A CNN-Based Structured Light Communication Scheme for Internet of Underwater Things Applications

Abstract

Underwater optical wireless communication is an emerging field that can provide reliable connectivity for future generation internet of underwater things devices. In this paper, we propose a communication system based on single and superposition of Laguerre Gaussian modes to transfer information and rely on a convolutional neural network for the mode identification in an underwater environment. A 100% recovery fidelity is reported at clear and turbid water. Beyond 90% of identification, accuracy is achieved under different laboratory-emulated underwater turbulence conditions. The practical implementation of the proposed spatial-mode based communication scheme is further discussed.

涉水光学

“…经调制后的声波经过长距离水下传输后到达接收端. 接收端安装有水声通 水下无线光通信 (underwater wireless optical communication, UWOC) 是近年来发展起来的一种 变革性的水下信息传输方式 [69] 近年来, UWOC 在水下物联网 (Internet of underwater Things, IoUT)、水下矿产勘探、水下机器 人通信、水下传感网络等领域展示出了广泛的应用前景 [71] . 然而, 涉水环境的不可预测性对 UWOC 链路的设计和部署造成了严重的困难, 例如信道衰减、信道散射、水下湍流、温度变化、盐度变化 等 [72] .…”

Theory and Practice of Orbital Angular Momentum and Beyond

“…Mode index modulation is also promising for harsh free space and underwater environments, as demonstrated in [28], [29]. The mode index modulation concept is still limited by how many beams can be generated by the transmitter, the highest beam order that can be collected by the receiving optics, and the maximum number of shapes that can be correctly distinguished by the used ML algorithm.…”

A Deep Echo State Network-Based Novel Signal Processing Approach for Underwater Wireless Optical Communication System with PAM and OFDM Signals

“…Beside these conventional equalization algorithms, with the fast advancement of machine learning technologies in recent years, neural networks have been employed in optical communication networks [22][23][24][27][28][29][30] to assist the signal processing and enhance the system's ability to resist both linear and non-linear distortion. For example, in [29], an equalizer for an underwater visible light communication (UVLC) system was built by training a convolution-enhanced long short-term memory (CE-LSTM) neural network to approximate the correct mapping from delayed channel output to originally transmitted symbols.…”