Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail
By Mohamed K. Abdel-Aziz, Chen-Feng Liu, Sumudu Samarakoon, Medhi Bennis, Walid Saad. 2018
57 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
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In Computer Networks and Communications
In Electrical and Electronic Engineering
- Anis Elgabli
- Medhi Bennis
- Merouane Debbah
- Jihong Park
- Sumudu Samarakoon
- Walid Saad
- Chen-Feng Liu
- Rui Zhang
Citations in the corpus
- Wireless Network Intelligence at the Edge (2018, neutral): “decisions for safe operation [12], [13].”
- Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks (2018, background): “However, the problem of AoI minimization for ultra reliable low latency communication (URLLC) systems should pay more attention to the tail behavior in addition to optimizing on average metrics [5].”