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  • Tian, GuodaLund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,LTH profilområde: AI och digitalisering,LTH profilområden,Lunds Tekniska Högskola,Communications Engineering,Lund University Research Groups,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH (author)

Deep-Learning Based High-Precision Localization with Massive MIMO

  • Article/chapterEnglish2023

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  • 2023
  • electronicrdacarrier

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  • LIBRIS-ID:oai:lup.lub.lu.se:759e2ecb-179a-4dec-84f6-7d1834d38707
  • https://lup.lub.lu.se/record/759e2ecb-179a-4dec-84f6-7d1834d38707URI

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  • Language:English
  • Summary in:English

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  • High-precision localization and machine learning (ML) are envisioned to be key technologies in future wireless systems. This paper presents an ML pipeline to solve localization tasks. It consists of multiple parallel processing chains, each trained using a different fingerprint to estimate the position of the user equipment. In this way, ensemble learning can be utilized to fuse all chains to improve localization performance. Nevertheless, a common problem of ML-based techniques is that network training and fine-tuning can be challenging due to the increase in network sizes when applied to (massive) multiple-input multiple-output (MIMO) systems. To address this issue, we utilize a subarray-based approach. We divide the large antenna array into several subarrays, feeding the fingerprints of the subarrays into the pipeline. In our case, such an approach eases the training process while maintaining or even enhancing the performance. We also use the Nyquist sampling theorem to gain insight on how to appropriately sample and average training data. Finally, an indoor measurement campaign is conducted at 3.7 GHz using the Lund University massive MIMO testbed to evaluate the approaches. Localization accuracy at a centimeter level has been reached in this particular measurement campaign.

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  • Yaman, IlaydaLund University,Lunds universitet,Integrerade elektroniksystem,Forskargrupper vid Lunds universitet,Integrated Electronic Systems,Lund University Research Groups(Swepub:lu)il0428ya (author)
  • Sandra, MichielLund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,Communications Engineering,Lund University Research Groups(Swepub:lu)mi3168sa (author)
  • Cai, XuesongLund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,Communications Engineering,Lund University Research Groups(Swepub:lu)xu2214ca (author)
  • Liu, LiangLund University,Lunds universitet,System på chips (master),Utbildningsprogram, LTH,Lunds Tekniska Högskola,Integrerade elektroniksystem,Forskargrupper vid Lunds universitet,LTH profilområde: Nanovetenskap och halvledarteknologi,LTH profilområden,LTH profilområde: AI och digitalisering,Embedded Electronics Engineering (M.Sc.),Educational programmes, LTH,Faculty of Engineering, LTH,Integrated Electronic Systems,Lund University Research Groups,LTH Profile Area: Nanoscience and Semiconductor Technology,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,Faculty of Engineering, LTH(Swepub:lu)eit-lli (author)
  • Tufvesson, FredrikLund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,LTH profilområde: AI och digitalisering,LTH profilområden,Lunds Tekniska Högskola,LU profilområde: Naturlig och artificiell kognition,Lunds universitets profilområden,Communications Engineering,Lund University Research Groups,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LU Profile Area: Natural and Artificial Cognition,Lund University Profile areas(Swepub:lu)es-ftu (author)
  • KommunikationsteknologiForskargrupper vid Lunds universitet (creator_code:org_t)

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  • In:IEEE Transactions on Machine Learning in Communications and Networking2831-316X

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Tian, Guoda
Yaman, Ilayda
Sandra, Michiel
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Tufvesson, Fredr ...
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