TY - JOUR
T1 - Toward Massive Connectivity for IoT in Mixed-ADC Distributed Massive MIMO
AU - Yuan, Jide
AU - He, Qi
AU - Matthaiou, Michail
AU - Quek, Tony Q.S.
AU - Jin, Shi
N1 - Funding Information:
Manuscript received April 14, 2019; revised November 12, 2019; accepted November 27, 2019. Date of publication December 3, 2019; date of current version March 12, 2020. The work of Jide Yuan and Shi Jin was supported in part by the National Science Foundation of China (NSFC) under Grant 61531011 and in part by the NSFC for Distinguished Young Scholars of China with under Grant 61625106. The work of Michail Matthaiou was supported in part by the RAEng/The Leverhulme Trust Senior Research Fellowship LTSRF1718\14\2. The work of Tony Q. S. Quek was supported in part by the Singapore University of Technology and Design-Zhejiang University (SUTD-ZJU) Research Collaboration under Grant SUTDZJU/RES/01/2016 and in part by the SUTD-ZJU Research Collaboration under Grant SUTD-ZJU/RES/05/2016. This article was presented in part at the Asilomar Conference on Signals, Systems, and Computers, October 2018. (Corresponding author: Shi Jin.) Jide Yuan and Shi Jin are with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (e-mail: yuanjide@seu.edu.cn; jinshi@seu.edu.cn).
Publisher Copyright:
© 2014 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - Massive connectivity is a key requirement for the Internet of Things (IoT). In practice, the network should be capable of accommodating thousands of devices and meeting their traffic demands. In this article, we consider the access phase for IoT in a mixed-analog-to-digital converter distributed massive multiple-input-multiple-output system, in which users are classified into light-load users and heavy-load users depending on their traffic load requirements. To meet the low-latency and low-cost demands in IoT, the access scheme for both types of users are designed in a grant-free fashion. For users with light-load traffic demands, by formulating the user activity detection (UAD) and channel estimation (CE) into a compressed sensing problem, we provide a low-complexity algorithm solver which requires no prior information. The simulation results verify that the proposed algorithm can effectively detect user activity and estimate channel state information (CSI) between the users and access points (APs). To satisfy the throughput requirements of heavy-load users, after UAD and CE, a two-step dynamic clustering is proposed for coordinated multipoint transmission using the large-scale fading (LSF) information. The impact of quantization noise on LSF estimation is investigated, as well as, a corresponding compensation method and accuracy bound. By detecting the clustering behavior among users in the first step, the complexity of the joint user and AP clustering is substantially reduced. The numerical results reveal that the proposed algorithm can offer significant performance gains in various scenarios with fast convergence.
AB - Massive connectivity is a key requirement for the Internet of Things (IoT). In practice, the network should be capable of accommodating thousands of devices and meeting their traffic demands. In this article, we consider the access phase for IoT in a mixed-analog-to-digital converter distributed massive multiple-input-multiple-output system, in which users are classified into light-load users and heavy-load users depending on their traffic load requirements. To meet the low-latency and low-cost demands in IoT, the access scheme for both types of users are designed in a grant-free fashion. For users with light-load traffic demands, by formulating the user activity detection (UAD) and channel estimation (CE) into a compressed sensing problem, we provide a low-complexity algorithm solver which requires no prior information. The simulation results verify that the proposed algorithm can effectively detect user activity and estimate channel state information (CSI) between the users and access points (APs). To satisfy the throughput requirements of heavy-load users, after UAD and CE, a two-step dynamic clustering is proposed for coordinated multipoint transmission using the large-scale fading (LSF) information. The impact of quantization noise on LSF estimation is investigated, as well as, a corresponding compensation method and accuracy bound. By detecting the clustering behavior among users in the first step, the complexity of the joint user and AP clustering is substantially reduced. The numerical results reveal that the proposed algorithm can offer significant performance gains in various scenarios with fast convergence.
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U2 - 10.1109/JIOT.2019.2957281
DO - 10.1109/JIOT.2019.2957281
M3 - Article
AN - SCOPUS:85082136282
VL - 7
SP - 1841
EP - 1856
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
SN - 2327-4662
IS - 3
M1 - 8920097
ER -