Machine Learning assisted Indoor Visible Light Communications Radio Environment Maps

Visible light communications (VLC) have been proposed as reliable high-capacity wireless optical communication systems for indoor access networks. This technology comprises a solution that does not harm human tissues and has been especially suggested for ultra-reliable environments like hospitals. In this paper we utilize machine learning for investigating the wireless optical channel in indoοr areas where ultra-reliable communications are needed and hence signal coverage should be guaranteed. It is proven that ML methods can reliably and efficiently predict the Radio Environment Map (REM) of a VLC enabled communication system.