TY - JOUR
T1 - Compressive Channel Estimation and User Activity Detection in Distributed-Input Distributed-Output Systems
AU - He, Qi
AU - Chen, Zhi
AU - Quek, Tony Q.S.
AU - Choi, Jinho
AU - Li, Shaoqian
N1 - Funding Information:
Manuscript received June 5, 2018; revised July 13, 2018; accepted July 17, 2018. Date of publication July 23, 2018; date of current version September 8, 2018. This work was supported in part by the Important National Science and Technology Specific Projects of China under Grant 2018ZX03001001, and in part by the Science and Technology on Communication Networks Laboratory Fund. The associate editor coordinating the review of this letter and approving it for publication was M. Egan. (Corresponding author: Zhi Chen.) Q. He, Z. Chen, and S. Li are with the Science and Technology on Communication Networks Laboratory, National Key Laboratory on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: heqi.tech@hotmail.com; chenzhi@ uestc.edu.cn; lsq@uestc.edu.cn).
PY - 2018/9
Y1 - 2018/9
N2 - We address the cloud radio access network with wireless fronthaul links for massive machine-type communication as a distributed-input distributed-output (DIDO) system for simplicity. In this letter, the channel estimation and user activity detection problems in the DIDO system are studied. We notice that there are two types of sparsity in DIDO systems: The first is the sparsity of user equipment (UE) activities, and the second is the spatial sparsity of UE signals. In response, a two-stage compressed sensing process is proposed in which UE activities and the overall channel states from active UEs to the baseband unit pool are identified at the first stage, and channel states from active UEs to remote radio heads are estimated at the second stage. A low-complexity method is proposed to accelerate the process in the first stage. Simulation results are also presented to show the performance of the proposed approach.
AB - We address the cloud radio access network with wireless fronthaul links for massive machine-type communication as a distributed-input distributed-output (DIDO) system for simplicity. In this letter, the channel estimation and user activity detection problems in the DIDO system are studied. We notice that there are two types of sparsity in DIDO systems: The first is the sparsity of user equipment (UE) activities, and the second is the spatial sparsity of UE signals. In response, a two-stage compressed sensing process is proposed in which UE activities and the overall channel states from active UEs to the baseband unit pool are identified at the first stage, and channel states from active UEs to remote radio heads are estimated at the second stage. A low-complexity method is proposed to accelerate the process in the first stage. Simulation results are also presented to show the performance of the proposed approach.
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U2 - 10.1109/LCOMM.2018.2858241
DO - 10.1109/LCOMM.2018.2858241
M3 - Article
AN - SCOPUS:85050407609
VL - 22
SP - 1850
EP - 1853
JO - IEEE Communications Letters
JF - IEEE Communications Letters
SN - 1089-7798
IS - 9
M1 - 8417429
ER -