TY - GEN
T1 - Comprehensive grant-free random access for massive & low latency communication
AU - Abebe, Ameha T.
AU - Kang, Chung G.
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the ICT R&D program of MSIP/IITP [14-000-04-001, Development of 5G Mobile Communication Technologies for Hyper-connected Smart Services] & [B0194-15-1002, Korea-China bilateral and international collaboration of mmWave core technologies for 5G standardization.]
PY - 2017/7/28
Y1 - 2017/7/28
N2 - In this paper, we introduce a comprehensive grant-free random access scheme for machine-type communication which is characterized by massive connectivity and low latency. The scheme presented in here is comprehensive in a sense that, synchronization, channel estimation, and users identification & data detection (multi-user detection) are performed all in a single shot. The scheme employs compressive sensing by exploiting two sparse phenomena: sparsity in users activity and sparsity in multi-path channel. Furthermore, the scheme is designed in such a way that channel estimation and multi-user detection have a bi-directional mutual relationship, enabling one to reinforce the other for accurate detection and estimation. Moreover, the iterative order recursive least square (IORLS) estimation algorithm is modified & employed in such a way that it exploits the joint structure in multi-path channel and multi-user signal sparsity.
AB - In this paper, we introduce a comprehensive grant-free random access scheme for machine-type communication which is characterized by massive connectivity and low latency. The scheme presented in here is comprehensive in a sense that, synchronization, channel estimation, and users identification & data detection (multi-user detection) are performed all in a single shot. The scheme employs compressive sensing by exploiting two sparse phenomena: sparsity in users activity and sparsity in multi-path channel. Furthermore, the scheme is designed in such a way that channel estimation and multi-user detection have a bi-directional mutual relationship, enabling one to reinforce the other for accurate detection and estimation. Moreover, the iterative order recursive least square (IORLS) estimation algorithm is modified & employed in such a way that it exploits the joint structure in multi-path channel and multi-user signal sparsity.
KW - compressive sensing
KW - machine-type communication
KW - multi-user detection
KW - sparse channel detection
KW - synchronization
UR - http://www.scopus.com/inward/record.url?scp=85020206449&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020206449&partnerID=8YFLogxK
U2 - 10.1109/ICC.2017.7996932
DO - 10.1109/ICC.2017.7996932
M3 - Conference contribution
AN - SCOPUS:85020206449
T3 - IEEE International Conference on Communications
BT - 2017 IEEE International Conference on Communications, ICC 2017
A2 - Debbah, Merouane
A2 - Gesbert, David
A2 - Mellouk, Abdelhamid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Communications, ICC 2017
Y2 - 21 May 2017 through 25 May 2017
ER -