Efficient RRH Activation Management for 5G V2X

被引:1
|
作者
Ke, Jing-Wen [1 ]
Hwang, Ren-Hung [2 ]
Wang, Chih-Yu [3 ]
Kuo, Jian-Jhih [1 ]
Chen, Wei-Yu [3 ]
机构
[1] Natl Chung Cheng Univ, Dept Comp Sci Informat Engn, Chiayi 60102, Taiwan
[2] Natl Yang Ming Chiao Tung Univ, Coll Artificial Intelligence, Tainan 71150, Taiwan
[3] Acad Sinica, Res Ctr Informat Technol Innovat CITI, Taipei 115, Taiwan
关键词
5G; vehicular-to-everything (V2X); remote radio head (RRH); resource allocation; optimization;
D O I
10.1109/TMC.2022.3232547
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Vehicle-to-everything (V2X) communication is one of the key technologies of 5G New Radio to support emerging applications such as autonomous driving. Due to the high density of vehicles, Remote Radio Heads (RRHs) will be deployed as Road Side Units to support V2X. Nevertheless, activation of all RRHs during low-traffic off-peak hours may cause energy wasting. The proper activation of RRH and association between vehicles and RRHs while maintaining the required service quality are the keys to reducing energy consumption. In this work, we first formulate the problem as an Integer Linear Programming optimization problem and prove that the problem is NP-hard. Then, we propose two novel algorithms, referred to as "Least Delete (LD)" and "Largest-First Rounding with Capacity Constraints (LFRCC)." The simulation results show that the proposed algorithms can achieve significantly better performance compared with existing solutions and are competitive with the optimal solution. Specifically, the LD and LFRCC algorithms can reduce the number of activated RRHs by 86% and 89% in low-density scenarios. In high-density scenarios, the LD algorithm can reduce the number of activated RRHs by 90%. In addition, the solution of LFRCC is larger than that of the optimal solution within 7% on average.
引用
收藏
页码:1215 / 1229
页数:15
相关论文
共 50 条
  • [31] Intelligent Network Slicing for V2X Services Toward 5G
    Mei, Jie
    Wang, Xianbin
    Zheng, Kan
    IEEE NETWORK, 2019, 33 (06): : 196 - 204
  • [32] Fast Packet Classification for V2X Services in 5G Networks
    Pak, Wooguil
    JOURNAL OF COMMUNICATIONS AND NETWORKS, 2017, 19 (03) : 218 - 226
  • [33] Leveraging 5G TSN in V2X Communication for Cloud Vehicle
    Wang, Dan
    Sun, Tao
    2020 IEEE INTERNATIONAL CONFERENCE ON EDGE COMPUTING (EDGE 2020), 2020, : 106 - 110
  • [34] Ultra-high reliable 5G V2X communications
    Husain S.S.
    Kunz A.
    Prasad A.
    Pateromichelakis E.
    Samdanis K.
    IEEE Communications Standards Magazine, 2019, 3 (02): : 46 - 52
  • [35] Task Offloading with 5G Network Slicing for V2X Communications
    Alkhoury, George
    Berri, Sara
    Chorti, Arsenia
    2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 4425 - 4430
  • [36] Enhanced Resource Scheduling for Platooning in 5G V2X Systems
    Hegde, Sudeep
    Blume, Oliver
    Shrivastava, Rudraksh
    Bakker, Hajo
    2019 IEEE 2ND 5G WORLD FORUM (5GWF), 2019, : 108 - 113
  • [37] WLAN, radar, IoT, V2X to complement 5G at IMS
    1600, Nelson Publishing Inc. (56):
  • [38] Analytical Model of 5G V2X Mode 2 for Sporadic Traffic
    Bankov, Dmitry
    Khorov, Evgeny
    Krasilov, Artem
    Otmakhov, Artem
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2023, 12 (08) : 1449 - 1453
  • [39] Energy-Efficient Reference Signal Optimization for 5G V2X Joint Communication and Sensing
    Zhao, Qimin
    Li, Songqian
    Tang, Aimin
    Wang, Xudong
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022), 2022, : 1040 - 1045
  • [40] IEEE 802.11bd & 5G NR V2X: Evolution of Radio Access Technologies for V2X Communications
    Naik, Gaurang
    Choudhury, Biplav
    Park, Jung-Min
    IEEE ACCESS, 2019, 7 : 70169 - 70184