Power efficient low complexity precoding for massive MIMO systems

Handle URI:
http://hdl.handle.net/10754/565855
Title:
Power efficient low complexity precoding for massive MIMO systems
Authors:
Sifaou, Houssem ( 0000-0003-0630-7073 ) ; Kammoun, Abla ( 0000-0002-0195-3159 ) ; Sanguinetti, Luca; Debbah, Merouane; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 )
Abstract:
This work aims at designing a low-complexity precoding technique in the downlink of a large-scale multiple-input multiple-output (MIMO) system in which the base station (BS) is equipped with M antennas to serve K single-antenna user equipments. This is motivated by the high computational complexity required by the widely used zero-forcing or regularized zero-forcing precoding techniques, especially when K grows large. To reduce the computational burden, we adopt a precoding technique based on truncated polynomial expansion (TPE) and make use of the asymptotic analysis to compute the deterministic equivalents of its corresponding signal-to-interference-plus-noise ratios (SINRs) and transmit power. The asymptotic analysis is conducted in the regime in which M and K tend to infinity with the same pace under the assumption that imperfect channel state information is available at the BS. The results are then used to compute the TPE weights that minimize the asymptotic transmit power while meeting a set of target SINR constraints. Numerical simulations are used to validate the theoretical analysis. © 2014 IEEE.
KAUST Department:
Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Communication Theory Lab
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Conference/Event name:
2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Issue Date:
Dec-2014
DOI:
10.1109/GlobalSIP.2014.7032197
Type:
Conference Paper
Appears in Collections:
Conference Papers; Electrical Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSifaou, Houssemen
dc.contributor.authorKammoun, Ablaen
dc.contributor.authorSanguinetti, Lucaen
dc.contributor.authorDebbah, Merouaneen
dc.contributor.authorAlouini, Mohamed-Slimen
dc.date.accessioned2015-08-11T13:43:35Zen
dc.date.available2015-08-11T13:43:35Zen
dc.date.issued2014-12en
dc.identifier.doi10.1109/GlobalSIP.2014.7032197en
dc.identifier.urihttp://hdl.handle.net/10754/565855en
dc.description.abstractThis work aims at designing a low-complexity precoding technique in the downlink of a large-scale multiple-input multiple-output (MIMO) system in which the base station (BS) is equipped with M antennas to serve K single-antenna user equipments. This is motivated by the high computational complexity required by the widely used zero-forcing or regularized zero-forcing precoding techniques, especially when K grows large. To reduce the computational burden, we adopt a precoding technique based on truncated polynomial expansion (TPE) and make use of the asymptotic analysis to compute the deterministic equivalents of its corresponding signal-to-interference-plus-noise ratios (SINRs) and transmit power. The asymptotic analysis is conducted in the regime in which M and K tend to infinity with the same pace under the assumption that imperfect channel state information is available at the BS. The results are then used to compute the TPE weights that minimize the asymptotic transmit power while meeting a set of target SINR constraints. Numerical simulations are used to validate the theoretical analysis. © 2014 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titlePower efficient low complexity precoding for massive MIMO systemsen
dc.typeConference Paperen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentCommunication Theory Laben
dc.identifier.journal2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)en
dc.conference.date3 December 2014 through 5 December 2014en
dc.conference.name2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014en
kaust.authorKammoun, Ablaen
kaust.authorAlouini, Mohamed-Slimen
kaust.authorSifaou, Houssemen
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