9,389 research outputs found
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 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
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
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