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  • 1.
    Cirkic, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Jan-Åke
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Approximating the LLR Distribution for a Class of Soft-Output MIMO Detectors2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 12, p. 6421-6434Article in journal (Refereed)
    Abstract [en]

    We present approximations of the LLR distribution for a class of fixed-complexity soft-output MIMO detectors, such as the optimal soft detector and the soft-output via partial marginalization detector. More specifically, in a MIMO AWGN setting, we approximate the LLR distribution conditioned on the transmitted signal and the channel matrix with a Gaussian mixture model (GMM). Our main results consist of an analytical expression of the GMM model (including the number of modes and their corresponding parameters) and a proof that, in the limit of high SNR, this LLR distribution converges in probability towards a unique Gaussian distribution.

  • 2.
    Jiang, Xiwen
    et al.
    EURECOM, France.
    Cirkic, Mirsad
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology. Ericsson, Ericsson Research, S-58330 Linkoping, Sweden.
    Kaltenberger, Florian
    EURECOM, France.
    Larsson, Erik G
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Deneire, Luc
    University of Nice Sophia Antipolis, France.
    Knopp, Raymond
    EURECOM, France.
    MIMO-TDD Reciprocity under Hardware Imbalances: Experimental Results2015In: 2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), IEEE , 2015, p. 4949-4953Conference paper (Refereed)
    Abstract [en]

    For time division duplexing (TDD) systems, the physical channel in the air is reciprocal for uplink (UL) and downlink (DL) within the channel coherence time. However when the transceivers radio frequency (RF) hardware is taken into consideration, TDD channel reciprocity no longer holds because of the non-symmetric characteristics of RF transmit and receive chains. Relative calibration has been proposed to compensate this hardware impairment with a multiplicative matrix. In this paper we perform hardware measurements on this calibration matrix which gives a direct insight on the physical phenomenon of TDD transceivers. Especially, we inspect the assumption that this calibration matrix is diagonal, which is widely adopted in literature but has never been verified by experiments. This work can be regarded as an experimental base for TDD calibration or for theoretical analysis of non-perfect channel reciprocity of TDD systems.

  • 3.
    Čirkić, Mirsad
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Efficient MIMO Detection Methods2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    For the past decades, the demand in transferring large amounts of data rapidly and reliably has been increasing drastically. One of the more promising techniques that can provide the desired performance is multiple-input multiple-output (MIMO) technology where multiple antennas are placed at both the transmitting and receiving side of the communication link. This performance potential is extremely high when the dimensions of the MIMO system are increased to an extreme (in the number of hundreds or thousands of antennas). One major implementation difficulty of the MIMO technology is the signal separation (detection) problem at the receiving side of the MIMO link, which holds for medium-size MIMO systems and even more so for large-size systems. This is due to the fact that the transmitted signals interfere with each other and that separating them can be very difficult if the MIMO channel conditions are not beneficial, i.e., the channel is not well-conditioned.

    The main problem of interest is to develop algorithms for practically feasible MIMO implementations without sacrificing the promising performance potential that such systems bring. These methods involve inevitably different levels of approximation. There are computationally cheap methods that come with low accuracy and there are computationally expensive methods that come with high accuracy. Some methods are more applicable in medium-size MIMO than in large-size MIMO and vice versa. Some simple methods for instance, which are typically inaccurate for medium-sized settings, can achieve optimal accuracy for certain large-sized settings that offer close-to-orthogonal spatial signatures. However, when the dimensions are overly increased, then even these (previously) simple methods become computationally burdensome. In different MIMO setups, the difficulty in detection shifts since methods with optimal accuracy are not the same. Therefore, devising one single algorithm which is well-suited for feasible MIMO implementations in all settings is not easy.

    This thesis addresses the general MIMO detection problem in two ways. One part treats a development of new and more efficient detection techniques for the different MIMO settings. The techniques that are proposed in this thesis demonstrate unprecedented performance in many relevant cases. The other part revolves around utilizing already proposed detection algorithms and their advantages versus disadvantages in an adaptive manner. For well-conditioned channels, low-complexity detection methods are often sufficiently accurate. In such cases, performing computationally very expensive optimal detection would be a waste of computational power. This said, for MIMO detection in a coded system, there is always a trade-off between performance and complexity. Intuitively, computational resources should be utilized more efficiently by performing optimal detection only when it is needed, and something simpler when it is not. However, it is not clear whether this is true or not. In trying to answer this, a general framework for adaptive computational-resource allocation to different (“simple” and “difficult”) detection problems is proposed. This general framework is applicable to any MIMO detector and scenario of choice, and it is exemplified using one particular detection method for which specific allocation techniques are developed and evaluated.

    List of papers
    1. Allocation of Computational Resources for Soft MIMO Detection
    Open this publication in new window or tab >>Allocation of Computational Resources for Soft MIMO Detection
    2011 (English)In: IEEE Journal of Selected Topics in Signal Processing, ISSN 1932-4553, Vol. 5, no 8, p. 1451-1461Article in journal (Refereed) Published
    Abstract [en]

    We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem with a well-defined objective function and constraints, and algorithms that solve this optimization problem efficiently computationally.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2011
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-69612 (URN)10.1109/JSTSP.2011.2162719 (DOI)000297348500006 ()
    Note
    ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Mirsad Čirkić, Daniel Persson and Erik G. Larsson, Allocation of Computational Resources for Soft MIMO Detection, 2011, accepted IEEE Journal of Selected Topics in Signal Processing Available from: 2011-07-06 Created: 2011-07-06 Last updated: 2016-08-31
    2. Approximating the LLR Distribution for a Class of Soft-Output MIMO Detectors
    Open this publication in new window or tab >>Approximating the LLR Distribution for a Class of Soft-Output MIMO Detectors
    2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 12, p. 6421-6434Article in journal (Refereed) Published
    Abstract [en]

    We present approximations of the LLR distribution for a class of fixed-complexity soft-output MIMO detectors, such as the optimal soft detector and the soft-output via partial marginalization detector. More specifically, in a MIMO AWGN setting, we approximate the LLR distribution conditioned on the transmitted signal and the channel matrix with a Gaussian mixture model (GMM). Our main results consist of an analytical expression of the GMM model (including the number of modes and their corresponding parameters) and a proof that, in the limit of high SNR, this LLR distribution converges in probability towards a unique Gaussian distribution.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2012
    Keywords
    Fixed-complexity sphere-decoder; Gaussian mixture model; LLR distribution; MIMO detection; partial marginalization
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-87205 (URN)10.1109/TSP.2012.2217336 (DOI)000311805000024 ()
    Note

    On the defence date of the Licentiate Thesis the status of this article was Manuscript and the title was Approximating the LLR Distribution for the Optimal and Partial Marginalization MIMO Detectors.

    Available from: 2013-01-14 Created: 2013-01-14 Last updated: 2017-12-06Bibliographically approved
    3. SUMIS: Near-Optimal Soft-In Soft-Out MIMO Detection with Low and Fixed Complexity
    Open this publication in new window or tab >>SUMIS: Near-Optimal Soft-In Soft-Out MIMO Detection with Low and Fixed Complexity
    2014 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 12, p. 3084-3097Article in journal (Refereed) Published
    Abstract [en]

    The fundamental problem of interest here is soft-input soft-output multiple-input multiple-output (MIMO) detection. We propose a method, referred to as subspace marginalization with interference suppression (SUMIS), that yields unprecedented performance at low and fixed (deterministic) complexity. Our method provides a well-defined tradeoff between computational complexity and performance. Apart from an initial sorting step consisting of selecting channel-matrix columns, the algorithm involves no searching nor algorithmic branching; hence the algorithm has a completely predictable run-time and allows for a highly parallel implementation. We numerically assess the performance of SUMIS in different practical settings: full/partial channel state information, sequential/iterative decoding, and low/high rate outer codes. We also comment on how the SUMIS method performs in systems with a large number of transmit antennas.

    Place, publisher, year, edition, pages
    IEEE Signal Processing Society, 2014
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-103671 (URN)10.1109/TSP.2014.2303945 (DOI)000338122400005 ()
    Available from: 2014-01-22 Created: 2014-01-22 Last updated: 2017-12-06
    4. On the Complexity of Very Large Multi-User MIMO Detection
    Open this publication in new window or tab >>On the Complexity of Very Large Multi-User MIMO Detection
    2014 (English)In: 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE, IEEE Press, 2014, p. 55-59Conference paper, Published paper (Refereed)
    Abstract [en]

    This work discusses efficient techniques for detection in large-size multi-user multiple-input multiple-output (MIMO) systems that are highly overdetermined. We exemplify the application of conjugate gradient methods in the setup of our interest and compare its performance with respect to methods based on the Neumann series expansion. We bring to light some important insights on the performance versus complexity tradeoffs that have not been uplifted before.

    Place, publisher, year, edition, pages
    IEEE Press, 2014
    Series
    IEEE International Workshop on Signal Processing Advances in Wireless Communications, ISSN 2325-3789
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-103672 (URN)10.1109/SPAWC.2014.6941316 (DOI)000348859000012 ()978-1-4799-4903-8 (ISBN)
    Conference
    IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
    Available from: 2014-01-22 Created: 2014-01-22 Last updated: 2016-09-13Bibliographically approved
  • 4.
    Čirkić, Mirsad
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Optimization of Computational Resources for MIMO Detection2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    For the past decades, the demand in transferring large amounts of data rapidly and reliably has been increasing drastically. One of the more promising techniques that can provide the desired performance is the multiple-input multiple-output (MIMO) technology where multiple antennas are placed at both the transmitting and receiving side of the communication link. One major implementation difficulty of the MIMO technology is the signal separation (detection) problem at the receiving side of the MIMO link. This is due to the fact that the transmitted signals interfere with each other and that separating them can be very difficult if the MIMO channel conditions are not beneficial, i.e., the channel is not well-conditioned.

    For well-conditioned channels, low-complexity detection methods are often sufficiently accurate. In such cases, performing computationally very expensive optimal detection would be a waste of computational power. This said, for MIMO detection in a coded system, there is always a trade-off between performance and complexity. The fundamental question is, can we save computational resources by performing optimal detection only when it is needed, and something simpler when it is not? This is the question that this thesis aims to answer. In doing so, we present a general framework for adaptively allocating computational resources to different (“simple” and“difficult”) detection problems. This general framework is applicable to any MIMO detector and scenario of choice, and it is exemplified using one particular detection method for which specific allocation techniques are developed and evaluated.

    List of papers
    1. Allocation of Computational Resources for Soft MIMO Detection
    Open this publication in new window or tab >>Allocation of Computational Resources for Soft MIMO Detection
    2011 (English)In: IEEE Journal of Selected Topics in Signal Processing, ISSN 1932-4553, Vol. 5, no 8, p. 1451-1461Article in journal (Refereed) Published
    Abstract [en]

    We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem with a well-defined objective function and constraints, and algorithms that solve this optimization problem efficiently computationally.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2011
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-69612 (URN)10.1109/JSTSP.2011.2162719 (DOI)000297348500006 ()
    Note
    ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Mirsad Čirkić, Daniel Persson and Erik G. Larsson, Allocation of Computational Resources for Soft MIMO Detection, 2011, accepted IEEE Journal of Selected Topics in Signal Processing Available from: 2011-07-06 Created: 2011-07-06 Last updated: 2016-08-31
    2. Approximating the LLR Distribution for a Class of Soft-Output MIMO Detectors
    Open this publication in new window or tab >>Approximating the LLR Distribution for a Class of Soft-Output MIMO Detectors
    2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 12, p. 6421-6434Article in journal (Refereed) Published
    Abstract [en]

    We present approximations of the LLR distribution for a class of fixed-complexity soft-output MIMO detectors, such as the optimal soft detector and the soft-output via partial marginalization detector. More specifically, in a MIMO AWGN setting, we approximate the LLR distribution conditioned on the transmitted signal and the channel matrix with a Gaussian mixture model (GMM). Our main results consist of an analytical expression of the GMM model (including the number of modes and their corresponding parameters) and a proof that, in the limit of high SNR, this LLR distribution converges in probability towards a unique Gaussian distribution.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2012
    Keywords
    Fixed-complexity sphere-decoder; Gaussian mixture model; LLR distribution; MIMO detection; partial marginalization
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-87205 (URN)10.1109/TSP.2012.2217336 (DOI)000311805000024 ()
    Note

    On the defence date of the Licentiate Thesis the status of this article was Manuscript and the title was Approximating the LLR Distribution for the Optimal and Partial Marginalization MIMO Detectors.

    Available from: 2013-01-14 Created: 2013-01-14 Last updated: 2017-12-06Bibliographically approved
  • 5.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Near-Optimal Soft-Output Fixed-Complexity MIMO Detection via Subspace Marginalization and Interference Suppression2012In: 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE Signal Processing Society, 2012, , p. 4p. 2805-2808Conference paper (Refereed)
    Abstract [en]

    The fundamental problem of our interest here is soft MIMO detection. We propose a method that yields excellent performance, atlow and at fixed (deterministic) complexity. Our method provides a well-defined tradeoff between computational complexity and performance. Apart from an initial step consisting of selecting columns,the algorithm involves no searching nor algorithmic branching; hence the algorithm has a completely predictable run-time, and it is readily and massively parallelizable.

  • 6.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    On the Complexity of Very Large Multi-User MIMO Detection2014In: 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE, IEEE Press, 2014, p. 55-59Conference paper (Refereed)
    Abstract [en]

    This work discusses efficient techniques for detection in large-size multi-user multiple-input multiple-output (MIMO) systems that are highly overdetermined. We exemplify the application of conjugate gradient methods in the setup of our interest and compare its performance with respect to methods based on the Neumann series expansion. We bring to light some important insights on the performance versus complexity tradeoffs that have not been uplifted before.

  • 7.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    SUMIS: Near-Optimal Soft-In Soft-Out MIMO Detection with Low and Fixed Complexity2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 12, p. 3084-3097Article in journal (Refereed)
    Abstract [en]

    The fundamental problem of interest here is soft-input soft-output multiple-input multiple-output (MIMO) detection. We propose a method, referred to as subspace marginalization with interference suppression (SUMIS), that yields unprecedented performance at low and fixed (deterministic) complexity. Our method provides a well-defined tradeoff between computational complexity and performance. Apart from an initial sorting step consisting of selecting channel-matrix columns, the algorithm involves no searching nor algorithmic branching; hence the algorithm has a completely predictable run-time and allows for a highly parallel implementation. We numerically assess the performance of SUMIS in different practical settings: full/partial channel state information, sequential/iterative decoding, and low/high rate outer codes. We also comment on how the SUMIS method performs in systems with a large number of transmit antennas.

  • 8.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Allocation of Computational Resources for Soft MIMO Detection2011In: IEEE Journal of Selected Topics in Signal Processing, ISSN 1932-4553, Vol. 5, no 8, p. 1451-1461Article in journal (Refereed)
    Abstract [en]

    We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem with a well-defined objective function and constraints, and algorithms that solve this optimization problem efficiently computationally.

  • 9.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    New Results on Adaptive Computational Resource Allocation in Soft MIMO Detection2011In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE conference proceedings, 2011, p. 2972-2975Conference paper (Refereed)
    Abstract [en]

    The fundamental problem of our interest is soft MIMO detection for  the case of block fading, i.e., when the transmitted codeword spans  over several independent channel realizations. We develop methods  that adaptively allocate computational resources to the detection  problems of each channel realization, under a total per-codeword  complexity constraint. The new results consist of a new algorithm, a  new performance measure, and a thorough complexity discussion.

  • 10.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Optimization of Computational Resource Allocation for Soft MIMO Detection2009In: Proceedings of the 43rd Asilomar Conference on Signals, Systems, and Computers (ACSSC'09), IEEE , 2009, p. 1488-1492Conference paper (Refereed)
    Abstract [en]

    We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate the computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem and a greedy algorithm to approximate it in a computationally feasible fashion.

  • 11.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Jan-Åke
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    Gaussian Approximation of the LLR Distribution for the ML and Partial Marginalization MIMO detectors2011In: Proceedings of the IEEE International Conference on Acoustics, Speech and SignalProcessing (ICASSP), IEEE conference proceedings, 2011, p. 3232-3235Conference paper (Refereed)
    Abstract [en]

    We derive a Gaussian approximation of the LLR distribution  conditioned on the transmitted signal and the channel matrix for the  soft-output via partial marginalization MIMO detector. This detector  performs exact ML as a special case. Our main results consist of  discussing the operational meaning of this approximation and a proof  that, in the limit of high SNR, the LLR distribution of interest  converges in probability towards a Gaussian distribution.

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