초록

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Abstract ▼ AI-Helper

Transportation infrastructures integrate advanced information technology (IT) to enable the operations and management of transportation systems. Advanced vehicle and road systems of a comprehensive concept improve the efficiency and safety of road traffic. They are being built and will be commercial...

Transportation infrastructures integrate advanced information technology (IT) to enable the operations and management of transportation systems. Advanced vehicle and road systems of a comprehensive concept improve the efficiency and safety of road traffic. They 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 (3GPP), many studies have focused on long term evolution (LTE)-based vehicle communications. Though vehicle communications move in propagation environments at high speed, they should realize reliable and optimized communications performance. To do this, we propose a novel channel estimation scheme suited for LTE sidelink-based vehicle-to-vehicle (V2V) systems. Conventional channel estimation schemes can be categorized as decision-directed channel estimation (DDCE), spectral temporal averaging (STA), and smoothing methods. In this study, unlike conventional channel estimation schemes, we propose a novel interference cancellation of channel estimation (NICCE) using quadratic smoothing (QS) 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 (NMSE) and bit error rate (BER) relative to conventional schemes.

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