Linear precoding based on polynomial expansion: reducing complexity in massive MIMO

Handle URI:
http://hdl.handle.net/10754/601296
Title:
Linear precoding based on polynomial expansion: reducing complexity in massive MIMO
Authors:
Mueller, Axel; Kammoun, Abla ( 0000-0002-0195-3159 ) ; Björnson, Emil; Debbah, Mérouane
Abstract:
Massive multiple-input multiple-output (MIMO) techniques have the potential to bring tremendous improvements in spectral efficiency to future communication systems. Counterintuitively, the practical issues of having uncertain channel knowledge, high propagation losses, and implementing optimal non-linear precoding are solved more or less automatically by enlarging system dimensions. However, the computational precoding complexity grows with the system dimensions. For example, the close-to-optimal and relatively “antenna-efficient” regularized zero-forcing (RZF) precoding is very complicated to implement in practice, since it requires fast inversions of large matrices in every coherence period. Motivated by the high performance of RZF, we propose to replace the matrix inversion and multiplication by a truncated polynomial expansion (TPE), thereby obtaining the new TPE precoding scheme which is more suitable for real-time hardware implementation and significantly reduces the delay to the first transmitted symbol. The degree of the matrix polynomial can be adapted to the available hardware resources and enables smooth transition between simple maximum ratio transmission and more advanced RZF. By deriving new random matrix results, we obtain a deterministic expression for the asymptotic signal-to-interference-and-noise ratio (SINR) achieved by TPE precoding in massive MIMO systems. Furthermore, we provide a closed-form expression for the polynomial coefficients that maximizes this SINR. To maintain a fixed per-user rate loss as compared to RZF, the polynomial degree does not need to scale with the system, but it should be increased with the quality of the channel knowledge and the signal-to-noise ratio.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Linear precoding based on polynomial expansion: reducing complexity in massive MIMO 2016, 2016 (1) EURASIP Journal on Wireless Communications and Networking
Publisher:
Springer Science + Business Media
Journal:
EURASIP Journal on Wireless Communications and Networking
Issue Date:
29-Feb-2016
DOI:
10.1186/s13638-016-0546-z
Type:
Article
ISSN:
1687-1499
Sponsors:
This research has been supported by the ERC Starting Grant 305123 MORE (Advanced Mathematical Tools for Complex Network Engineering). Parts of the results were previously presented at the 8th IEEE Sensor Array and Multichannel Signal Processing Workshop, 2014. E. Björnson is funded by the International Postdoc Grant 2012-228 from the Swedish Research Council.
Additional Links:
http://jwcn.eurasipjournals.com/content/2016/1/63
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMueller, Axelen
dc.contributor.authorKammoun, Ablaen
dc.contributor.authorBjörnson, Emilen
dc.contributor.authorDebbah, Mérouaneen
dc.date.accessioned2016-03-13T13:59:36Zen
dc.date.available2016-03-13T13:59:36Zen
dc.date.issued2016-02-29en
dc.identifier.citationLinear precoding based on polynomial expansion: reducing complexity in massive MIMO 2016, 2016 (1) EURASIP Journal on Wireless Communications and Networkingen
dc.identifier.issn1687-1499en
dc.identifier.doi10.1186/s13638-016-0546-zen
dc.identifier.urihttp://hdl.handle.net/10754/601296en
dc.description.abstractMassive multiple-input multiple-output (MIMO) techniques have the potential to bring tremendous improvements in spectral efficiency to future communication systems. Counterintuitively, the practical issues of having uncertain channel knowledge, high propagation losses, and implementing optimal non-linear precoding are solved more or less automatically by enlarging system dimensions. However, the computational precoding complexity grows with the system dimensions. For example, the close-to-optimal and relatively “antenna-efficient” regularized zero-forcing (RZF) precoding is very complicated to implement in practice, since it requires fast inversions of large matrices in every coherence period. Motivated by the high performance of RZF, we propose to replace the matrix inversion and multiplication by a truncated polynomial expansion (TPE), thereby obtaining the new TPE precoding scheme which is more suitable for real-time hardware implementation and significantly reduces the delay to the first transmitted symbol. The degree of the matrix polynomial can be adapted to the available hardware resources and enables smooth transition between simple maximum ratio transmission and more advanced RZF. By deriving new random matrix results, we obtain a deterministic expression for the asymptotic signal-to-interference-and-noise ratio (SINR) achieved by TPE precoding in massive MIMO systems. Furthermore, we provide a closed-form expression for the polynomial coefficients that maximizes this SINR. To maintain a fixed per-user rate loss as compared to RZF, the polynomial degree does not need to scale with the system, but it should be increased with the quality of the channel knowledge and the signal-to-noise ratio.en
dc.description.sponsorshipThis research has been supported by the ERC Starting Grant 305123 MORE (Advanced Mathematical Tools for Complex Network Engineering). Parts of the results were previously presented at the 8th IEEE Sensor Array and Multichannel Signal Processing Workshop, 2014. E. Björnson is funded by the International Postdoc Grant 2012-228 from the Swedish Research Council.en
dc.language.isoenen
dc.publisherSpringer Science + Business Mediaen
dc.relation.urlhttp://jwcn.eurasipjournals.com/content/2016/1/63en
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.subjectMassive MIMOen
dc.subjectLinear precodingen
dc.subjectMultiuser systemsen
dc.subjectPolynomial expansionen
dc.subjectRandom matrix theoryen
dc.titleLinear precoding based on polynomial expansion: reducing complexity in massive MIMOen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalEURASIP Journal on Wireless Communications and Networkingen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionMathematical and Algorithmic Sciences Lab, France Research Center, Huawei Technologies Co. Ltd., Arcs de Seine Bâtiment A, 20 Quai du Point du Jour, 92100 Boulogne-Billancourt, Franceen
dc.contributor.institutionAlcatel-Lucent on Flexible Radio, SUPELEC, Plateau de Moulon, 3 Rue Joliot Curie, 91190 Gif-sur-Yvette, Franceen
dc.contributor.institutionLinköping University, Department of Electrical Engineering, SE-581 83 Linköping, Swedenen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorKammoun, Ablaen
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