Deep-Learning-Based High-Precision Localization With Massive MIMO
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 posit...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10330061/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850035987291308032 |
|---|---|
| author | Guoda Tian Ilayda Yaman Michiel Sandra Xuesong Cai Liang Liu Fredrik Tufvesson |
| author_facet | Guoda Tian Ilayda Yaman Michiel Sandra Xuesong Cai Liang Liu Fredrik Tufvesson |
| author_sort | Guoda Tian |
| collection | DOAJ |
| description | 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 3.7GHz 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. |
| format | Article |
| id | doaj-art-5fa1b09779f04d2eba92888a72e851a3 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-5fa1b09779f04d2eba92888a72e851a32025-08-20T02:57:19ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-012193310.1109/TMLCN.2023.333471210330061Deep-Learning-Based High-Precision Localization With Massive MIMOGuoda Tian0https://orcid.org/0000-0003-2466-4621Ilayda Yaman1https://orcid.org/0000-0002-2416-2077Michiel Sandra2Xuesong Cai3https://orcid.org/0000-0001-7759-7448Liang Liu4https://orcid.org/0000-0001-9491-8821Fredrik Tufvesson5https://orcid.org/0000-0003-1072-0784Department of Electrical and Information Technology, Lund University, Lund, SwedenDepartment of Electrical and Information Technology, Lund University, Lund, SwedenDepartment of Electrical and Information Technology, Lund University, Lund, SwedenDepartment of Electrical and Information Technology, Lund University, Lund, SwedenDepartment of Electrical and Information Technology, Lund University, Lund, SwedenDepartment of Electrical and Information Technology, Lund University, Lund, SwedenHigh-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 3.7GHz 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.https://ieeexplore.ieee.org/document/10330061/Channel measurementsdeep learninglocalizationmassive MIMO |
| spellingShingle | Guoda Tian Ilayda Yaman Michiel Sandra Xuesong Cai Liang Liu Fredrik Tufvesson Deep-Learning-Based High-Precision Localization With Massive MIMO IEEE Transactions on Machine Learning in Communications and Networking Channel measurements deep learning localization massive MIMO |
| title | Deep-Learning-Based High-Precision Localization With Massive MIMO |
| title_full | Deep-Learning-Based High-Precision Localization With Massive MIMO |
| title_fullStr | Deep-Learning-Based High-Precision Localization With Massive MIMO |
| title_full_unstemmed | Deep-Learning-Based High-Precision Localization With Massive MIMO |
| title_short | Deep-Learning-Based High-Precision Localization With Massive MIMO |
| title_sort | deep learning based high precision localization with massive mimo |
| topic | Channel measurements deep learning localization massive MIMO |
| url | https://ieeexplore.ieee.org/document/10330061/ |
| work_keys_str_mv | AT guodatian deeplearningbasedhighprecisionlocalizationwithmassivemimo AT ilaydayaman deeplearningbasedhighprecisionlocalizationwithmassivemimo AT michielsandra deeplearningbasedhighprecisionlocalizationwithmassivemimo AT xuesongcai deeplearningbasedhighprecisionlocalizationwithmassivemimo AT liangliu deeplearningbasedhighprecisionlocalizationwithmassivemimo AT fredriktufvesson deeplearningbasedhighprecisionlocalizationwithmassivemimo |