Koopman based trajectory model and computation offloading for high mobility paradigm in ISAC enabled IoT system
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914851054419968 |
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| author | Tran, Minh-Tuan |
| author_facet | Tran, Minh-Tuan |
| contents | User experience on mobile devices is constrained by limited battery capacity and processing power, but 6G technology advancements are diving rapidly into mobile technical evolution. Mobile edge computing (MEC) offers a solution, offloading computationally intensive tasks to edge cloud servers, reducing battery drain compared to local processing. The upcoming integrated sensing and communication in mobile communication may improve the trajectory prediction and processing delays. This study proposes a greedy resource allocation optimization strategy for multi-user networks to minimize aggregate energy usage. Numerical results show potential improvement at 33\% for every 1000 iteration. Addressing prediction model division and velocity accuracy issues is crucial for better results. A plan for further improvement and achieving objectives is outlined for the upcoming work phase. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19871 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Koopman based trajectory model and computation offloading for high mobility paradigm in ISAC enabled IoT system Tran, Minh-Tuan Machine Learning Networking and Internet Architecture Systems and Control 52-08 C.2 User experience on mobile devices is constrained by limited battery capacity and processing power, but 6G technology advancements are diving rapidly into mobile technical evolution. Mobile edge computing (MEC) offers a solution, offloading computationally intensive tasks to edge cloud servers, reducing battery drain compared to local processing. The upcoming integrated sensing and communication in mobile communication may improve the trajectory prediction and processing delays. This study proposes a greedy resource allocation optimization strategy for multi-user networks to minimize aggregate energy usage. Numerical results show potential improvement at 33\% for every 1000 iteration. Addressing prediction model division and velocity accuracy issues is crucial for better results. A plan for further improvement and achieving objectives is outlined for the upcoming work phase. |
| title | Koopman based trajectory model and computation offloading for high mobility paradigm in ISAC enabled IoT system |
| topic | Machine Learning Networking and Internet Architecture Systems and Control 52-08 C.2 |
| url | https://arxiv.org/abs/2406.19871 |