  The upcoming industrial revolution requires deployment of critical wireless
sensor networks for automation and monitoring purposes. However, the
reliability of the wireless communication is rendered unpredictable by mobile
elements in the communication environment such as humans or mobile robots which
lead to dynamically changing radio environments. Changes in the wireless
channel can be monitored with frequent pilot transmission. However, that would
stress the battery life of sensors. In this work a new wireless channel
estimation technique, Veni Vidi Dixi, VVD, is proposed. VVD leverages the
redundant information in depth images obtained from the surveillance cameras in
the communication environment and utilizes Convolutional Neural Networks CNNs
to map the depth images of the communication environment to complex wireless
channel estimations. VVD increases the wireless communication reliability
without the need for frequent pilot transmission and with no additional
complexity on the receiver. The proposed method is tested by conducting
measurements in an indoor environment with a single mobile human. Up to authors
best knowledge our work is the first to obtain complex wireless channel
estimation from only depth images without any pilot transmission. The collected
wireless trace, depth images and codes are publicly available.
