TY - JOUR
T1 - Spatiotemporal Modeling of Massive MIMO Systems with Mixed-Type IoT Devices
T2 - Scheduling Optimization with Delay Constraints
AU - Zhang, Qi
AU - Yang, Howard H.
AU - Quek, Tony Q.S.
AU - Jin, Shi
N1 - Funding Information:
Manuscript received August 13, 2020; revised December 13, 2020; accepted January 6, 2021. Date of publication January 12, 2021; date of current version June 7, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61801244; in part by the Natural Science Foundation of Jiangsu Province under Grant BK20180754 and Grant BK20191378; in part by the Initial Scientic Research Foundation of NJUPT under Grant NY218103; and in part by the National Science Research Project of Jiangsu Higher Education Institutions under Grant 18KJB510034. The work of H. H. Yang was supported in part by the Zhejiang University/University of Illinois at Urbana–Champaign Institute Starting Fund. The work of T. Q. S. Quek was supported in part by the MOE ARF Tier 2 under Grant T2EP20120-0006. The work of S. Jin was supported in part by the National Science Foundation of China under Grant 61921004. (Corresponding authors: Qi Zhang and Howard H. Yang.) Qi Zhang is with the Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China (e-mail: zhangqi1212@njupt.edu.cn).
Publisher Copyright:
© 2014 IEEE.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - In this article, we develop a framework for the analysis of massive multiple-input-multiple-output (MIMO) systems where multiple types of devices with different configurations and requirements co-exist, by taking into account the randomness of spatial locations and temporal traffic. A tight closed-form approximation of the spatial mean packet throughput, which denotes the average number of packets that are successfully transmitted at any unit time slot and area is derived, by using tools from the stochastic geometry and queuing theory, which captures all the key features of the devices in the Internet of Things (IoT). Based on the analysis, we investigate the optimal scheduling number for each type of devices that maximizes the spatial mean packet throughput while meeting devices' delay constraints. It is found that when the base station (BS) has excessive number of antennas ( M ), the BS should schedule all devices under its coverage, regardless of devices' variances on spatiotemporal configurations and demands. However, when M is limited, the BS should have a bias on scheduling devices with heavier traffic, lower decoding threshold, or higher transmit power. On this basis, if the delay constraint of one device becomes stricter, it will be scheduled more often to access the radio channel, which acts more significantly when the ratio of M to the deployment density of devices gets smaller.
AB - In this article, we develop a framework for the analysis of massive multiple-input-multiple-output (MIMO) systems where multiple types of devices with different configurations and requirements co-exist, by taking into account the randomness of spatial locations and temporal traffic. A tight closed-form approximation of the spatial mean packet throughput, which denotes the average number of packets that are successfully transmitted at any unit time slot and area is derived, by using tools from the stochastic geometry and queuing theory, which captures all the key features of the devices in the Internet of Things (IoT). Based on the analysis, we investigate the optimal scheduling number for each type of devices that maximizes the spatial mean packet throughput while meeting devices' delay constraints. It is found that when the base station (BS) has excessive number of antennas ( M ), the BS should schedule all devices under its coverage, regardless of devices' variances on spatiotemporal configurations and demands. However, when M is limited, the BS should have a bias on scheduling devices with heavier traffic, lower decoding threshold, or higher transmit power. On this basis, if the delay constraint of one device becomes stricter, it will be scheduled more often to access the radio channel, which acts more significantly when the ratio of M to the deployment density of devices gets smaller.
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U2 - 10.1109/JIOT.2021.3051055
DO - 10.1109/JIOT.2021.3051055
M3 - Article
AN - SCOPUS:85099539872
VL - 8
SP - 10146
EP - 10159
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
SN - 2327-4662
IS - 12
M1 - 9320550
ER -