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research-article

Code-Aided Channel Tracking and Decoding Over Sparse Fast-Fading Multipath Channels With an Application to Train Backbone Networks

Authors:

Shahrouz Khalili,

Osvaldo Simeone,

Alexander M. Haimovich,

MengChu ZhouAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 18, Issue 3

Pages 481 - 492

https://doi.org/10.1109/TITS.2016.2549544

Published: 01 March 2017 Publication History

Abstract

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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Cited By

Saki MAbolhasan MLipman JJamalipour A(2021)Mobility Model for Contact-Aware Data Offloading Through Train-to-Train Communications in Rail NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.301458823:1(597-609)Online publication date: 27-Dec-2021
https://dl.acm.org/doi/10.1109/TITS.2020.3014588
Vahidi V(2021)Uplink data transmission for high speed trains in severe doubly selective channels of 6G communication systemsPhysical Communication10.1016/j.phycom.2021.10148949:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.phycom.2021.101489

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 18, Issue 3

March 2017

252 pages

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IEEE Press

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Published: 01 March 2017

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Cited By

Saki MAbolhasan MLipman JJamalipour A(2021)Mobility Model for Contact-Aware Data Offloading Through Train-to-Train Communications in Rail NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.301458823:1(597-609)Online publication date: 27-Dec-2021
https://dl.acm.org/doi/10.1109/TITS.2020.3014588
Vahidi V(2021)Uplink data transmission for high speed trains in severe doubly selective channels of 6G communication systemsPhysical Communication10.1016/j.phycom.2021.10148949:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.phycom.2021.101489

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