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
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the Institute for Information and Communications Technology Promotion through Korea Government under grant 2016-0-00209, and LG Electronics Co. Ltd.
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
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U2 - 10.1109/ICCW.2018.8403604
DO - 10.1109/ICCW.2018.8403604
M3 - Conference contribution
AN - SCOPUS:85050293625
T3 - 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings
SP - 1
EP - 6
BT - 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018
Y2 - 20 May 2018 through 24 May 2018
ER -