Abstract
In massive machine-type communication (mMTC), by utilizing sporadic device activities, compressed sensing based multi-user detection (CS-MUD) can be used to recover sparse multi-user vectors in the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, the channel state information (CSI) between each active device and the basestation should be estimated before the symbol detection. In this paper, we propose a novel Bayesian joint active user detection (AUD) and channel estimation (CE) method based on the expectation propagation (EP) algorithm. The proposed method finds the best Gaussian approximation for the computationally intractable posterior distribution of the sparse channel vector using iterative EP parameter update rules. Using the approximated distribution, identification and CSI estimation of active devices are jointly performed. We show from numerical simulations that the proposed technique greatly improves the performance of AUD and CE.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538643280 |
| DOIs | |
| Publication status | Published - 2018 Jul 3 |
| Externally published | Yes |
| Event | 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Kansas City, United States Duration: 2018 May 20 → 2018 May 24 |
Other
| Other | 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 |
|---|---|
| Country | United States |
| City | Kansas City |
| Period | 18/5/20 → 18/5/24 |
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Keywords
- Active user detection
- Channel estimation
- Compressed sensing
- Expectation propagation
- Massive machine-type communication
- Nonorthogonal multiple access
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
Cite this
Expectation propagation-based active user detection and channel estimation for massive machine-type communications. / Ahn, Jinyoup; Shim, Byonghyo; Lee, Kwang Bok.
2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Expectation propagation-based active user detection and channel estimation for massive machine-type communications
AU - Ahn, Jinyoup
AU - Shim, Byonghyo
AU - Lee, Kwang Bok
PY - 2018/7/3
Y1 - 2018/7/3
N2 - In massive machine-type communication (mMTC), by utilizing sporadic device activities, compressed sensing based multi-user detection (CS-MUD) can be used to recover sparse multi-user vectors in the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, the channel state information (CSI) between each active device and the basestation should be estimated before the symbol detection. In this paper, we propose a novel Bayesian joint active user detection (AUD) and channel estimation (CE) method based on the expectation propagation (EP) algorithm. The proposed method finds the best Gaussian approximation for the computationally intractable posterior distribution of the sparse channel vector using iterative EP parameter update rules. Using the approximated distribution, identification and CSI estimation of active devices are jointly performed. We show from numerical simulations that the proposed technique greatly improves the performance of AUD and CE.
AB - In massive machine-type communication (mMTC), by utilizing sporadic device activities, compressed sensing based multi-user detection (CS-MUD) can be used to recover sparse multi-user vectors in the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, the channel state information (CSI) between each active device and the basestation should be estimated before the symbol detection. In this paper, we propose a novel Bayesian joint active user detection (AUD) and channel estimation (CE) method based on the expectation propagation (EP) algorithm. The proposed method finds the best Gaussian approximation for the computationally intractable posterior distribution of the sparse channel vector using iterative EP parameter update rules. Using the approximated distribution, identification and CSI estimation of active devices are jointly performed. We show from numerical simulations that the proposed technique greatly improves the performance of AUD and CE.
KW - Active user detection
KW - Channel estimation
KW - Compressed sensing
KW - Expectation propagation
KW - Massive machine-type communication
KW - Nonorthogonal multiple access
UR - http://www.scopus.com/inward/record.url?scp=85050293625&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050293625&partnerID=8YFLogxK
U2 - 10.1109/ICCW.2018.8403604
DO - 10.1109/ICCW.2018.8403604
M3 - Conference contribution
AN - SCOPUS:85050293625
SP - 1
EP - 6
BT - 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -