Statistical AoI Guarantee Optimization for Supporting xURLLC in ISAC-enabled V2I Networks
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911974535725056 |
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| author | Zhang, Yanxi Yao, Mingwu Yang, Qinghai Yan, Dongqi Zhang, Xu Bao, Xu Mei, Muyu |
| author_facet | Zhang, Yanxi Yao, Mingwu Yang, Qinghai Yan, Dongqi Zhang, Xu Bao, Xu Mei, Muyu |
| contents | This paper addresses the critical challenge of supporting next-generation ultra-reliable and low-latency communication (xURLLC) within integrated sensing and communication (ISAC)-enabled vehicle-to-infrastructure (V2I) networks. We incorporate channel evaluation and retransmission mechanisms for real-time reliability enhancement. Using stochastic network calculus (SNC), we establish a theoretical framework to derive upper bounds for the peak age of information violation probability (PAVP) via characterized sensing and communication moment generation functions (MGFs). By optimizing these bounds, we develop power allocation schemes that significantly reduce the statistical PAVP of sensory packets in such networks. Simulations validate our theoretical derivations and demonstrate the effectiveness of our proposed schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00381 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Statistical AoI Guarantee Optimization for Supporting xURLLC in ISAC-enabled V2I Networks Zhang, Yanxi Yao, Mingwu Yang, Qinghai Yan, Dongqi Zhang, Xu Bao, Xu Mei, Muyu Information Theory Systems and Control This paper addresses the critical challenge of supporting next-generation ultra-reliable and low-latency communication (xURLLC) within integrated sensing and communication (ISAC)-enabled vehicle-to-infrastructure (V2I) networks. We incorporate channel evaluation and retransmission mechanisms for real-time reliability enhancement. Using stochastic network calculus (SNC), we establish a theoretical framework to derive upper bounds for the peak age of information violation probability (PAVP) via characterized sensing and communication moment generation functions (MGFs). By optimizing these bounds, we develop power allocation schemes that significantly reduce the statistical PAVP of sensory packets in such networks. Simulations validate our theoretical derivations and demonstrate the effectiveness of our proposed schemes. |
| title | Statistical AoI Guarantee Optimization for Supporting xURLLC in ISAC-enabled V2I Networks |
| topic | Information Theory Systems and Control |
| url | https://arxiv.org/abs/2408.00381 |