Joint Computation Offloading and Target Tracking in Integrated Sensing and Communication Enabled UAV Networks
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911837483696128 |
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| author | Van Chien, Trinh Cong, Mai Dinh Luong, Nguyen Cong Do, Tri Nhu Kim, Dong In Chatzinotas, Symeon |
| author_facet | Van Chien, Trinh Cong, Mai Dinh Luong, Nguyen Cong Do, Tri Nhu Kim, Dong In Chatzinotas, Symeon |
| contents | In this paper, we investigate a joint computation offloading and target tracking in Integrated Sensing and Communication (ISAC)-enabled unmanned aerial vehicle (UAV) network. Therein, the UAV has a computing task that is partially offloaded to the ground UE for execution. Meanwhile, the UAV uses the offloading bit sequence to estimate the velocity of a ground target based on an autocorrelation function. The performance of the velocity estimation that is represented by Cramer-Rao lower bound (CRB) depends on the length of the offloading bit sequence and the UAV's location. Thus, we jointly optimize the task size for offloading and the UAV's location to minimize the overall computation latency and the CRB of the mean square error for velocity estimation subject to the UAV's budget. The problem is non-convex, and we propose a genetic algorithm to solve it. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08396 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Joint Computation Offloading and Target Tracking in Integrated Sensing and Communication Enabled UAV Networks Van Chien, Trinh Cong, Mai Dinh Luong, Nguyen Cong Do, Tri Nhu Kim, Dong In Chatzinotas, Symeon Information Theory In this paper, we investigate a joint computation offloading and target tracking in Integrated Sensing and Communication (ISAC)-enabled unmanned aerial vehicle (UAV) network. Therein, the UAV has a computing task that is partially offloaded to the ground UE for execution. Meanwhile, the UAV uses the offloading bit sequence to estimate the velocity of a ground target based on an autocorrelation function. The performance of the velocity estimation that is represented by Cramer-Rao lower bound (CRB) depends on the length of the offloading bit sequence and the UAV's location. Thus, we jointly optimize the task size for offloading and the UAV's location to minimize the overall computation latency and the CRB of the mean square error for velocity estimation subject to the UAV's budget. The problem is non-convex, and we propose a genetic algorithm to solve it. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. |
| title | Joint Computation Offloading and Target Tracking in Integrated Sensing and Communication Enabled UAV Networks |
| topic | Information Theory |
| url | https://arxiv.org/abs/2404.08396 |