Distributive estimation of frequency selective channels for massive MIMO systems

Handle URI:
http://hdl.handle.net/10754/621361
Title:
Distributive estimation of frequency selective channels for massive MIMO systems
Authors:
Zaib, Alam; Masood, Mudassir ( 0000-0003-0462-7874 ) ; Ghogho, Mounir; Al-Naffouri, Tareq Y.
Abstract:
We consider frequency selective channel estimation in the uplink of massive MIMO-OFDM systems, where our major concern is complexity. A low complexity distributed LMMSE algorithm is proposed that attains near optimal channel impulse response (CIR) estimates from noisy observations at receive antenna array. In proposed method, every antenna estimates the CIRs of its neighborhood followed by recursive sharing of estimates with immediate neighbors. At each step, every antenna calculates the weighted average of shared estimates which converges to near optimal LMMSE solution. The simulation results validate the near optimal performance of proposed algorithm in terms of mean square error (MSE). © 2015 EURASIP.
KAUST Department:
Electrical Engineering Program
Citation:
Zaib A, Masood M, Ghogho M, Al-Naffouri TY (2015) Distributive estimation of frequency selective channels for massive MIMO systems. 2015 23rd European Signal Processing Conference (EUSIPCO). Available: http://dx.doi.org/10.1109/eusipco.2015.7362511.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2015 23rd European Signal Processing Conference (EUSIPCO)
Conference/Event name:
23rd European Signal Processing Conference, EUSIPCO 2015
Issue Date:
28-Dec-2015
DOI:
10.1109/eusipco.2015.7362511
Type:
Conference Paper
Sponsors:
KAUST, King Abdullah University of Science and Technology
Appears in Collections:
Conference Papers; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorZaib, Alamen
dc.contributor.authorMasood, Mudassiren
dc.contributor.authorGhogho, Mouniren
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.date.accessioned2016-11-03T06:58:31Z-
dc.date.available2016-11-03T06:58:31Z-
dc.date.issued2015-12-28en
dc.identifier.citationZaib A, Masood M, Ghogho M, Al-Naffouri TY (2015) Distributive estimation of frequency selective channels for massive MIMO systems. 2015 23rd European Signal Processing Conference (EUSIPCO). Available: http://dx.doi.org/10.1109/eusipco.2015.7362511.en
dc.identifier.doi10.1109/eusipco.2015.7362511en
dc.identifier.urihttp://hdl.handle.net/10754/621361-
dc.description.abstractWe consider frequency selective channel estimation in the uplink of massive MIMO-OFDM systems, where our major concern is complexity. A low complexity distributed LMMSE algorithm is proposed that attains near optimal channel impulse response (CIR) estimates from noisy observations at receive antenna array. In proposed method, every antenna estimates the CIRs of its neighborhood followed by recursive sharing of estimates with immediate neighbors. At each step, every antenna calculates the weighted average of shared estimates which converges to near optimal LMMSE solution. The simulation results validate the near optimal performance of proposed algorithm in terms of mean square error (MSE). © 2015 EURASIP.en
dc.description.sponsorshipKAUST, King Abdullah University of Science and Technologyen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectChannel estimationen
dc.subjectdistributed estimationen
dc.subjectLeast squaresen
dc.subjectLMMSEen
dc.subjectmassive MIMOen
dc.titleDistributive estimation of frequency selective channels for massive MIMO systemsen
dc.typeConference Paperen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journal2015 23rd European Signal Processing Conference (EUSIPCO)en
dc.conference.date31 August 2015 through 4 September 2015en
dc.conference.name23rd European Signal Processing Conference, EUSIPCO 2015en
dc.contributor.institutionElectrical Engineering, Department King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabiaen
dc.contributor.institutionSchool of Electronics and Electrical Engineering, University of Leeds, United Kingdomen
kaust.authorMasood, Mudassiren
kaust.authorAl-Naffouri, Tareq Y.en
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