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Allocation of Computational Resources for Soft MIMO Detection
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-7599-4367
2011 (English)In: IEEE Journal of Selected Topics in Signal Processing, ISSN 1932-4553, Vol. 5, no 8, 1451-1461 p.Article 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. Vol. 5, no 8, 1451-1461 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-69612DOI: 10.1109/JSTSP.2011.2162719ISI: 000297348500006OAI: oai:DiVA.org:liu-69612DiVA: diva2:429946
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
In thesis
1. Optimization of Computational Resources for MIMO Detection
Open this publication in new window or tab >>Optimization of Computational Resources for MIMO Detection
2011 (English)Licentiate 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 26 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1514
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-72368 (URN)978-91-7393-011-6 (ISBN)
Presentation
2011-12-19, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2011-11-28 Created: 2011-11-28 Last updated: 2016-08-31Bibliographically approved
2. Efficient MIMO Detection Methods
Open this publication in new window or tab >>Efficient MIMO Detection Methods
2014 (English)Doctoral 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 43 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1570
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-103675 (URN)10.3384/diss.diva-103675 (DOI)978-91-7519-413-4 (ISBN)
Public defence
2014-02-21, Visionen, Hus B (ing°ang 27), Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2014-01-22 Created: 2014-01-22 Last updated: 2016-08-31Bibliographically approved

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Čirkić, MirsadPersson, DanielLarsson, Erik G.

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