Channel Estimation for Distributed Intelligent Reflecting Surfaces Assisted Multi-User MISO Systems

dc.conference.date7-11 Dec. 2020
dc.conference.locationTaipei, Taiwan
dc.conference.name2020 IEEE Globecom Workshops
dc.contributor.authorAlwazani, Hibatallah
dc.contributor.authorNadeem, Qurrat-Ul-Ain
dc.contributor.authorChaaban, Anas
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.institutionUniversity of British Columbia
dc.date.accessioned2021-03-28T11:28:49Z
dc.date.available2020-10-05T12:56:21Z
dc.date.available2021-03-28T11:28:49Z
dc.date.issued2020-12
dc.description.abstractIntelligent reflecting surfaces (IRSs)-assisted wireless communication promises improved system performance, while posing new challenges in channel estimation (CE) due to the passive nature of the reflecting elements. Although a few CE protocols for IRS-assisted multiple-input single-output (MISO) systems have appeared, they either require long channel training times or are developed under channel sparsity assumptions. Moreover, existing works focus on a single IRS, whereas in practice multiple such surfaces should be installed to truly benefit from the concept of reconfiguring propagation environments. In light of these challenges, this paper tackles the CE problem for the distributed IRSs-assisted multi-user MISO system. An optimal CE protocol requiring relatively low training overhead is developed using Bayesian techniques under the practical assumption that the BS-IRSs channels are dominated by the line-of-sight (LoS) components. An optimal solution for the phase shifts vectors required at all IRSs during CE is determined and the minimum mean square error (MMSE) estimates of the BSusers direct channels and the IRSs-users channels are derived. Simulation results corroborate the normalized MSE (NMSE) analysis and establish the advantage of the proposed protocol as compared to benchmark scheme in terms of training overhead.
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) under Award No. OSR-2018-CRG7-3734
dc.eprint.versionPre-print
dc.identifier.arxivid2009.10653
dc.identifier.citationAlwazani, H., Nadeem, Q.-U.-A., & Chaaban, A. (2020). Channel Estimation for Distributed Intelligent Reflecting Surfaces Assisted Multi-User MISO Systems. 2020 IEEE Globecom Workshops (GC Wkshps. doi:10.1109/gcwkshps50303.2020.9367461
dc.identifier.doi10.1109/gcwkshps50303.2020.9367461
dc.identifier.isbn9781728173078
dc.identifier.urihttp://hdl.handle.net/10754/665448
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9367461/
dc.rightsArchived with thanks to IEEE
dc.titleChannel Estimation for Distributed Intelligent Reflecting Surfaces Assisted Multi-User MISO Systems
dc.typeConference Paper
display.details.left<span><h5>Type</h5>Conference Paper<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Alwazani, Hibatallah,equals">Alwazani, Hibatallah</a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0001-8423-3482&spc.sf=dc.date.issued&spc.sd=DESC">Nadeem, Qurrat-Ul-Ain</a> <a href="https://orcid.org/0000-0001-8423-3482" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0002-8713-5084&spc.sf=dc.date.issued&spc.sd=DESC">Chaaban, Anas</a> <a href="https://orcid.org/0000-0002-8713-5084" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><br><h5>KAUST Department</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division,equals">Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Electrical Engineering Program,equals">Electrical Engineering Program</a><br><br><h5>KAUST Grant Number</h5>OSR-2018-CRG7-3734<br><br><h5>Date</h5>2020-12</span>
display.details.right<span><h5>Abstract</h5>Intelligent reflecting surfaces (IRSs)-assisted wireless communication promises improved system performance, while posing new challenges in channel estimation (CE) due to the passive nature of the reflecting elements. Although a few CE protocols for IRS-assisted multiple-input single-output (MISO) systems have appeared, they either require long channel training times or are developed under channel sparsity assumptions. Moreover, existing works focus on a single IRS, whereas in practice multiple such surfaces should be installed to truly benefit from the concept of reconfiguring propagation environments. In light of these challenges, this paper tackles the CE problem for the distributed IRSs-assisted multi-user MISO system. An optimal CE protocol requiring relatively low training overhead is developed using Bayesian techniques under the practical assumption that the BS-IRSs channels are dominated by the line-of-sight (LoS) components. An optimal solution for the phase shifts vectors required at all IRSs during CE is determined and the minimum mean square error (MMSE) estimates of the BSusers direct channels and the IRSs-users channels are derived. Simulation results corroborate the normalized MSE (NMSE) analysis and establish the advantage of the proposed protocol as compared to benchmark scheme in terms of training overhead.<br><br><h5>Citation</h5>Alwazani, H., Nadeem, Q.-U.-A., & Chaaban, A. (2020). Channel Estimation for Distributed Intelligent Reflecting Surfaces Assisted Multi-User MISO Systems. 2020 IEEE Globecom Workshops (GC Wkshps. doi:10.1109/gcwkshps50303.2020.9367461<br><br><h5>Acknowledgements</h5>This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) under Award No. OSR-2018-CRG7-3734<br><br><h5>Publisher</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.publisher=Institute of Electrical and Electronics Engineers (IEEE),equals">Institute of Electrical and Electronics Engineers (IEEE)</a><br><br><h5>Conference/Event Name</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.conference=2020 IEEE Globecom Workshops,equals">2020 IEEE Globecom Workshops</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1109/gcwkshps50303.2020.9367461">10.1109/gcwkshps50303.2020.9367461</a><br><br><h5>arXiv</h5><a href="https://arxiv.org/abs/2009.10653">2009.10653</a><br><br><h5>Additional Links</h5>https://ieeexplore.ieee.org/document/9367461/</span>
kaust.acknowledged.supportUnitCRG
kaust.acknowledged.supportUnitOSR
kaust.grant.numberOSR-2018-CRG7-3734
kaust.personAlwazani, Hibatallah
kaust.personNadeem, Qurrat-Ul-Ain
kaust.personChaaban, Anas
orcid.authorAlwazani, Hibatallah
orcid.authorNadeem, Qurrat-Ul-Ain::0000-0001-8423-3482
orcid.authorChaaban, Anas::0000-0002-8713-5084
orcid.id0000-0002-8713-5084
orcid.id0000-0001-8423-3482
refterms.dateFOA2020-10-05T12:56:47Z
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