OFDM High Speed Train Communication Systems in 5G Cellular Networks
Document Type
Conference Proceeding
Publication Date
3-19-2018
Publication Title
Consumer Communications & Networking Conference (CCNC), 2018 15th IEEE Annual
First page number:
1
Last page number:
6
Abstract
A reliable high-speed wireless communication is essential for high speed trains (HSTs) in upcoming 5G communication cellular networks. Orthogonal frequency division multiplexing (OFDM) has been widely used for wideband communication standards because of its efficiency and its robustness to multipath propagations and it is a good candidate for 5G communication systems as well. However, the expected high carrier frequency of 5G, along with the high speed of HSTs, causes high Doppler shifts that results in sever inter carrier interferences (ICIs) in OFDM systems. To mitigate the ICI, the channel state information (CSI) is needed at the receiver. In this paper, the earlier proposed method for HST channel estimation in conventional communication standards is enhanced to become applicable for 5G communication systems. In the enhanced method, an accurate estimate of the Doppler shift is applied to calculate the channel tap gains. Besides that, by exploiting the estimated CSI, an adaptive channel coding scheme is proposed which employs different coding rates based on the power of the subcarriers. Simulation results indicate that our proposed channel estimation procedure estimates the CSI accurately and that CSI can enhance the fidelity of the data reconstruction considerably when it is utilized by the proposed adaptive coding scheme.
Keywords
High speed train (HST); Orthongonal frequency division; Multiplexing (OFDM); Fifth generation (5G); Channel estimation; Compressed sensing; Reed Solomon (RS)
Disciplines
Electrical and Computer Engineering
Language
English
Repository Citation
Vahidi, V.,
Saberinia, E.
(2018).
OFDM High Speed Train Communication Systems in 5G Cellular Networks.
Consumer Communications & Networking Conference (CCNC), 2018 15th IEEE Annual
1-6.
http://dx.doi.org/10.1109/CCNC.2018.8319172