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

TL;DR

This paper develops a stochastic network calculus framework to optimize power allocation for supporting ultra-reliable low-latency communication in ISAC-enabled V2I networks, reducing information age violations.

Contribution

It introduces a theoretical approach to derive bounds on information freshness and proposes power allocation schemes for reliability enhancement in V2I networks.

Findings

Proven bounds on peak age of information violation probability

Power allocation schemes significantly reduce PAVP

Simulation results validate theoretical models

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.