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open-access-imgOpen AccessLeveraging high‐order statistics and classification in frame timing estimation for reliable vehicle‐to‐vehicle communications
Author(s)
Zhen Li,
Qin Hao,
Song Bin,
Ding Rui,
Zhang Yanling
Publication year2018
Publication title
iet communications
Resource typeJournals
PublisherThe Institution of Engineering and Technology
In vehicle‐to‐vehicle (V2V) communications, achieving reliable physical layer performance is a challenging task due to the highly dynamic nature of V2V propagation channels. Frame timing estimation, as one of the most critical signal processing procedures that rely on channel statistics, has to be appropriately enhanced to tackle this challenge. This study presents a novel frame timing estimation scheme based on both the available periodical preambles in IEEE 802.11p standard. By designing the fourth‐order statistics‐based correlation and differential normalisation functions, the proposed timing metric not only is capable of possessing an extensible correlation length, but also achieves the robustness to multipath effect and large carrier frequency offset. From the standpoints of hypothesis testing and classification, the proposed approach can effectively increase the distinction between correct and wrong timing indexes in terms of the class‐separability criteria, and consequently has a significantly improved timing estimation performance compared with the existing methods. Simulation results consist with theoretical analysis under the typical V2V channel model, and demonstrate that the proposed method can significantly reduce both the probabilities of false alarm and missed detection, and make the selection of a suitable threshold for frame detection much easier.
Subject(s)algorithm , biochemistry , channel (broadcasting) , chemistry , computer science , data mining , false alarm , frame (networking) , gene , machine learning , multipath propagation , real time computing , robustness (evolution) , telecommunications
Language(s)English
SCImago Journal Rank0.355
H-Index62
eISSN1751-8636
pISSN1751-8628
DOI10.1049/iet-com.2017.1066

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