Vision-Aided Reference Signal Receiving Power Prediction for Smart Factory

Smart factory is a new intelligent platform requiring high throughput and millimeter wave (mmWave) technology has become an enabler for high speed communications in Industry 4.0. However, the sensitivity of mmWave signals to blockage poses serious challenges to the reliability of wireless networks in these frequency ranges. In this paper, we propose a vision-aided reference signal receiving power prediction (RSRP) framework for smart factory to avoid communications interruption caused by unexpected blockage. In particular, we design a feature extraction method to obtain communications-related features in environmental images. Then, we construct a joint image-channel dataset based on Blender and Wireless Insite software. Simulations show that the root mean square error (RMSE) of RSRP prediction 400 ms ahead reaches 2.88 dB. RSRP prediction can assist base station (BS) handover to avoid communications interruption. Hence, the proposed study provides a promising direction for enabling ultra-reliable communications under mmWave and even Terahertz bands in smart factory of Industry 4.0.