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 | |
| 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 | Portugal |
| City | Porto |
| Period | 18/6/3 → 18/6/6 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics
Cite this
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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 proceeding › Conference contribution
TY - GEN
T1 - Channel Estimation for Uplink SCMA Systems with Reduced Training Blocks
AU - Heo, Jehyun
AU - Jung, Insik
AU - Kim, Taehyung
AU - Kim, Hyunsoo
AU - Hong, Daesik
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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UR - http://www.scopus.com/inward/citedby.url?scp=85050959313&partnerID=8YFLogxK
U2 - 10.1109/VTCSpring.2018.8417510
DO - 10.1109/VTCSpring.2018.8417510
M3 - Conference contribution
AN - SCOPUS:85050959313
T3 - IEEE Vehicular Technology Conference
SP - 1
EP - 5
BT - 2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -