Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Statistical AoI Guarantee Optimization for Supporting xURLLC in ISAC-enabled V2I Networks

Yanxi Zhang, Mingwu Yao, Qinghai Yang, Dongqi Yan, Xu Zhang, Xu Bao, Muyu Mei

TL;DR

This work tackles delivering xURLLC in ISAC-enabled V2I networks with short sensory packets by developing a stochastic-network-calculus framework that bounds the peak AoI violation probability (PAVP) using MGFs of packet inter-arrival and service times. It introduces a channel-evaluation mechanism and finite-blocklength based retransmissions over Rayleigh fading, derives MGFs for sensing and communication, and provides a power-allocation optimization to minimize the PAVP upper bound under a total power constraint. Theoretical results are encapsulated in a PAVP bound $\Upsilon(\alpha)$ (Theorem 1) and validated via simulations that show tight agreement with the bound across key parameters like bandwidth, DEP, threshold, and deferral duration. The findings offer practical guidelines for selecting the sensing/communication power split and system parameters to achieve robust, low-latency ISAC-V2I performance in dynamic vehicular environments.

Abstract

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.

Statistical AoI Guarantee Optimization for Supporting xURLLC in ISAC-enabled V2I Networks

TL;DR

This work tackles delivering xURLLC in ISAC-enabled V2I networks with short sensory packets by developing a stochastic-network-calculus framework that bounds the peak AoI violation probability (PAVP) using MGFs of packet inter-arrival and service times. It introduces a channel-evaluation mechanism and finite-blocklength based retransmissions over Rayleigh fading, derives MGFs for sensing and communication, and provides a power-allocation optimization to minimize the PAVP upper bound under a total power constraint. Theoretical results are encapsulated in a PAVP bound (Theorem 1) and validated via simulations that show tight agreement with the bound across key parameters like bandwidth, DEP, threshold, and deferral duration. The findings offer practical guidelines for selecting the sensing/communication power split and system parameters to achieve robust, low-latency ISAC-V2I performance in dynamic vehicular environments.

Abstract

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.
Paper Structure (11 sections, 27 equations, 7 figures, 1 table)

This paper contains 11 sections, 27 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Model
  4. Performance Metric and Problem Statement
  5. The Analysis Framework for PAVP
  6. Joint Analysis and Optimization for PAVP of Sensory Packets in the ISAC-enabled V2I Network
  7. MGF Characterization for Packet Inter-arrival Time
  8. MGF Characterization for Packet Service Time
  9. PAVP Upper Bound Optimization under Power Constraints
  10. Numerical Results
  11. Conclusion

Figures (7)

  • Figure : Fig. 1: The ISAC-enabled V2I networks.
  • Figure : Fig. 2: SDP vs. $D$ under various $\bar{\rho}$ and $d$. The lines represent the theoretical values, and the markers represent the simulation values.
  • Figure : Fig. 4: PAVP vs. $\alpha$ under various thresholds $\zeta$.
  • Figure : Fig. 2: SDP vs. $D$ under various $\bar{\rho}$ and $d$. The lines represent the theoretical values, and the markers represent the simulation values.
  • Figure : Fig. 3: PAVP vs. bandwidth under various $\epsilon$. The simulated curves, denoted as "simu.", and the theoretical upper bounds, denoted as "upp.", are used throughout unless otherwise stated.
  • ...and 2 more figures