TY - GEN
T1 - A Compressive Sensing-Based Active User and Symbol Detection Technique for Massive Machine-Type Communications
AU - Jeong, Byeong Kook
AU - Shim, Byonghyo
AU - Lee, Kwang Bok
N1 - Funding Information:
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.2016-0-000209).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - In massive machine-type communication (mMTC) systems, a large number of machine-type devices sporadically transmit small packets with low rates. By exploiting the sporadic activity of machine-type devices, we can cast the detection problem as the compressive sensing-based multi-user detection (CS-MUD). In this paper, we propose a novel CS-MUD algorithm for the active user and symbol detection based on a maximum a posteriori probability (MAP) criterion. By exchanging extrinsic information between active user detector and symbol detector, the proposed algorithm improves the performance of active user detection and the reliability of symbol estimate. Numerical simulations demonstrate that the proposed algorithm achieves outstanding MUD performance.
AB - In massive machine-type communication (mMTC) systems, a large number of machine-type devices sporadically transmit small packets with low rates. By exploiting the sporadic activity of machine-type devices, we can cast the detection problem as the compressive sensing-based multi-user detection (CS-MUD). In this paper, we propose a novel CS-MUD algorithm for the active user and symbol detection based on a maximum a posteriori probability (MAP) criterion. By exchanging extrinsic information between active user detector and symbol detector, the proposed algorithm improves the performance of active user detection and the reliability of symbol estimate. Numerical simulations demonstrate that the proposed algorithm achieves outstanding MUD performance.
KW - Compressive sensing-based multi-user detection
KW - Massive machine-type communications
KW - Maximum a posteriori probability
UR - http://www.scopus.com/inward/record.url?scp=85053780261&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8462195
DO - 10.1109/ICASSP.2018.8462195
M3 - Conference contribution
AN - SCOPUS:85053780261
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6623
EP - 6627
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -