Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866914948005756928 |
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| author | Bui, Van-Phuc Ana, Pedro Maia de Sant Gherekhloo, Soheil Pandey, Shashi Raj Popovski, Petar |
| author_facet | Bui, Van-Phuc Ana, Pedro Maia de Sant Gherekhloo, Soheil Pandey, Shashi Raj Popovski, Petar |
| contents | This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer-Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08005 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications Bui, Van-Phuc Ana, Pedro Maia de Sant Gherekhloo, Soheil Pandey, Shashi Raj Popovski, Petar Signal Processing This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer-Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency. |
| title | Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2409.08005 |