Transportation infrastructures integrate advanced Information Technology to enable the operations and management of transportation systems. Advanced vehicle and road systems of a comprehensive concept that improve the efficiency and safety of road traffic are being built and will be commercially available through Intelligent Transport Systems (ITS) technology and services. To support ITS technology, Vehicle-To-Everything (V2X) is needed, and in the 3rd Generation Partnership Project, many studies have focused on Long Term Evolution (LTE)-based vehicle communications. In order to realize reliable and optimized communications performance in vehicle communications, which move in propagation environments at high speed, in this study, we propose a novel channel estimation scheme suited for LTE sidelink-based Vehicle-To-Vehicle systems. Conventional channel estimation schemes can be categorized as Decision-Directed Channel Estimation, spectral temporal averaging, and smoothing methods. In this study, unlike conventional channel estimation schemes, we propose a Novel Interference Cancellation of Channel Estimation (NICCE) using Quadratic Smoothing of the pilot symbols, which estimates a channel with greater accuracy, and a novel interference cancellation of channel estimation in data symbols. In simulation results, the proposed NICCE scheme shows improved overall performance in terms of the Normalized Mean Square Error and uncoded Bit Error Rate relative to conventional schemes.
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Acknowledgements This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R2718-16-0011) supervised by the IITP (Institute for Information and communications Technology Promotion). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059397). This study was financially supported by Chonnam National University (Grant No. 2016-2503).
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