Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networks

TL;DR

This paper enhances ultra-reliable low-latency vehicular communication by focusing on the tail distribution of age of information, using EVT and Lyapunov methods to minimize transmission power while controlling extreme AoI events.

Contribution

It introduces a novel approach to optimize AoI tail distribution in URLLC, addressing the limitations of average AoI metrics in vehicular networks.

Findings

Over two-fold reduction in AoI tail length

Effective power minimization under strict AoI constraints

Tradeoff identified between arrival rate and AoI

Abstract

While the notion of age of information (AoI) has recently been proposed for analyzing ultra-reliable low-latency communications (URLLC), most of the existing works have focused on the average AoI measure. Designing a wireless network based on average AoI will fail to characterize the performance of URLLC systems, as it cannot account for extreme AoI events, occurring with very low probabilities. In contrast, this paper goes beyond the average AoI to improve URLLC in a vehicular communication network by characterizing and controlling the AoI tail distribution. In particular, the transmission power minimization problem is studied under stringent URLLC constraints in terms of probabilistic AoI for both deterministic and Markovian traffic arrivals. Accordingly, an efficient novel mapping between AoI and queue-related distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem considering both long and short packets transmissions. Simulation results show over a two-fold improvement, in shortening the AoI distribution tail, versus a baseline that models the maximum queue length distribution, in addition to a tradeoff between arrival rate and AoI.