Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems
In contemporary wireless communication systems, achieving precise localization of communicating devices and accurate channel estimation is crucial for enhancing operational efficiency and reliability. This study introduces a novel approach that integrates the localization task and channel estimation...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4095 |
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| author | Maria Camila Molina Iness Ahriz Lounis Zerioul Michel Terré |
| author_facet | Maria Camila Molina Iness Ahriz Lounis Zerioul Michel Terré |
| author_sort | Maria Camila Molina |
| collection | DOAJ |
| description | In contemporary wireless communication systems, achieving precise localization of communicating devices and accurate channel estimation is crucial for enhancing operational efficiency and reliability. This study introduces a novel approach that integrates the localization task and channel estimation into a single framework. We present a multi-task neural network architecture capable of simultaneously estimating channels from multiple base stations in a blind manner while estimating user terminal coordinates in given indoor environments. This approach exploits the relationship between channel characteristics and spatial information, using the same channel state information (CSI) data to perform both tasks with a single model. We evaluate the proposed solution, assessing its effectiveness across differing antenna spacing configurations and indoor test environments using both WiFi and 5G orthogonal frequency-division multiplexing (OFDM) systems. The results show performance benefits, achieving comparable channel estimation results to other studies while simultaneously providing a localization estimate, resulting in reduced model overhead while leveraging spatial context. The presented system demonstrates potential to improve the efficiency of communication systems in real-world applications, aligning with the goals of emerging integrated sensing and communication (ISAC) systems. Results based on experimental data using the proposed solution show a 50th percentile localization error of 1.62 m for 3-tap channels and 0.89 m for 10-tap channels. |
| format | Article |
| id | doaj-art-b22777c6ca3d42dfbd79078df1c90f2a |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b22777c6ca3d42dfbd79078df1c90f2a2025-08-20T03:49:55ZengMDPI AGSensors1424-82202025-06-012513409510.3390/s25134095Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM SystemsMaria Camila Molina0Iness Ahriz1Lounis Zerioul2Michel Terré3Conservatoire National des Arts et Métiers, CEDRIC, 292 rue Saint Martin, 75141 Paris, FranceConservatoire National des Arts et Métiers, CEDRIC, 292 rue Saint Martin, 75141 Paris, FranceConservatoire National des Arts et Métiers, CEDRIC, 292 rue Saint Martin, 75141 Paris, FranceConservatoire National des Arts et Métiers, CEDRIC, 292 rue Saint Martin, 75141 Paris, FranceIn contemporary wireless communication systems, achieving precise localization of communicating devices and accurate channel estimation is crucial for enhancing operational efficiency and reliability. This study introduces a novel approach that integrates the localization task and channel estimation into a single framework. We present a multi-task neural network architecture capable of simultaneously estimating channels from multiple base stations in a blind manner while estimating user terminal coordinates in given indoor environments. This approach exploits the relationship between channel characteristics and spatial information, using the same channel state information (CSI) data to perform both tasks with a single model. We evaluate the proposed solution, assessing its effectiveness across differing antenna spacing configurations and indoor test environments using both WiFi and 5G orthogonal frequency-division multiplexing (OFDM) systems. The results show performance benefits, achieving comparable channel estimation results to other studies while simultaneously providing a localization estimate, resulting in reduced model overhead while leveraging spatial context. The presented system demonstrates potential to improve the efficiency of communication systems in real-world applications, aligning with the goals of emerging integrated sensing and communication (ISAC) systems. Results based on experimental data using the proposed solution show a 50th percentile localization error of 1.62 m for 3-tap channels and 0.89 m for 10-tap channels.https://www.mdpi.com/1424-8220/25/13/4095indoor localizationfingerprint localizationchannel state informationblind channel estimationOFDM |
| spellingShingle | Maria Camila Molina Iness Ahriz Lounis Zerioul Michel Terré Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems Sensors indoor localization fingerprint localization channel state information blind channel estimation OFDM |
| title | Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems |
| title_full | Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems |
| title_fullStr | Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems |
| title_full_unstemmed | Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems |
| title_short | Multi-Task Learning for Joint Indoor Localization and Blind Channel Estimation in OFDM Systems |
| title_sort | multi task learning for joint indoor localization and blind channel estimation in ofdm systems |
| topic | indoor localization fingerprint localization channel state information blind channel estimation OFDM |
| url | https://www.mdpi.com/1424-8220/25/13/4095 |
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