TY - JOUR
T1 - Code-Aided Channel Tracking and Decoding over Sparse Fast-Fading Multipath Channels with an Application to Train Backbone Networks
AU - Khalili, Shahrouz
AU - Feng, Jianghua
AU - Simeone, Osvaldo
AU - Tang, Jun
AU - Wen, Zheng
AU - Haimovich, Alexander M.
AU - Zhou, Mengchu
PY - 2017/3/1
Y1 - 2017/3/1
N2 - In a fast-fading environment, e.g., high-speed railway communications, channel estimation and tracking require the availability of a number of pilot symbols that is at least as large as the number of independent channel parameters. Aiming at reducing the number of necessary pilot symbols, this work proposes a novel technique for joint channel tracking and decoding, which is based on the following three ideas. 1) Sparsity: While the total number of channel parameters to be estimated is large, the actual number of independent multipath components is generally small; 2) Long-Term versus short-Term channel parameters: Each multipath component is typically characterized by long-Term parameters that slowly change with respect to the duration of a transmission time slot, such as delays or average power values, and by fast-varying fading amplitudes; and 3) Code-Aided methods: Decision-feedback techniques can optimally leverage past, and partially reliable, decisions on the data symbols to obtain 'virtual' pilots via the expectation-maximization (EM) algorithm. Numerical results show that the proposed code-Aided EM algorithm is effective in performing joint channel tracking and decoding even for velocities as high as 350 km/h, as in high-speed railway communications, and with as few as four pilots per orthogonal frequency-division multiplexing data symbol, as in the IEEE 802.11a/n/p standards, outperforming existing schemes at the cost of larger computational complexity.
AB - In a fast-fading environment, e.g., high-speed railway communications, channel estimation and tracking require the availability of a number of pilot symbols that is at least as large as the number of independent channel parameters. Aiming at reducing the number of necessary pilot symbols, this work proposes a novel technique for joint channel tracking and decoding, which is based on the following three ideas. 1) Sparsity: While the total number of channel parameters to be estimated is large, the actual number of independent multipath components is generally small; 2) Long-Term versus short-Term channel parameters: Each multipath component is typically characterized by long-Term parameters that slowly change with respect to the duration of a transmission time slot, such as delays or average power values, and by fast-varying fading amplitudes; and 3) Code-Aided methods: Decision-feedback techniques can optimally leverage past, and partially reliable, decisions on the data symbols to obtain 'virtual' pilots via the expectation-maximization (EM) algorithm. Numerical results show that the proposed code-Aided EM algorithm is effective in performing joint channel tracking and decoding even for velocities as high as 350 km/h, as in high-speed railway communications, and with as few as four pilots per orthogonal frequency-division multiplexing data symbol, as in the IEEE 802.11a/n/p standards, outperforming existing schemes at the cost of larger computational complexity.
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U2 - 10.1109/TITS.2016.2549544
DO - 10.1109/TITS.2016.2549544
M3 - Article
AN - SCOPUS:84971486979
SN - 1524-9050
VL - 18
SP - 481
EP - 492
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 3
M1 - 7475874
ER -