상세정보

  V2X communication enables the delivery of information messages from a vehicle to any entity that may affect the vehicle, and vice versa. The messages exchanged among neighboring vehicles are the foundation of crash avoidance in future driving environments. Moving vehicles continually broadcast their position, heading, acceleration, steering angle, and vehicle size, among other data, so that neighboring vehicles may track and predict each others’ positions, thereby reducing the chance of a collision. One difficulty in the reliable delivery of the messages, however, is channel congestion, which occurs when the wireless channel capacity allocated for the beacon exchange is exceeded due to dense vehicular traffic. 
    In the European Telecommunication Standards Institute (ETSI) framework, the vehicle-to-vehicle (V2V) and the vehicle-to-roadside (V2R) communications on the 5 GHz frequency band must use the decentralized congestion control (DCC) algorithm standardized by the ETSI. The DCC algorithm distinguishes itself from other methods in that it simultaneously regulates default four parameters that all work to the identical effects. However, it has been claimed that this apparently reassuring feature is actually excessive and can lead DCC to perform sub-optimally. We show that by the simplified DCC algorithm to use a single control parameter, the DCC framework can obtain comparable performance with state of the art algorithms whether adaptive DCC algorithm or reactive DCC algorithm. Also, this dissertation explores how two algorithms, DCC from Europe and Error Model Based Adaptive Rate Control for Vehicle-to-Vehicle Communication (EMBARC) from the United States fare against each other in a mixed vehicular network, and reveals that DCC has a serious fairness problem in face of EMBARC, and shows the simplified DCC algorithm gets better performance than DCC algorithm to improve fairness. 

Contents
  Abstract	i
  List of Tables	vi
  List of Figures	vii
1. Introduction	1
2. Background	5
    2.1 Decentralized Congestion Control (DCC)	6
         2.1.1 ETSI DCC architecture	7
         2.1.2 ETSI DCC mechanism	10
    2.2 Joint rate-power control algorithm	14
         2.2.1 Transmission rate control	14
         2.2.2 Transmission power control	16
    2.3 LIMERIC	18
         2.3.1 Channel capacity	18
         2.3.2 Rate control	19
    2.4 PULSAR	20
         2.4.1 Channel load assessment	20
         2.4.2 Rate adaptation	21
         2.4.3 Information sharing	23
    2.5 EMBARC	24
         2.5.1 STE component	25
         2.5.2 Transmission scheduling mechanism	27
3. Improving ETSI DCC algorithm with an adaptive approach	31
    3.1 Introduction	31
    3.2 Simple adaptive DCC algorithm	33
    3.3 Evaluation	36
         3.3.1 PDR and number of received messages	37
         3.3.2 Inter-message gap	40
    3.4 Summary	41
4. Improving ETSI DCC algorithm with a reactive approach	42
    4.1 Introduction	42
    4.2 Simple reactive DCC algorithm 	44
         4.2.1 Description of DCC	44
         4.2.2 Simplifying DCC in reactive control frameworks	45
    4.3 Experiments	48
         4.3.1 Inter-packet gap and tracking error	51
         4.3.2 Stability and fairness	60
    4.4 Summary	63
5. Coexistence performance of DCC and EMBARC	64
    5.1 Introduction	64
    5.2 DCC and EMBARC	67
         5.2.1 Decentralized Congestion Control (DCC)	67
         5.2.2 Error Model Based Adaptive Rate Control (EMBARC)	69
    5.3 Comparative performance evaluation	70
         5.3.1 Configurations	71
         5.3.2 Results	72
         5.3.3 Coexistence performance evaluation of simplifying DCC               and EMBARC	80
    5.4 Summary	87
6. Conclusion 	88
Bibliography	90
Appendix A Cooperative ITS (C-ITS) standards	98
    A.1 Overview of C-ITS standards 	98
    A.2 C-ITS Access Layer standards	103
    A.3 C-ITS Networking and Transport Layer standards	106
    A.4 C-ITS Facilities Layer standards	109
    A.5 C-ITS Application Layer standards	114
Summary (in Korean)	117