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  • 1.
    Jahandideh, Mojtaba
    et al.
    Univ Oulu, Finland.
    Moltafet, Mohammad
    Univ Oulu, Finland.
    Codreanu, Marian
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Latva-aho, Matti
    Univ Oulu, Finland.
    Low Complexity Sparse Channel Estimation for Wideband mmWave Systems: Multi-Stage Approach2019In: 2019 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), IEEE , 2019Conference paper (Refereed)
    Abstract [en]

    We consider the problem of channel estimation in hybrid transceiver architectures operating in millimeter wave (mmWave) band. Due to the dynamic features of the environment and the sensitivity of mmWave bands to blockage and deafness, it is important to estimate mmWave channels with a low complexity and high performance algorithm. In this regard, we exploit the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse signal reconstruction in frequency domain. In order to solve the estimation problem, we propose a multi-stage based low complexity algorithm. Simulation results show that the proposed algorithm significantly reduces the computational complexity while preserving the quality of the estimation.

  • 2.
    Leinonen, Markus
    et al.
    Univ Oulu, Finland.
    Codreanu, Marian
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Giannakis, Georgios
    Univ Minnesota, USA.
    Compressed Sensing with Applications in Wireless Networks2019In: FOUNDATIONS AND TRENDS IN SIGNAL PROCESSING, ISSN 1932-8346, Vol. 13, no 1-2Article in journal (Refereed)
    Abstract [en]

    Sparsity is an attribute present in a myriad of natural signals and systems, occurring either inherently or after a suitable projection. Such signals with lots of zeros possess minimal degrees of freedom and are thus attractive from an implementation perspective in wireless networks. While sparsity has appeared for decades in various mathematical fields, the emergence of compressed sensing (CS) - the joint sampling and compression paradigm - in 2006 gave rise to plethora of novel communication designs that can efficiently exploit sparsity. In this monograph, we review several CS frameworks where sparsity is exploited to improve the quality of signal reconstruction/detection while reducing the use of radio and energy resources by decreasing, e.g., the sampling rate, transmission rate, and number of computations. The first part focuses on several advanced CS signal reconstruction techniques along with wireless applications. The second part deals with efficient data gathering and lossy compression techniques in wireless sensor networks. Finally, the third part addresses CS-driven designs for spectrum sensing and multi-user detection for cognitive and wireless communications.

    The full text will be freely available from 2020-05-29 06:00
  • 3.
    Leinonen, Markus
    et al.
    Univ Oulu, Finland.
    Codreanu, Marian
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Juntti, Markku
    Univ Oulu, Finland.
    Practical Compression Methods for Quantized Compressed Sensing2019In: IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (IEEE INFOCOM 2019 WKSHPS), IEEE , 2019, p. 756-761Conference paper (Refereed)
    Abstract [en]

    In order to save energy of low-power sensors in Internet of Things applications, minimizing the number of bits to compress and communicate real-valued sources with a pre-defined distortion becomes crucial. In such a lossy source coding context, we study rate-distortion (RD) performance of various single-sensor quantized compressed sensing (QCS) schemes for compressing sparse signals via quantized/encoded noisy linear measurements. The paper combines and refines the recent advances of QCS algorithm designs and theoretical analysis. In particular, several practical symbol-by-symbol quantizer based QCS methods of different complexities relying on 1) compress-and-estimate, 2) estimate-and-compress, and 3) support-estimation-and-compress strategies are proposed. Simulation results demonstrate the RD performances of different schemes and compare them to the information-theoretic limits.

  • 4.
    Leinonen, Markus
    et al.
    Univ Oulu, Finland.
    Codreanu, Marian
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Juntti, Markku
    Univ Oulu, Finland.
    Signal Reconstruction Performance under Quantized Noisy Compressed Sensing2019In: 2019 DATA COMPRESSION CONFERENCE (DCC), IEEE , 2019, p. 586-586Conference paper (Refereed)
    Abstract [en]

    We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS) schemes for acquiring sparse signals via quantized/encoded noisy linear measurements, motivated by low-power sensor applications. For such a quantized CS (QCS) context, the paper combines and refines our recent advances in algorithm designs and theoretical analysis. Practical symbol-by-symbol quantizer based QCS methods of different compression strategies are proposed. The compression limit of QCS - the remote RDF - is assessed through an analytical lower bound and a numerical approximation method. Simulation results compare the RD performances of different schemes.

  • 5.
    Moltafet, Mohammad
    et al.
    Univ Oulu, Finland.
    Leinonen, Markus
    Univ Oulu, Finland.
    Codreanu, Marian
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Worst Case Age of Information in Wireless Sensor Networks: A Multi-Access Channel2020In: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 9, no 3, p. 321-325Article in journal (Refereed)
    Abstract [en]

    Freshness of status update packets is essential for enabling a wide range of applications in wireless sensor networks (WSNs). Accordingly, we consider a WSN where sensors communicate status updates to a destination by contending for the channel access based on a carrier sense multiple access (CSMA) method. We analyze the worst case average age of information (AoI) and average peak AoI from the view of one sensor in a system where all the other sensors have a saturated queue. Numerical results illustrate the importance of optimizing the contention window size and the packet arrival rate to maximize the information freshness.

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