University of Oulu

M. K. Abdel-Aziz, S. Samarakoon, C. Liu, M. Bennis and W. Saad, "Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networks," in IEEE Transactions on Communications, vol. 68, no. 3, pp. 1911-1924, March 2020. doi: 10.1109/TCOMM.2019.2961083

Optimized age of information tail for ultra-reliable low-latency communications in vehicular networks

Saved in:
Author: Abdel-Aziz, Mohamed K.1; Samarakoon, Sumudu1; Liu, Chen-Feng1;
Organizations: 1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202001131883
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-01-13
Description:

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.

see all

Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 68
Issue: 3
Pages: 1911 - 1924
DOI: 10.1109/TCOMM.2019.2961083
OADOI: https://oadoi.org/10.1109/TCOMM.2019.2961083
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
5G
Funding: This work was supported in part by the Academy of Finland project CARMA, and 6Genesis Flagship (grant no. 318927), in part by the INFOTECH project NOOR, in part by the Office of Naval Research (ONR) under MURI Grant N00014-19-1-2621, in part by the National Science Foundation under Grant IIS-1633363, and in part by the Kvantum Institute strategic project SAFARI.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.