An Empirical Study of Deep Learning Models for LED Signal Demodulation in Optical Camera Communication
An Empirical Study of Deep Learning Models for LED Signal Demodulation in Optical Camera Communication
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DOI：
10.3390/network1030016
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Abdul Haseeb Ahmed;Sethuraman Trichy Viswanathan;Md. Rashed Rahman;A. Ashok
中科院分区：
文献类型：
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作者：
Abdul Haseeb Ahmed;Sethuraman Trichy Viswanathan;Md. Rashed Rahman;A. Ashok
光学相机通信是一种新兴技术，它使用光束进行通信，其中信息通过发光二极管(LED)的光传输进行调制。这项工作进行了实证研究，以确定在相机通信中使用深度学习模型来改善信号接收的可行性和有效性。这项工作的主要贡献包括研究转移学习和定制现有模型，通过在接收端将分类模型应用于摄像机帧来解调使用单个LED传输的信号。除了研究用于解调单个VLC传输的深度学习方法外，本工作还评估了在视觉多输入多输出(MIMO)中整合深度学习的两个真实使用案例，其中来自LED阵列的传输在相机接收器上被解码。本文对计算机视觉中传统用于摄像机通信的深度神经网络(DNN)体系结构进行了经验评估。
Optical camera communication is an emerging technology that enables communication using light beams, where information is modulated through optical transmissions from light-emitting diodes (LEDs). This work conducts empirical studies to identify the feasibility and effectiveness of using deep learning models to improve signal reception in camera communication. The key contributions of this work include the investigation of transfer learning and customization of existing models to demodulate the signals transmitted using a single LED by applying the classification models on the camera frames at the receiver. In addition to investigating deep learning methods for demodulating a single VLC transmission, this work evaluates two real-world use-cases for the integration of deep learning in visual multiple-input multiple-output (MIMO), where transmissions from a LED array are decoded on a camera receiver. This paper presents the empirical evaluation of state-of-the-art deep neural network (DNN) architectures that are traditionally used for computer vision applications for camera communication.