Tsai Deng-Cheng, Chang Yun-Han, Chow Chi-Wai, Liu Yang, Yeh Chien-Hung, Peng Ching-Wei, Hsu Li-Sheng
Opt Express. 2022 May 9;30(10):16069-16077. doi: 10.1364/OE.449860.
We demonstrate an optical-camera-communication (OCC) system utilizing a laser-diode (LD) coupled optical-diffusing-fiber (ODF) transmitter (Tx) and rolling-shutter based image sensor receiver (Rx). The ODF is a glass optical fiber produced for decorative lighting or embedded into small areas where bulky optical sources cannot fit. Besides, decoding the high data rate rolling-shutter pattern from the thin ODF Tx is very challenging. Here, we propose and experimentally demonstrate the pixel-row-per-bit based neural-network (PPB-NN) to decode the rolling-shutter-pattern emitted by the thin ODF Tx. The proposed PPB-NN algorithm is discussed. The proposed PPB-NN method can satisfy the pre-forward error correction (FEC) BER at data rate of 3,300 bit/s at a transmission distance of 35 cm. Theoretical analysis of the maximum ODF Tx angle is also discussed; and our experimental values agree with our theoretical results.
我们展示了一种光相机通信(OCC)系统,该系统利用激光二极管(LD)耦合光扩散光纤(ODF)发射器(Tx)和基于滚动快门的图像传感器接收器(Rx)。ODF是一种用于装饰照明或嵌入到大尺寸光源无法适配的小区域的玻璃光纤。此外,从细ODF Tx解码高数据速率滚动快门图案极具挑战性。在此,我们提出并通过实验证明了基于每像素行逐位的神经网络(PPB-NN)来解码细ODF Tx发射的滚动快门图案。讨论了所提出的PPB-NN算法。所提出的PPB-NN方法在35厘米的传输距离下,以3300比特/秒的数据速率能够满足前向纠错(FEC)前的误码率(BER)要求。还讨论了ODF Tx最大角度的理论分析;并且我们的实验值与理论结果相符。