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.
|Title of host publication||2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2018 Jul 3|
|Event||2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Kansas City, United States|
Duration: 2018 May 20 → 2018 May 24
|Name||2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings|
|Other||2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018|
|Period||18/5/20 → 18/5/24|
Bibliographical noteFunding 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.
© 2018 IEEE.
- 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