Low-Overhead Wireless Uplink Scheduling for Large-Scale Internet-of-Things

With the rapid growth of Internet-of-Things (IoT) applications in recent years, there is a strong need for wireless uplink scheduling algorithms that determine when and which subset of a large number of users should transmit to the central controller. Different from the downlink case, the central co...

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Bibliographic Details
Published in:IEEE transactions on mobile computing Vol. 20; no. 2; pp. 577 - 587
Main Authors: Li, Bin, Liu, Jia, Ji, Bo
Format: Magazine Article
Language:English
Published: Los Alamitos IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Controllers
Internet of Things
low-complexity algorithm
Sampling
Schedules
Scheduling
Scheduling algorithms
throughput performance
Uplink
uplink scheduling
Wireless communication
Wireless sensor networks
ISSN:1536-1233, 1558-0660
Online Access:Get full text
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Summary:With the rapid growth of Internet-of-Things (IoT) applications in recent years, there is a strong need for wireless uplink scheduling algorithms that determine when and which subset of a large number of users should transmit to the central controller. Different from the downlink case, the central controller in the uplink scenario typically has very limited information about the users. On the other hand, periodically collecting all such information from a large number of users typically incurs a prohibitively high communication overhead. This motivates us to investigate the development of an efficient and low-overhead uplink scheduling algorithm that is suitable for large-scale IoT applications. Specifically, we first characterize a capacity outer bound subject to the sampling constraint where only a small subset of users are allowed to use control channels for system state reporting at each time. Next, we relax the sampling constraint and propose a joint sampling and transmission algorithm, which utilizes full knowledge of channel state distributions and instantaneous queue lengths to achieve the capacity outer bound. The insights obtained from this capacity-achieving algorithm allow us to develop a low-overhead scheduling algorithm that can strictly satisfy the sampling constraint with asymptotically diminishing throughput loss.
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ISSN:1536-1233
1558-0660
DOI:10.1109/TMC.2019.2949291