Gsnake: A lightweight SNR optimization algorithm for practical optical camera communication

Abstract

Exploring light-to-camera wireless communication (a.k.a. Optical Camera Communication (OCC)) has been a hot research direction in recent years, the breakthrough and commercialization of its key technology can not only solve the spectrum crunch in traditional radio frequency based communication but also revolutionize the next generation of green communications. However, OCC is still trapped by several serious challenges in realistic complex application scenarios (such as exhibits and museums), where they need reflected OCC, but the reflective surfaces are not regular and smooth. Furthermore, the communication performance of the reflected OCC is seriously degraded by the color and texture of the reflective surface in realistic scenarios. To this end, many researchers, including us, have done systematic and deep research to address above issues. In this work, we propose a novel algorithm namely Gsnake to overcome the bottleneck problem of OCC technology in complex scenarios and further improve the performance and practicability of OCC. Essentially, the proposed scheme can search and select pixels in the high SNR (Signal-to-Noise Ratio) region projected by the LED emitter so as to maximize the received signal strength, whereas the conventional ones normally only select a fixed row of pixels. By deploying the algorithm in a real-world application, the experimental results verify the efficacy and usability of the proposed scheme, which is superior to existing algorithms.