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Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 39,
  • Issue 8,
  • pp. 2368-2375
  • (2021)

A Hybrid Radio-Optical Wireless System With Efficient Sub-Centimeter Localization for Full-Coverage Indoor Services

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Abstract

Wireless transmission capacity demands are continuously growing. Infrared optical wireless communication (IR-OWC) is considered as a promising solution for the future high-speed indoor applications because it can easily offer more than 10 GHz bandwidth with commercially available devices of the fiber-optic communication eco-systems. An IR-OWC cell employing narrow beam widths requires powerful beam-steering and localization functions to dynamically pinpoint the optical beams to different user devices (UDs), and typically has a limited service area. Powerful localization functions with high accuracy typically require sophisticated hardware and resource- and time-consuming signal processing, which may cause serious economical burdens to the communication system. Besides, in regard of a system with extended service area, it may be comprehensive and thus expensive to fully cover the whole space by densely spaced IR-OWC cells. In this article, we introduce a hybrid radio-optical wireless system, which offers high speed IR-OWC services at specific hotspots, and covers spatial gaps among the IR-OWC cells through broadcasting of Wi-Fi signals. This balances the requirements among capacity, coverage, and cost. For each IR-OWC cell, image feature matching based on processing with additional differential operations is proposed to more efficiently process the signals. The images are captured by IP cameras, and can be transparently transported over the communication system through Wi-Fi. Based on our proposal, a 10 Gb/s automatically beam-steered IR-OWC system integrated with ∼80 Mb/s WiFi and with localization function is designed and discussed. An independent test showing 0.24-cm localization accuracy for a 1-cm UD aperture over 180-cm distance is performed. Our processing time shows ∼50% efficiency improvement to the speeded up robust features (SURF) method, and has less recognition uncertainties.

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