2026-04-21T14:38:51Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/54722024-03-01T12:12:02Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Alonso Gómez, Arrate author 2022 Vehicular communications have grown in interest over the years and are nowadays recognized as a pillar for the Intelligent Transportation Systems (ITSs) in order to ensure an efficient management of the road traffic and to achieve a reduction in the number of traffic accidents. To support the safety applications, both the ETSI ITS-G5 and IEEE 1609 standard families require each vehicle to deliver periodic awareness messages throughout the neighborhood. As the vehicles density grows, the scenario dynamics may require a high message exchange that can easily lead to a radio channel congestion issue and then to a degradation on safety critical services. ETSI has defined a Decentralized Congestion Control (DCC) mechanism to mitigate the channel congestion acting on the transmission parameters (i.e., message rate, transmit power and data-rate) with performances that vary according to the specific algorithm. In this paper, a review of the DCC standardization activities is proposed as well as an analysis of the existing methods and algorithms for the congestion mitigation. Also, some applied machine learning techniques for DCC are addressed. 2214-2096 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=164922 https://hdl.handle.net/20.500.11984/5472 Vehicular networks Wireless communication Decentralized congestion control ETSI ITS-G5 DSRC Machine learning Survey on decentralized congestion control methods for vehicular communication