ML-based estimation of the number of devices in industrial networks using unlicensed bands

Advanced automation is being adopted by manu-facturing facilities and wireless technologies are set to be a key component in driving the factories of the future. It is expected that private cellular networks and WLAN technologies would be deployed for smart factory operations. Since both wireless technologies can operate on the same channel in unlicensed bands, then efficient resource sharing becomes important. When multiple devices compete for the resource, the estimation of number of devices contending for the channel resource can help the design of an efficient resource sharing scheme. This paper aims to address the challenge of estimating the number of factory devices contending to transmit over the unlicensed channel. We adopt three machine learning (ML) techniques and develop a novel device number estimation system by collating and analysing the idle-time interval between transmission across the channel. By using NS-3 simulation, the performance of the proposed estimation approach is evaluated. The results presented reveal the significance of the chosen features and performance of each ML algorithm used.