Deep Reinforcement Learning based Congestion Control for V2X Communication

被引：7
|
作者：
Roshdi, Moustafa [1 ]
Bhadauria, Shubhangi [1 ]
Hassan, Khaled [2 ]
Fischer, Georg [3 ]
机构：
[1] Fraunhofer IIS, Erlangen, Germany
[2] Robert Bosch GmbH, Gerlingen, Germany
[3] Friedrich Alexander Univ, Erlangen, Germany
来源：
2021 IEEE 32ND ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC) | 2021年
关键词：
C-V2X communication; Congestion control; DRL; FRAMEWORK;
D O I：
10.1109/PIMRC50174.2021.9569259
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
In release 14 (Rel-14) Long Term Evolution (LTE), the 3rd generation partnership project (3GPP) standard has introduced Cellular Vehicle to Everything (C-V2X) communication to pave the way for future intelligent transport systems (ITS). C-V2X communication envisions supporting a diverse range of use cases with varying quality of service (QoS) requirements. For example, cooperative collision avoidance requires stringent reliability, while infotainment use cases require a high data throughput. C-V2X communication remains susceptible to performance degradation due to network congestion. This paper presents a centralized congestion control scheme for C-V2X communication based on the Deep Reinforcement Learning (DRL) framework. A performance evaluation of the algorithm is conducted based on system-level simulation based on TAPASCologne scenario in the Simulation of Urban Mobility (SUMO) platform. The results show the effectiveness of a DRL-based approach to achieve the packet reception ratio (PRR) as per the packet's associated QoS while maintaining the average measured Channel Busy Ratio (CBR) below 0.65.
引用
下载
收藏
页数：6
相关论文
共 50 条
  • [1] An Intelligent Congestion Control Strategy in Heterogeneous V2X Based on Deep Reinforcement Learning
    Wang, Hui
    Li, Haoyu
    Zhao, Yuan
    SYMMETRY-BASEL, 2022, 14 (05):
  • [2] Deep Reinforcement Learning-Based Distributed Congestion Control in Cellular V2X Networks
    Choi, Joo-Young
    Jo, Han-Shin
    Mun, Cheol
    Yook, Jong-Gwan
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2021, 10 (11) : 2582 - 2586
  • [3] Jointly Learning V2X Communication and Platoon Control with Deep Reinforcement Learning
    Liu, Tong
    Lei, Lei
    Liu, Zhiming
    Zheng, Kan
    2023 IEEE 34TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS, PIMRC, 2023,
  • [4] Deep reinforcement learning-based dual-mode congestion control for cellular V2X environments
    Yoon, Yeomyung
    Lee, Hojeong
    Kim, Hyogon
    ELECTRONICS LETTERS, 2023, 59 (20)
  • [5] QoS based Deep Reinforcement Learning for V2X Resource Allocation
    Bhadauria, Shubhangi
    Shabbir, Zohaib
    Roth-Mandutz, Elke
    Fischer, Georg
    2020 IEEE INTERNATIONAL BLACK SEA CONFERENCE ON COMMUNICATIONS AND NETWORKING (BLACKSEACOM), 2020,
  • [6] Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information
    Lei, Lei
    Liu, Tong
    Zheng, Kan
    Hanzo, Lajos
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (06) : 5811 - 5826
  • [7] An Efficient Deep Reinforcement Learning Based Distributed Channel Multiplexing Framework for V2X Communication Networks
    Hu, Run
    Wang, Xinguo
    Su, Yuyuan
    Yang, Bin
    2021 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS AND COMPUTER ENGINEERING (ICCECE), 2021, : 154 - 160
  • [8] Deep Learning Based Predictive Power Allocation for V2X Communication
    Sang, Jian
    Zhou, Ting
    Xu, Tianheng
    Jin, Yanliang
    Zhu, Zhenghang
    IEEE ACCESS, 2021, 9 (09): : 72881 - 72893
  • [9] Adaptive Task Offloading in V2X Networks Based on Deep Reinforcement Learning
    Yang, Dengsong
    Ni, Baili
    Qin, Hao
    Ma, Fei
    INTERNATIONAL CONFERENCE ON INTELLIGENT TRAFFIC SYSTEMS AND SMART CITY (ITSSC 2021), 2022, 12165
  • [10] Spectrum Management with Congestion Avoidance for V2X Based on Multi-Agent Reinforcement Learning
    Althamary, Ibrahim
    Lin, Jun-Yong
    Huang, Chih-Wei
    2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2020,
12345