars require higher accuracy and integrity. Using the car as a sensor probe for road conditions in cloud-based services also sets other kind of requirements. The concept of the Internet of Things requires stand-alone solutions without access to vehicle data. Our vision is a future with both invehicle localization algorithms and after-market products, where the position is computed with high accuracy in gnss-denied environments. We present a localization approach based on a prior that vehicles spend the most time on the road, with the odometer as the primary input. When wheel speeds are not available, we present an approach solely based on inertial sensors, which also can be used as a speedometer. The map information is included in a Bayesian setting using the particle filter (PF) rather than standard map matching. In extensive experiments, the performance without gnss is shown to have basically the same quality as utilizing a gnss sensor. Several topics are treated: virtual measurements, dead reckoning, inertial sensor information, indoor positioning, off-road driving, and multilevel positioning.

  • 21.
    Kleijn, W. Bastiaan
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. Victoria Univ Wellington, Wellington, New Zealand.
    Crespo, Joao B.
    Hendriks, Richard C.
    Petkov, Petko N.
    Sauert, Bastian
    Vary, Peter
    Optimizing Speech Intelligibility in a Noisy Environment2015In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 32, no 2, p. 43-54Article in journal (Refereed)
    Abstract [en]

    Modern communication technology facilitates communication from anywhere to anywhere. As a result, low speech intelligibility has become a common problem, which is exacerbated by the lack of feedback to the talker about the rendering environment. In recent years, a range of algorithms has been developed to enhance the intelligibility of speech rendered in a noisy environment. We describe methods for intelligibility enhancement from a unified vantage point. Before one defines a measure of intelligibility, the level of abstraction of the representation must be selected. For example, intelligibility can be measured on the message, the sequence of words spoken, the sequence of sounds, or a sequence of states of the auditory system. Natural measures of intelligibility defined at the message level are mutual information and the hit-or-miss criterion. The direct evaluation of high-level measures requires quantitative knowledge of human cognitive processing. Lower-level measures can be derived from higher-level measures by making restrictive assumptions. We discuss the implementation and performance of some specific enhancement systems in detail, including speech intelligibility index (SII)-based systems and systems aimed at enhancing the sound-field where it is perceived by the listener. We conclude with a discussion of the current state of the field and open problems.

  • 22. Larsson, E. G.
    et al.
    Jorswieck, E. A.
    Lindblom, J.
    Mochaourab, Rami
    Dresden University of Technology, Dresden, Germany.
    Game theory and the flat-fading Gaussian interference channel2009In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 26, no 5, p. 18-27Article in journal (Refereed)
  • 23.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    MIMO Detection Methods: How They Work2009In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 26, no 3, p. 91-95Article in journal (Refereed)
    Abstract [en]

    The goal of this lecture has been to provide an overview of approaches, in the communications receiver context. Which method is the best in practice? This depends much on the purpose of solving : what error rate can be tolerated, what is the ultimate measure of performance (e.g., frame-error-rate, worst-case complexity, or average complexity), and what computational platform is used. Additionally, the bits in s may be part of a larger code word and different s vectors in that code word may either see the same H (slow fading) or many different realizations of H (fast fading). This complicates the picture, because notions that are important in slow fading (such as spatial diversity) are less important in fast fading, where diversity is provided anyway by time variations. Detection for MIMO has been an active field for more than ten years, and this research will probably continue for some time.

  • 24.
    Larsson, Erik G.
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Danev, Danyo
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering. University of Sofia, Bulgaria.
    Olofsson, Mikael
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Sörman, Simon
    Ericsson Res, Linkoping, Sweden.
    Teaching the Principles of Massive MIMO: Exploring reciprocity-based multiuser MIMO beamforming using acoustic waves2017In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 34, no 1, p. 40-47Article in journal (Refereed)
    Abstract [en]

    Massive multiple-input, multiple-output (MIMO) is currently the most compelling wireless physical layer technology and a key component of fifth-generation (5G) systems. The understanding of its core principles has emerged during the last five years, and material is becoming available that is rigorously refined to focus on timeless fundamentals [1], facilitating the instruction of the topic to both master- and doctoral-level students [2]. Meaningful laboratory work that exposes the operational principles of massive MIMO is more difficult to accomplish. At Linköping University, Sweden, this was achieved through a project course, based on the conceive-design-implement-operate (CDIO) concept [3], and through the creation of a specially designed experimental setup using acoustic signals.

  • 25.
    Larsson, Erik G.
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Jorswieck, Eduard A.
    Dresden Unoversity of Technology.
    Lindblom, Johannes
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Mochaourab, Rami
    Dresden University of Technology.
    Game Theory and the Flat-Fading Gaussian Interference Channel: Analyzing Resource Conflicts in Wireless Networks2009In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 26, no 5, p. 18-27Article in journal (Refereed)
    Abstract [en]

    In this article, we described some basic concepts from noncooperative and cooperative game theory and illustrated them by three examples using the interference channel model, namely, the power allocation game for SISO IFC, the beamforming game for MISO IFC, and the transmit covariance game for MIMO IFC. In noncooperative game theory, we restricted ourselves to discuss the NE and PoA and their interpretations in the context of our application. Extensions to other noncooperative approaches include Stackelberg equilibria and the corresponding question "Who will go first?" We also correlated equilibria where a certain type of common randomness can be exploited to increase the utility region. We leave the large area of coalitional game theory open.

  • 26. Li, Jian
    et al.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    MIMO radar with colocated antennas: Review of some recent work2007In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 24, no 5, p. 106-114Article in journal (Refereed)
  • 27. Li, Jian
    et al.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    The phased array is the maximum SNR active array2010In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 27, no 2, p. 143-144Article in journal (Refereed)
  • 28.
    Liu, Liang
    et al.
    Univ Toronto, Canada.
    Larsson, Erik G
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Yu, Wei
    Univ Toronto, Canada; Canadian Acad Engn, Canada.
    Popovski, Petar
    Aalborg Univ, Denmark.
    Stefanovic, Cedomir
    Aalborg Univ, Denmark.
    de Carvalho, Elisabeth
    Stanford Univ, CA 94305 USA; Aalborg Univ, Denmark.
    Sparse Signal Processing for Grant-Free Massive Connectivity A future paradigm for random access protocols in the Internet of Things2018In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 35, no 5, p. 88-99Article in journal (Refereed)
    Abstract [en]

    The next wave of wireless technologies will proliferate in connecting sensors, machines, and robots for myriad new applications, thereby creating the fabric for the Internet of Things (IoT). A generic scenario for IoT connectivity involves a massive number of machine-type connections, but in a typical application, only a small (unknown) subset of devices are active at any given instant; therefore, one of the key challenges of providing massive IoT connectivity is to detect the active devices first and then decode their data with low latency. This article advocates the usage of grant-free, rather than grant-based random access schemes to overcome the challenge of massive IoT access. Several key signal processing techniques that promote the performance of the grant-free strategies are outlined, with a primary focus on advanced compressed sensing techniques and their applications for the efficient detection of active devices. We argue that massive multiple-input, multiple-output (MIMO) is especially well suited for massive IoT connectivity because the device detection error can be driven to zero asymptotically in the limit as the number of antennas at the base station (BS) goes to infinity by using the multiple-measurement vector (MMV) compressed sensing techniques. This article also provides a perspective on several related important techniques for massive access, such as embedding short messages onto the device-activity detection process and the coded random access.

  • 29.
    Ljung, Patric
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Winskog, Carl
    Pathology Section, The Forensic Sciences Centre, Barbados.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medicine and Care, Medical Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology UHL.
    Lundström, Claes
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra-Imtec AB, Linköping, Sweden.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Forensic Virtual Autopsies by Direct Volume Rendering2007In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 24, no 6, p. 112-116Article in journal (Other academic)
    Abstract [en]

    This paper presents state-of-the-art methods, which address the technical challenges in visualizing large three-dimensional (3D) data and enable rendering at interactive frame rates.

  • 30.
    Nilsson, John Olof
    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.
    Fast Argument Quantization2013In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 30, no 6, p. 169-172Article in journal (Refereed)
    Abstract [en]

    Aquantized low-resolution argument of a complex number or two-dimensional vector is required in many digital signal processing algorithms. Examples include APSK code demodulation for which it may be used to evaluate the Voronoi diagram; low-level processing for many computer vision methods that exploit histograms of gradient sample arguments, e.g., SIFT and HOG; and phase tracking/frequency estimation for which it may be used as a low-cost phase approximation. Often, such quantized arguments will have to be computed many times and under real-time constraints. Therefore, efficient solutions to these calculations are of interest.

  • 31.
    Ortega-Garcia, Javier
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Bigun, Josef
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Reynolds, Douglas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Gonzalez-Rodriguez, Joaquin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Authentication gets personal with biometrics2004In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 21, no 2, p. 50-62Article in journal (Refereed)
    Abstract [en]

    Securing the exchange of intellectual property and providing protection to multimedia contents in distribution systems have enabled the advent of digital rights management (DRM) systems. User authentication, a key component of any DRM system, ensures that only those with specific rights are able to access the digital information. It is here that biometrics play an essential role. It reinforces security at all stages where customer authentication is needed. Biometric recognition, as a means of personal authentication, is an emerging signal processing area focused on increasing security and convenience of use in applications where users need to be securely identified. In this article, we outline the state-of-the-art of several popular biometric modalities and technologies and provide specific applications where biometric recognition may be beneficially incorporated. In addition, the article also discussed integration strategies of biometric authentication technologies into DRM systems that satisfy the needs and requirements of consumers, content providers, and payment brokers, securing delivery channels and contents.

  • 32. Roberts, William
    et al.
    He, Hao
    Li, Jian
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Probing Waveform Synthesis and Receiver Filter Design2010In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 27, no 4, p. 99-112Article in journal (Refereed)
  • 33. Rowe, William
    et al.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Li, Jian
    Spectrally constrained waveform design2014In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 31, no 3, p. 157-162Article in journal (Refereed)
  • 34.
    Rusek, Fredrik
    et al.
    EIT, LU.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Lau, Buon Kiong
    EIT, LU.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Marzetta, Thomas L.
    Bell Laboratories, Alcatel-Lucent, Murray Hill, USA.
    Edfors, Ove
    EIT, LU.
    Tufvesson, Fredrik
    EIT, LU.
    Scaling up MIMO: Opportunities and Challenges with Very Large Arrays2013In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 30, no 1, p. 40-60Article in journal (Refereed)
    Abstract [en]

    Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].

  • 35.
    Savazzi, Stefano
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group. CNR, Inst Elect Comp & Telecommun Engn, I-00185 Rome, Italy.;Univ Calif San Diego, La Jolla, CA 92093 USA.;Forschungszentrum Telekommunikat Wien, Vienna, Austria..
    Sigg, Stephan
    Aalto Univ, Dept Commun & Networking, Aalto, Finland.;Univ Gottingen, Comp Networks Grp, Gottingen, Germany.;TU Braunschweig, Braunschweig, Germany.;Swiss Fed Inst Technol, Wearable Comp Lab, Zurich, Switzerland.;Univ Helsinki, Nodes Lab, FIN-00014 Helsinki, Finland.;Natl Inst Informat, Informat Syst Architecture Res Div, Tokyo, Japan..
    Nicoli, Monica
    Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Milan, Italy..
    Rampa, Vittorio
    CNR, Inst Elect Comp & Telecommun Engn, I-00185 Rome, Italy.;Politecn Milan, Milan, Italy..
    Kianoush, Sanaz
    CNR, Inst Elect Comp & Telecommun Engn, I-00185 Rome, Italy..
    Spagnolini, Umberto
    Politecn Milan, Milan, Italy..
    Device-Free Radio Vision for Assisted Living Leveraging wireless channel quality information for human sensing2016In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 33, no 2, p. 45-58Article in journal (Refereed)
    Abstract [en]

    Wireless propagation is conventionally considered as the enabling tool for transporting information in digital communications. However, recent research has shown that the perturbations of the same electromagnetic (EM) fields that are adopted for data transmission can be used as a powerful sensing tool for device-free radio vision. Applications range from human body motion detection and localization to passive gesture recognition. In line with the current evolution of mobile phone sensing [1], radio terminals are not only ubiquitous communication interfaces, but they also incorporate novel or augmented sensing potential, capable of acquiring an accurate human-scale understanding of space and motion. This article shows how radio-frequency (RF) signals can be employed to provide a device-free environmental vision and investigates the detection and tracking capabilities for potential benefits in daily life.

  • 36.
    Stoica, Peter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Babu, Prabhu
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    The Gaussian data assumption leads to the largest Cramér-Rao bound2011In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 28, no 3, p. 132-133Article in journal (Refereed)
  • 37.
    Stoica, Peter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Li, Jian
    Xue, Ming
    Transmit codes and receive filters for radar: A look at the design process2008In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 25, no 6, p. 94-109Article in journal (Refereed)
  • 38.
    Stoica, Peter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Sandgren, Niclas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Smoothed nonparametric spectral estimation via cepsturm thresholding2006In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 23, no 6, p. 34-45Article in journal (Refereed)
  • 39. Umbert, M.
    et al.
    Bonada, J.
    Goto, M.
    Nakano, T.
    Sundberg, Johan
    Uppsala University, Sweden.
    Expression control in singing voice synthesis: Features, approaches, evaluation, and challenges2015In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 32, no 6, p. 55-73Article in journal (Refereed)
    Abstract [en]

    In the context of singing voice synthesis, expression control manipulates a set of voice features related to a particular emotion, style, or singer. Also known as performance modeling, it has been approached from different perspectives and for different purposes, and different projects have shown a wide extent of applicability. The aim of this article is to provide an overview of approaches to expression control in singing voice synthesis. We introduce some musical applications that use singing voice synthesis techniques to justify the need for an accurate control of expression. Then, expression is defined and related to speech and instrument performance modeling. Next, we present the commonly studied set of voice parameters that can change perceptual aspects of synthesized voices. After that, we provide an up-to-date classification, comparison, and description of a selection of approaches to expression control. Then, we describe how these approaches are currently evaluated and discuss the benefits of building a common evaluation framework and adopting perceptually-motivated objective measures. Finally, we discuss the challenges that we currently foresee.

  • 40.
    Umbert, Marti
    et al.
    Univ Politecn Cataluna, Telecommun, E-08028 Barcelona, Spain..
    Bonada, Jordi
    UPF, Comp Sci & Digital Commun, Barcelona, Spain..
    Goto, Masataka
    Waseda Univ, Tokyo, Japan..
    Nakano, Tomoyasu
    Univ Tsukuba, Tsukuba, Ibaraki 305, Japan..
    Sundberg, Johan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Arts, Department of Musicology.
    Expression Control in Singing Voice Synthesis: Features, approaches, evaluation, and challenge2015In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 32, no 6, p. 55-73Article in journal (Refereed)
    Abstract [en]

    In the context of singing voice synthesis, expression control manipulates a set of voice features related to a particular emotion, style, or singer. Also known as performance modeling, it has been approached from different perspectives and for different purposes, and different projects have shown a wide extent of applicability. The aim of this article is to provide an overview of approaches to expression control in singing voice synthesis. We introduce some musical applications that use singing voice synthesis techniques to justify the need for an accurate control of expression. Then, expression is defined and related to speech and instrument performance modeling. Next, we present the commonly studied set of voice parameters that can change perceptual aspects of synthesized voices. After that, we provide an up-to-date classification, comparison, and description of a selection of approaches to expression control. Then, we describe how these approaches are currently evaluated and discuss the benefits of building a common evaluation framework and adopting perceptually-motivated objective measures. Finally, we discuss the challenges that we currently foresee.

  • 41. Wübben, Dirk
    et al.
    Seethaler, Dominik
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Matz, Gerald
    Lattice Reduction2011In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 28, no 3Article in journal (Refereed)
  • 42.
    Yu, Wei
    et al.
    Univ Toronto, Toronto, ON, Canada.;Univ Toronto, Informat Theory & Wireless Commun, Toronto, ON, Canada.;IEEE, Piscataway, NJ 08854 USA.;Canadian Academy Engn, Ottawa, ON, Canada.;IEEE Signal Proc Commun & Networking Tech Comm, Ottawa, ON, Canada..
    Jaldén, Joakim
    KTH, School of Electrical Engineering (EES), Signal Processing. IEEE Signal Proc Commun & Networking Tech Comm, Ottawa, ON, Canada.
    Perspectives in Signal Processing for Communications and Networking2018In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 35, no 5, p. 188-+Article in journal (Refereed)
  • 43.
    Zachariah, Dave
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Cramér–Rao bound analog of Bayes' rule2015In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 32, no 2, p. 164-168Article in journal (Refereed)
1 - 43 of 43
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