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논문 기본 정보

자료유형
학술대회자료
저자정보
Manuel Eugenio Morocho Cayamcela (Kumoh National Institute of Technology) Wansu Lim (Kumoh National Institute of Technology)
저널정보
한국통신학회 한국통신학회 학술대회논문집 2019년도 한국통신학회 하계종합학술발표회 논문집
발행연도
2019.6
수록면
294 - 297 (4page)

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초록· 키워드

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Vehicle-to-vehicle (V2V) technology generally adopts Dedicated Short-Range Communications (DSRC) to transmit based safety messages (BSMs) e.g., geographical location, braking information, speed, the status of the turn signal, and direction of travel. Specific propagation and wireless communications channel models have been proposed from industry and academic researchers. However, the range of DSRC is limited to a few hundred meters, and it is necessary to employ a multi-hop communication to extend the range of communication, reaching many target vehicles as possible. In this article, we explore the problem of multi-hop connectivity in V2V networks and propose a methodology that consists of two different deep learning (DL) routines. First, two convolutional neural networks (CNN) are created and tuned to segment terrestrial imagery into different environments. The multi-environments are anticipated to have different propagation models. The second part uses a reinforcement learning (RL) algorithm to find the optimal multi-hop path with the lowest propagation loss, based on the results of the environment segmentation. The optimal multi-hop link is simulated and compared with current single propagation models, showing that our proposal can extend the coverage of multi-hop wireless links by transmitting the link via the optimum path.

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Abstract
Ⅰ. INTRODUCTION
Ⅱ. PROPOSED METHODOLOGY
Ⅲ. SIMULATION RESULTS AND DISCUSSION
Ⅳ. CONCLUSIONS AND FUTURE WORK
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