Veni Vidi Dixi: Reliable wireless communication with depth images

Serkut Ayvaşik, H. Murat Gürsu, Wolfgang Kellerer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

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 camera(s) 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.

Original languageEnglish
Title of host publicationCoNEXT 2019 - Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies
PublisherAssociation for Computing Machinery, Inc
Pages172-185
Number of pages14
ISBN (Electronic)9781450369985
DOIs
StatePublished - 3 Dec 2019
Event15th ACM International Conference on Emerging Networking Experiments and Technologies, CoNEXT 2019 - Orlando, United States
Duration: 9 Dec 201912 Dec 2019

Publication series

NameCoNEXT 2019 - Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies

Conference

Conference15th ACM International Conference on Emerging Networking Experiments and Technologies, CoNEXT 2019
Country/TerritoryUnited States
CityOrlando
Period9/12/1912/12/19

Keywords

  • Channel estimation
  • Convolutional neural networks
  • Dataset

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