Statistical AoI Guarantee Optimization for Supporting xURLLC in ISAC-enabled V2I Networks
<|reference_start|>Statistical AoI Guarantee Optimization for Supporting xURLLC in ISAC-enabled V2I Networks: 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.<|reference_end|>
@article{zhang2024statistical,
    title={Statistical AoI Guarantee Optimization for Supporting xURLLC in
  ISAC-enabled V2I Networks},
    author={Yanxi Zhang (1), Mingwu Yao (1), Qinghai Yang (1), Dongqi Yan (1), Xu
  Zhang (1), Xu Bao (2) and Muyu Mei (2) ((1) Xidian University, (2) Jiangsu
  University)},
    journal={arXiv preprint arXiv:2408.00381},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00381},
    primaryClass={cs.IT cs.SY eess.SY math.IT}
}