Veni Vidi Dixi: Reliable Wireless Communication with Depth Images
| Autor: | Wolfgang Kellerer, Serkut Ayvaşık, H. Murat Gursu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Networking and Internet Architecture (cs.NI)
FOS: Computer and information sciences Computer Science - Machine Learning business.industry Computer science Information Theory (cs.IT) Computer Vision and Pattern Recognition (cs.CV) Reliability (computer networking) Computer Science - Information Theory 020208 electrical & electronic engineering Real-time computing Computer Science - Computer Vision and Pattern Recognition 020206 networking & telecommunications Mobile robot 02 engineering and technology Automation Convolutional neural network Machine Learning (cs.LG) Computer Science - Networking and Internet Architecture Transmission (telecommunications) 0202 electrical engineering electronic engineering information engineering Wireless business Wireless sensor network Communication channel |
| Zdroj: | CoNEXT |
| Popis: | 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. Accepted for publication in CoNext 2019 with reproducibility badges. The measurements and the processing codes are available at https://gitlab.lrz.de/lkn_measurements/vvd_measurements for your evaluation |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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