Packet Delivery Impact of Predictive Resource Allocation for Quasi-Periodic Cellular V2X Communication

Hyeonji Seon, Hojeong Lee, Hyogon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The channel width for Intelligent Transportation System (ITS) band for vehicle-to-everything (V2X) system is expected to be relatively narrow. It implies that effective congestion control mechanisms will be essential. A well-established standard mechanism is the Cooperative Awareness Message (CAM) generation rule that drops unnecessary periodic beacons. Dropping packets from a periodic packet stream produces a non-periodic traffic. It renders the distributed Semi-Persistent Scheduling (SPS) algorithm exposed to resource waste and packet collision problems. The problems can be tackled by supplementing SPS with machine learning (ML) that predicts the transmission times of non-dropped CAM packets and reserves a transmit resource at the predicted time. The proposed approach is shown to significantly extend the distance up to which the target packet reception ratio (PRR) is met, compared to the current SPS algorithm.

Original languageEnglish
Title of host publication2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350311143
Publication statusPublished - 2023
Event97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy
Duration: 2023 Jun 202023 Jun 23

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference97th IEEE Vehicular Technology Conference, VTC 2023-Spring

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2020R1A2C3011888).

Publisher Copyright:
© 2023 IEEE.


  • V2X
  • congestion control
  • deep learning
  • packet reception ratio (PRR)
  • prediction
  • quasi-periodic traffic

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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