Reinforcement Learning-Based Computing and Transmission Scheduling for LTE-U-Enabled IoT

Abstract

To facilitate the private deployment of industrial Internet-of-Things (IoT), applying LTE in unlicensed spectrum (LTE-U) is a promising approach, which both tackles the problem of lacking licensed spectrum and leverages an LTE protocol to meet stringent quality-of- service (QoS) requirements via centralized control. In this paper, we investigate the computing offloading problem in an LTE-U-enabled network, where the task on an IoT device is carried out either locally or is offloaded to the LTE-U base station (BS). The offloading policy is formulated as an optimization problem to maximize the long term discounted reward, considering both task completion profit and the task completion delay. Due to the stochastic task arrival process at each device and the Wi-Fi's contention-based random access, we reformulate the computing offloading problem into a Q-learning problem and solve it by a deep learning network-based approximation method. Simulation results show that the proposed scheme considerably enhances the system performance.