Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail

While the notion of age of information (AoI) has recently emerged as an important concept for analyzing ultra-reliable low-latency communications (URLLC), the majority of the existing works have focused on the average AoI measure. However, an average AoI based design falls short in properly characterizing the performance of URLLC systems as it cannot account for extreme events that occur with very low probabilities. In contrast, in this paper, the main objective is to go beyond the traditional notion of average AoI by characterizing and optimizing a URLLC system while capturing the AoI tail distribution. In particular, the problem of vehicles' power minimization while ensuring stringent latency and reliability constraints in terms of probabilistic AoI is studied. To this end, a novel and efficient mapping between both AoI and queue length distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem. Simulation results shows a nearly two-fold improvement in terms of shortening the tail of the AoI distribution compared to a baseline whose design is based on the maximum queue length among vehicles, when the number of vehicular user equipment (VUE) pairs is 80. The results also show that this performance gain increases significantly as the number of VUE pairs increases.Comment: Accepted in IEEE GLOBECOM 2018 with 7 pages, 6 figure

Optimizing Age-of-Information in a Multi-class Queueing System

We consider the age-of-information in a multi-class $M/G/1$ queueing system, where each class generates packets containing status information. Age of information is a relatively new metric that measures the amount of time that elapsed between status updates, thus accounting for both the queueing delay and the delay between packet generation. This gives rise to a tradeoff between frequency of status updates, and queueing delay. In this paper, we study this tradeoff in a system with heterogenous users modeled as a multi-class $M/G/1$ queue. To this end, we derive the exact peak age-of-Information (PAoI) profile of the system, which measures the "freshness" of the status information. We then seek to optimize the age of information, by formulating the problem using quasiconvex optimization, and obtain structural properties of the optimal solution