KCC 2021
Current Result Document :
| 한글제목(Korean Title) |
Towards Reliable Communications for Vehicle-to-Vehicle Networks: A Neural Networks-based Approach |
| 영문제목(English Title) |
Towards Reliable Communications for Vehicle-to-Vehicle Networks: A Neural Networks-based Approach |
| 저자(Author) |
Madyan Alsenwi
Shashi Raj Pandey
Seong-Bae Park
Choong Seon Hong
|
| 원문수록처(Citation) |
VOL 48 NO. 01 PP. 1249 ~ 1251 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
This paper proposes an intelligent resource allocation framework for Vehicle-to-Vehicle (V2V) communications, where the Base Station (BS) allocates orthogonal frequency resources to each V2V link. Unlike the non-orthogonal approach, allocating orthogonal Resource Blocks (RBs) to each V2V link mitigates the interference and this improves the system reliability. To achieve that, we leverage the Hopfield Neural Networks (HNNs) to design a resource allocation mechanism considering the channel state of each V2V link and the network traffic. Simulation results validate the proposed resource slicing algorithm. |
| 키워드(Keyword) |
V2V communication
reliability
resource slicing
neural networks
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| 파일첨부 |
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