Hyppää sisältöön
    • FI
    • ENG
  • FI
  • /
  • EN
OuluREPO – Oulun yliopiston julkaisuarkisto / University of Oulu repository
Näytä viite 
  •   OuluREPO etusivu
  • Oulun yliopisto
  • Avoin saatavuus
  • Näytä viite
  •   OuluREPO etusivu
  • Oulun yliopisto
  • Avoin saatavuus
  • Näytä viite
JavaScript is disabled for your browser. Some features of this site may not work without it.

Distributed federated learning for ultra-reliable low-latency vehicular communications

Samarakoon, Sumudu; Bennis, Mehdi; Saad, Walid; Debbah, Mérouane (2019-11-28)

 
Avaa tiedosto
nbnfi-fe202001314071.pdf (3.627Mt)
nbnfi-fe202001314071_meta.xml (32.06Kt)
nbnfi-fe202001314071_solr.xml (35.93Kt)
Lataukset: 

URL:
https://doi.org/10.1109/TCOMM.2019.2956472

Samarakoon, Sumudu
Bennis, Mehdi
Saad, Walid
Debbah, Mérouane
Institute of Electrical and Electronics Engineers
28.11.2019

S. Samarakoon, M. Bennis, W. Saad and M. Debbah, "Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications," in IEEE Transactions on Communications, vol. 68, no. 2, pp. 1146-1159, Feb. 2020. doi: 10.1109/TCOMM.2019.2956472

https://rightsstatements.org/vocab/InC/1.0/
© 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.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/TCOMM.2019.2956472
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe202001314071
Tiivistelmä

Abstract

In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied. Therein, the network-wide power consumption of vehicular users (VUEs) is minimized subject to high reliability in terms of probabilistic queuing delays. Using extreme value theory, a new reliability measure is defined to characterize extreme events pertaining to vehicles’ queue lengths exceeding a predefined threshold. To learn these extreme events, assuming they are independently and identically distributed over VUEs, a novel distributed approach based on federated learning (FL) is proposed to estimate the tail distribution of the queue lengths. Considering the communication delays incurred by FL over wireless links, Lyapunov optimization is used to derive the JPRA policies enabling URLLC for each VUE in a distributed manner. The proposed solution is then validated via extensive simulations using a Manhattan mobility model. Simulation results show that FL enables the proposed method to estimate the tail distribution of queues with an accuracy that is close to a centralized solution with up to 79% reductions in the amount of exchanged data. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, while reducing the average power consumption by two folds, compared to an average queue-based baseline.

Kokoelmat
  • Avoin saatavuus [37920]
oulurepo@oulu.fiOulun yliopiston kirjastoOuluCRISLaturiMuuntaja
SaavutettavuusselosteTietosuojailmoitusYlläpidon kirjautuminen
 

Selaa kokoelmaa

NimekkeetTekijätJulkaisuajatAsiasanatUusimmatSivukartta

Omat tiedot

Kirjaudu sisäänRekisteröidy
oulurepo@oulu.fiOulun yliopiston kirjastoOuluCRISLaturiMuuntaja
SaavutettavuusselosteTietosuojailmoitusYlläpidon kirjautuminen