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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.20849 |
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| _version_ | 1866908422424756224 |
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| author | Lu, Ziyang Gursoy, M. Cenk Mohan, Chilukuri K. Varshney, Pramod K. |
| author_facet | Lu, Ziyang Gursoy, M. Cenk Mohan, Chilukuri K. Varshney, Pramod K. |
| contents | In this paper, we tackle the task of adaptive time allocation in integrated sensing and communication systems equipped with radar and communication units. The dual-functional radar-communication system's task involves allocating dwell times for tracking multiple targets and utilizing the remaining time for data transmission towards estimated target locations. We introduce a novel constrained deep reinforcement learning (CDRL) approach, designed to optimize resource allocation between tracking and communication under time budget constraints, thereby enhancing target communication quality. Our numerical results demonstrate the efficiency of our proposed CDRL framework, confirming its ability to maximize communication quality in highly dynamic environments while adhering to time constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20849 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning-Based Resource Management in Integrated Sensing and Communication Systems Lu, Ziyang Gursoy, M. Cenk Mohan, Chilukuri K. Varshney, Pramod K. Machine Learning In this paper, we tackle the task of adaptive time allocation in integrated sensing and communication systems equipped with radar and communication units. The dual-functional radar-communication system's task involves allocating dwell times for tracking multiple targets and utilizing the remaining time for data transmission towards estimated target locations. We introduce a novel constrained deep reinforcement learning (CDRL) approach, designed to optimize resource allocation between tracking and communication under time budget constraints, thereby enhancing target communication quality. Our numerical results demonstrate the efficiency of our proposed CDRL framework, confirming its ability to maximize communication quality in highly dynamic environments while adhering to time constraints. |
| title | Learning-Based Resource Management in Integrated Sensing and Communication Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.20849 |