A Contextual Bandit Learning Based Quality Test System in 5G-Enabled IIoT

Abstract

The industrial Internet of Things (IIoT) interconnects an exponential number of industrial devices, and more flexible and low-cost communications are widely in demand. The fifth-generation (5G) communication provides two industrial-target technologies, massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC), to meet the demand. We design a 5G-aided quality test system, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short-length commands and small-size feedback to each other via URLLC. The problem is formulated as a long-term optimization one with the purpose of improving the product qualification rate. We develop a novel contextual combinatorial quality test (CC-QT) algorithm to solve the problem. We further derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.