Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10998
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dc.contributor.authorChen, S.-
dc.contributor.authorZhang, J.-
dc.contributor.authorBjornson, E.-
dc.contributor.authorDemir, O.T.-
dc.contributor.authorAi, B.-
dc.date.accessioned2024-01-21T09:24:31Z-
dc.date.available2024-01-21T09:24:31Z-
dc.date.issued2023-
dc.identifier.issn1536-1276-
dc.identifier.urihttps://doi.org/10.1109/TWC.2023.3270299-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10998-
dc.description.abstractCell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-Term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. © 2002-2012 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Transactions on Wireless Communicationsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCell-free massive MIMO; distributed processing; energy efficiency; large-scale fading; sparse optimizationen_US
dc.subjectElectric power utilization; Energy efficiency; Mean square error; Optimization; Signal processing; Spectrum efficiency; Cell-free; Cell-free massive MIMO; Distributed database; Distributed processing; Downlink; Large-scale fading; Large-scales; Power demands; Signal-processing; Sparse optimizations; Task analysis; Uplink; Wireless communications; MIMO systemsen_US
dc.titleEnergy-Efficient Cell-Free Massive Mimo Through Sparse Large-Scale Fading Processingen_US
dc.typeArticleen_US
dc.departmentTOBB ETÜen_US
dc.identifier.volume22en_US
dc.identifier.issue12en_US
dc.identifier.startpage9374en_US
dc.identifier.endpage9389en_US
dc.identifier.wosWOS:001128031700038en_US
dc.identifier.scopus2-s2.0-85159797448en_US
dc.institutionauthor-
dc.identifier.doi10.1109/TWC.2023.3270299-
dc.authorscopusid57202302382-
dc.authorscopusid57202148166-
dc.authorscopusid24478602800-
dc.authorscopusid55807906700-
dc.authorscopusid7005327119-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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