liu.seSearch for publications in DiVA
Change search
Refine search result
1 - 16 of 16
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 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.
    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.

  • 6.
    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)
  • 7.
    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.

  • 8.
    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.

  • 9.
    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.

  • 10.
    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.

  • 11.
    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.

  • 12.
    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.

  • 13.
    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.

  • 14.
    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.

  • 15.
    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.

  • 16.
    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].

1 - 16 of 16
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf