Vision-Aided Reference Signal Receiving Power Prediction for Smart Factory

Yuan Feng, Feifei Gao, Xiaoming Tao, Shaodan Ma, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350303582
DOIs
StatePublished - 2024
Externally publishedYes
Event25th IEEE Wireless Communications and Networking Conference, WCNC 2024 - Dubai, United Arab Emirates
Duration: Apr 21 2024Apr 24 2024

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)1525-3511

Conference

Conference25th IEEE Wireless Communications and Networking Conference, WCNC 2024
Country/TerritoryUnited Arab Emirates
CityDubai
Period4/21/244/24/24

All Science Journal Classification (ASJC) codes

  • General Engineering

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