Abstract
With the advent of the Internet-of-Things (IoT), massive machine-type communications (mMTC) have become one of the most important requirements for next generation (5G) communication systems. In the mMTC scenarios, grant-free non-orthogonal multiple access (NOMA) on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are a promising solution because many users sporadically transmit small data packets at low rates. In this paper, we propose a novel CS-MUD algorithm for the active user and data detection in the mMTC systems. The proposed scheme consists of the maximum a posteriori probability (MAP) based active user detector (MAP-AUD) and the MAP-based data detector (MAP-DD). By exchanging the extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the active user detection performance and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm dramatically enhances the active user detection performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Vehicular Technology |
| DOIs | |
| Publication status | Accepted/In press - 2018 Jun 20 |
| Externally published | Yes |
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Keywords
- active user detection
- compressive sensing-based multi-user detection
- group sparsity
- massive machine-type communications
- maximum a posteriori probability
ASJC Scopus subject areas
- Automotive Engineering
- Aerospace Engineering
- Computer Networks and Communications
- Electrical and Electronic Engineering
Cite this
MAP-based Active User and Data Detection for Massive Machine-Type Communications. / Jeong, Byeong Kook; Shim, Byonghyo; Lee, Kwang Bok.
In: IEEE Transactions on Vehicular Technology, 20.06.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - MAP-based Active User and Data Detection for Massive Machine-Type Communications
AU - Jeong, Byeong Kook
AU - Shim, Byonghyo
AU - Lee, Kwang Bok
PY - 2018/6/20
Y1 - 2018/6/20
N2 - With the advent of the Internet-of-Things (IoT), massive machine-type communications (mMTC) have become one of the most important requirements for next generation (5G) communication systems. In the mMTC scenarios, grant-free non-orthogonal multiple access (NOMA) on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are a promising solution because many users sporadically transmit small data packets at low rates. In this paper, we propose a novel CS-MUD algorithm for the active user and data detection in the mMTC systems. The proposed scheme consists of the maximum a posteriori probability (MAP) based active user detector (MAP-AUD) and the MAP-based data detector (MAP-DD). By exchanging the extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the active user detection performance and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm dramatically enhances the active user detection performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms.
AB - With the advent of the Internet-of-Things (IoT), massive machine-type communications (mMTC) have become one of the most important requirements for next generation (5G) communication systems. In the mMTC scenarios, grant-free non-orthogonal multiple access (NOMA) on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are a promising solution because many users sporadically transmit small data packets at low rates. In this paper, we propose a novel CS-MUD algorithm for the active user and data detection in the mMTC systems. The proposed scheme consists of the maximum a posteriori probability (MAP) based active user detector (MAP-AUD) and the MAP-based data detector (MAP-DD). By exchanging the extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the active user detection performance and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm dramatically enhances the active user detection performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms.
KW - active user detection
KW - compressive sensing-based multi-user detection
KW - group sparsity
KW - massive machine-type communications
KW - maximum a posteriori probability
UR - http://www.scopus.com/inward/record.url?scp=85048855541&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048855541&partnerID=8YFLogxK
U2 - 10.1109/TVT.2018.2849621
DO - 10.1109/TVT.2018.2849621
M3 - Article
AN - SCOPUS:85048855541
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
SN - 0018-9545
ER -