Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publication2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538663554
DOIs
Publication statusPublished - 2018 Jul 20
Event87th IEEE Vehicular Technology Conference, VTC Spring 2018 - Porto, Portugal
Duration: 2018 Jun 32018 Jun 6

Publication series

NameIEEE Vehicular Technology Conference
Volume2018-June
ISSN (Print)1550-2252

Other

Other87th IEEE Vehicular Technology Conference, VTC Spring 2018
CountryPortugal
CityPorto
Period18/6/318/6/6

Fingerprint

Channel Estimation
Multiple Access
Uplink
Channel estimation
Autocorrelation
Frequency response
Frequency Response
Mean Squared Error
Connectivity
Estimator
Calculate
Resources
Training
Estimate
Simulation

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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
Heo, Jehyun ; Jung, Insik ; Kim, Taehyung ; Kim, Hyunsoo ; Hong, Daesik. / Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks. 2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-5 (IEEE Vehicular Technology Conference).
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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; Kim, Hyunsoo; Hong, Daesik.

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 proceedingConference contribution

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Heo J, Jung I, Kim T, Kim H, Hong D. Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks. In 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). https://doi.org/10.1109/VTCSpring.2018.8417510