Study of Cluster-based Vehicular Network for Heterogeneous Network Access by Using Reinforcement Learning and Fuzzy Theory

• Main Authors: Yu-Ling Chen, 陳昱伶
• Other Authors: Yen-Wen Chen
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/5f6zu3

碩士 === 國立中央大學 === 通訊工程學系 === 107 === Vehicular communication is a technology developed to support autonomous driving. Recently, the two major communication standards organization, Third Generation Partnership Project (3GPP) and Wi-Fi Alliance (WFA), are actively studying related technologies. However, each of the two standards has its own characteristics, along with some features such as high mobility of vehicles, frequent topology changes and limited transmission resources. Consequently, how to select the communication interface will be the problem that we should solve in the future. However, even if we disperse the vehicles into two different communication interface, the control signaling overhead caused by the connections between vehicles and the central equipment will exhaust the transmission resources. Therefore, the issue of designing a proper transmission model will be important. In other words, we aim to reduce the number of connections between the vehicle and the central equipment, achieved by forcing the vehicle to transmit data via its neighbor. According to information given by vehicles and environment condition of the system, we proposed an algorithm that decides which communication interface the vehicle should choose, as well as a vehicle clustering method, in order to improve packet delivery ratio and reduce the number of connections efficiently.