Enhancing the Accuracy of CSI-Based Positioning in Massive MIMO Systems

refereedrefRefereedRefereegranskatFagfellevurdertconferencePaperkonConference paperKonferensbidragKonferansepaperproceedingpublishedPaperNadaAliDoctoral student1983-autHögskolan i Halmstad, Akademin för informationsteknologiCentre for Research on Embedded Systems (CERES)orcid.org=0009-0007-6933-1608diva-portal.org=authority-person:108158Centre for Research on Embedded Systems (CERES)othResearch GroupAliHazemautHögskolan i Halmstad, Akademin för informationsteknologiCentre for Research on Embedded Systems (CERES)orcid.org=0000-0003-1342-4227diva-portal.org=authority-person:108579Centre for Research on Embedded Systems (CERES)othResearch GroupLiuLiang1983-autDepartment of Electrical and Information Technology, Lund University, Lund, SwedenAlkabaniYousra1981-autHögskolan i Halmstad, Akademin för informationsteknologiorcid.org=0000-0001-8806-8146diva-portal.org=authority-person:79353Högskolan i HalmstadAkademin för informationsteknologipblEnhancing the Accuracy of CSI-Based Positioning in Massive MIMO SystemsengIEEE20232023-11-13T14:59:00979-8-3503-3782-2979-8-3503-3783-9https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-5198610.1109/BlackSeaCom58138.2023.10299742textConvolutional Neural NetworksPruningBatch NormalizationPositioning AccuracyModel SizeEngineering and TechnologyElectrical Engineering, Electronic Engineering, Information EngineeringCommunication SystemsTeknik och teknologierElektroteknik och elektronikKommunikationssystemNatural SciencesComputer and Information SciencesComputer SciencesNaturvetenskapData- och informationsvetenskapDatavetenskap (datalogi)<p>Massive Multiple-Input Multiple-Output (MIMO) communication systems are being investigated intensively for positioning services. Enhancing the accuracy on these services in terms of accurate positioning of users is an important goal to improve related applications in the future. Convolutional Neural Networks (CNNs) has been proposed to infer the position of a user from Channel State Information (CSI) of a massive MIMO system. This paper investigates different architectures of CNNs to enhance the accuracy of a fingerprint-based positionina system. Three new CNNs has been proposed in which the Convolutional Layer (CL) and the Fully Connected (FC) layer are re-dimensioned. Batch Normalization (BN) layer is introduced to the layer structure of the newly proposed CNNs. The CNNs were trained, and accordingly mean error is measured. The first re-constructed CNN composed of 13 CLs, 7 BNs, and 3 FC layers has achieved the best accuracy out of the three models. It managed to achieve a mean error of 10.09 mm, that outperforms a similar work by 82 % in terms of positioning accuracy. Pruning was added to the layer structure of the newly proposed CNN s. It reduced the model size significantly, approximately by 65 % compared to a similar model of previous work.</p>2023 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)90952023 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Istanbul, Turkey, July 4-7, 2023ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications [B02]alinadhh2023-11-132025-02-20diva2:1811549

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Ali Nada

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( School of Information Technology )

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Enhancing the Accuracy of CSI-Based Positioning in Massive MIMO Systems