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Optimization of Computational Resources for MIMO Detection
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
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: urn:nbn:se:liu:diva-72368ISBN: 978-91-7393-011-6 (print)OAI: oai:DiVA.org:liu-72368DiVA: diva2:459671
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
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, 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
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, 6421-6434 p.Article 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
Keyword
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

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Čirkić, Mirsad

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