Veni Vidi Dixi: Reliable Wireless Communication with Depth Images
This addresses the need for reliable wireless sensor networks in industrial automation, though it is incremental as it builds on existing channel estimation techniques with a novel data source.
The paper tackles the problem of unreliable wireless communication in dynamic environments by proposing Veni Vidi Dixi (VVD), a method that uses depth images and CNNs to estimate wireless channels without frequent pilot transmissions, achieving increased reliability as tested in an indoor setting with a mobile human.
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.