Active learning for efficient data selection in radio‐signal‐based positioning via deep learning
Abstract The problem of user equipment positioning based on radio signals is considered via deep learning. As in most supervised‐learning tasks, a critical aspect is the availability of a relevant dataset to train a model. However, in a cellular network, the data‐collection step may induce a high co...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Electronics Letters |
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| Online Access: | https://doi.org/10.1049/ell2.70040 |
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| _version_ | 1850266047316230144 |
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| author | Vincent Corlay Milan Courcoux‐Caro |
| author_facet | Vincent Corlay Milan Courcoux‐Caro |
| author_sort | Vincent Corlay |
| collection | DOAJ |
| description | Abstract The problem of user equipment positioning based on radio signals is considered via deep learning. As in most supervised‐learning tasks, a critical aspect is the availability of a relevant dataset to train a model. However, in a cellular network, the data‐collection step may induce a high communication overhead. As a result, to reduce the required size of the dataset, it may be interesting to carefully choose the positions to be labelled and to be used in the training. Therefore, an active learning approach for efficient data collection is proposed. It is first shown that significant gains (both in terms of positioning accuracy and size of the required dataset) can be obtained for the considered positioning problem using a genie. This validates the interest of active learning for positioning. Then, a practical method is proposed to approximate this genie. |
| format | Article |
| id | doaj-art-65b754fc86ae43eca8baeec24ffd797b |
| institution | OA Journals |
| issn | 0013-5194 1350-911X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj-art-65b754fc86ae43eca8baeec24ffd797b2025-08-20T01:54:16ZengWileyElectronics Letters0013-51941350-911X2024-10-016020n/an/a10.1049/ell2.70040Active learning for efficient data selection in radio‐signal‐based positioning via deep learningVincent Corlay0Milan Courcoux‐Caro1Mitsubishi Electric Research and Development Centre Europe Rennes FranceMitsubishi Electric Research and Development Centre Europe Rennes FranceAbstract The problem of user equipment positioning based on radio signals is considered via deep learning. As in most supervised‐learning tasks, a critical aspect is the availability of a relevant dataset to train a model. However, in a cellular network, the data‐collection step may induce a high communication overhead. As a result, to reduce the required size of the dataset, it may be interesting to carefully choose the positions to be labelled and to be used in the training. Therefore, an active learning approach for efficient data collection is proposed. It is first shown that significant gains (both in terms of positioning accuracy and size of the required dataset) can be obtained for the considered positioning problem using a genie. This validates the interest of active learning for positioning. Then, a practical method is proposed to approximate this genie.https://doi.org/10.1049/ell2.70040learning (artificial intelligence)location‐based servicessignal processingwireless channels |
| spellingShingle | Vincent Corlay Milan Courcoux‐Caro Active learning for efficient data selection in radio‐signal‐based positioning via deep learning Electronics Letters learning (artificial intelligence) location‐based services signal processing wireless channels |
| title | Active learning for efficient data selection in radio‐signal‐based positioning via deep learning |
| title_full | Active learning for efficient data selection in radio‐signal‐based positioning via deep learning |
| title_fullStr | Active learning for efficient data selection in radio‐signal‐based positioning via deep learning |
| title_full_unstemmed | Active learning for efficient data selection in radio‐signal‐based positioning via deep learning |
| title_short | Active learning for efficient data selection in radio‐signal‐based positioning via deep learning |
| title_sort | active learning for efficient data selection in radio signal based positioning via deep learning |
| topic | learning (artificial intelligence) location‐based services signal processing wireless channels |
| url | https://doi.org/10.1049/ell2.70040 |
| work_keys_str_mv | AT vincentcorlay activelearningforefficientdataselectioninradiosignalbasedpositioningviadeeplearning AT milancourcouxcaro activelearningforefficientdataselectioninradiosignalbasedpositioningviadeeplearning |