Edge-Aided Computing and Transmission Scheduling for LTE-U-Enabled IoT

To facilitate the deployment of private industrial Internet-of-Things (IoT), applying long-term-evolution (LTE) over unlicensed spectrum (LTE-U) is a promising technology, which can deal with the licensed spectrum scarcity problem and the stringent quality-of-service (QoS) requirement via centralize...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 19; no. 12; pp. 7881 - 7896
Main Authors: He, Hongli, Shan, Hangguan, Huang, Aiping, Ye, Qiang, Zhuang, Weihua
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computing costs
constrained deep reinforcement learning
Delays
Dynamic scheduling
Edge computing
Industrial Internet of Things
Internet of Things
LTE over unlicensed
Machine learning
Markov processes
Mobile edge computing
Power consumption
Processor scheduling
Servers
Task analysis
task offloading
Task scheduling
Wireless communication
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:To facilitate the deployment of private industrial Internet-of-Things (IoT), applying long-term-evolution (LTE) over unlicensed spectrum (LTE-U) is a promising technology, which can deal with the licensed spectrum scarcity problem and the stringent quality-of-service (QoS) requirement via centralized control. In this paper, we investigate the computing offloading problem for LTE-U-enabled IoT, where computing tasks on an IoT device are either executed locally or offloaded to the edge server on an LTE-U base station. Considering a constrained edge computing cost (e.g., operation power consumption) for offloaded tasks, the task scheduling problem is formulated as a constrained Markov decision process (CMDP) to maximize the long-term average reward, which integrates both task completion profit and task completion delay. In order to address the uncertainty of task arrivals and channel availability, a constrained deep Q-learning-based task scheduling algorithm with provable convergence is proposed, where an adaptive reward function can appropriately bound the average edge computing cost. Extensive simulation results show that the proposed scheme considerably enhances the system performance.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2020.3017207