Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks

Jehyun Heo; Insik Jung; Taehyung Kim; Hyunsoo Kim; Daesik Hong

doi:10.1109/VTCSpring.2018.8417510

Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks

Jehyun Heo, Insik Jung, Taehyung Kim, Hyunsoo Kim, Daesik Hong

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Sparse code multiple access (SCMA) is the most prominent non-orthogonal multiple access (NOMA) scheme considered as massive connectivity. Because SCMA users transmit information using the same time-frequency resources, properly estimating each user's channel information is a challenging issue. In this paper, we propose a channel estimator in the uplink SCMA system. We design a pilot structure based on a cyclically shifted Zadoff-Chu (ZC) sequence. The proposed algorithm using the autocorrelation property of the ZC sequence separates each user's channel information and estimates each user's channel frequency response. In addition, we calculate the number of required training blocks and prove that the number of training blocks in the proposed algorithm is lower than the number of needed in conventional channel estimation techniques. In simulation results, we compare the mean squared error (MSE) of the proposed algorithm with conventional approaches.

Original language	English
Title of host publication	2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781538663554
DOIs	https://doi.org/10.1109/VTCSpring.2018.8417510
Publication status	Published - 2018 Jul 20
Event	87th IEEE Vehicular Technology Conference, VTC Spring 2018 - Porto, Portugal Duration: 2018 Jun 3 → 2018 Jun 6

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2018-June
ISSN (Print)	1550-2252

Other

Other	87th IEEE Vehicular Technology Conference, VTC Spring 2018
Country/Territory	Portugal
City	Porto
Period	18/6/3 → 18/6/6

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea(NRF) grant funded by the Korea government (NRF-2015R1A2A1A01006162) and in part by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. 2016-0-00181, Development on the core technologies of transmission, modulation and coding with low-power and low-complexity for massive connectivity in the IoT environment)

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1109/VTCSpring.2018.8417510

Cite this

Heo, J., Jung, I., Kim, T., Kim, H., & Hong, D. (2018). Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks. In 2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings (pp. 1-5). (IEEE Vehicular Technology Conference; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCSpring.2018.8417510

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abstract = "Sparse code multiple access (SCMA) is the most prominent non-orthogonal multiple access (NOMA) scheme considered as massive connectivity. Because SCMA users transmit information using the same time-frequency resources, properly estimating each user's channel information is a challenging issue. In this paper, we propose a channel estimator in the uplink SCMA system. We design a pilot structure based on a cyclically shifted Zadoff-Chu (ZC) sequence. The proposed algorithm using the autocorrelation property of the ZC sequence separates each user's channel information and estimates each user's channel frequency response. In addition, we calculate the number of required training blocks and prove that the number of training blocks in the proposed algorithm is lower than the number of needed in conventional channel estimation techniques. In simulation results, we compare the mean squared error (MSE) of the proposed algorithm with conventional approaches.",

author = "Jehyun Heo and Insik Jung and Taehyung Kim and Hyunsoo Kim and Daesik Hong",

note = "Funding Information: This work was supported in part by the National Research Foundation of Korea(NRF) grant funded by the Korea government (NRF-2015R1A2A1A01006162) and in part by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. 2016-0-00181, Development on the core technologies of transmission, modulation and coding with low-power and low-complexity for massive connectivity in the IoT environment) Publisher Copyright: {\textcopyright} 2018 IEEE.; 87th IEEE Vehicular Technology Conference, VTC Spring 2018 ; Conference date: 03-06-2018 Through 06-06-2018",

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Heo, J, Jung, I, Kim, T, Kim, H & Hong, D 2018, Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks. in 2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings. IEEE Vehicular Technology Conference, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 87th IEEE Vehicular Technology Conference, VTC Spring 2018, Porto, Portugal, 18/6/3. https://doi.org/10.1109/VTCSpring.2018.8417510

Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks. / Heo, Jehyun; Jung, Insik; Kim, Taehyung et al.

2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-5 (IEEE Vehicular Technology Conference; Vol. 2018-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: This work was supported in part by the National Research Foundation of Korea(NRF) grant funded by the Korea government (NRF-2015R1A2A1A01006162) and in part by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. 2016-0-00181, Development on the core technologies of transmission, modulation and coding with low-power and low-complexity for massive connectivity in the IoT environment) Publisher Copyright: © 2018 IEEE.

PY - 2018/7/20

Y1 - 2018/7/20

N2 - Sparse code multiple access (SCMA) is the most prominent non-orthogonal multiple access (NOMA) scheme considered as massive connectivity. Because SCMA users transmit information using the same time-frequency resources, properly estimating each user's channel information is a challenging issue. In this paper, we propose a channel estimator in the uplink SCMA system. We design a pilot structure based on a cyclically shifted Zadoff-Chu (ZC) sequence. The proposed algorithm using the autocorrelation property of the ZC sequence separates each user's channel information and estimates each user's channel frequency response. In addition, we calculate the number of required training blocks and prove that the number of training blocks in the proposed algorithm is lower than the number of needed in conventional channel estimation techniques. In simulation results, we compare the mean squared error (MSE) of the proposed algorithm with conventional approaches.

AB - Sparse code multiple access (SCMA) is the most prominent non-orthogonal multiple access (NOMA) scheme considered as massive connectivity. Because SCMA users transmit information using the same time-frequency resources, properly estimating each user's channel information is a challenging issue. In this paper, we propose a channel estimator in the uplink SCMA system. We design a pilot structure based on a cyclically shifted Zadoff-Chu (ZC) sequence. The proposed algorithm using the autocorrelation property of the ZC sequence separates each user's channel information and estimates each user's channel frequency response. In addition, we calculate the number of required training blocks and prove that the number of training blocks in the proposed algorithm is lower than the number of needed in conventional channel estimation techniques. In simulation results, we compare the mean squared error (MSE) of the proposed algorithm with conventional approaches.

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Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks

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