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

• 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; 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
• DOI: 10.1109/TWC.2020.3017207

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.