User identification by matching radio “vision” and computer vision through means of machine learning

In the context of 5G networks, the possibility to fusion "radio vision" with "computer vision" is a must-have asset, enabler of building the extensive navigation maps through the so-called MirrorWorld. This demo will showcase an essential building block for merging the two environments: matching a user equipment's identity in video stream and in radio measurements. We demonstrate the integration of a computer vision system with a radio access network and showcase the identification of the true radio transmitter between two equipment existing in a video feed.