Underwater wireless optical communication (UWOC) has been proposed to provide high-rate data services by exploiting the ample optical spectra. However, the underwater scenario presents a hostile environment for wireless optical signal propagation due to the various channel effects, such as absorption, scattering and turbulence. Furthermore, link misalignment (LM) between the optical transmitter and receiver caused by the turbulent water waves degrades the achievable system performance. All the aforementioned factors make the information recovery a challenging task for UWOC systems, especially for long-distance data transmissions. In this letter, we introduce a deep learning (DL) based misalignment-robust blind receiver (MBR) to recover the received data in a multiple-input multiple-output (MIMO) UWOC system, where a convolutional neural network (CNN) is used to formulate the signal characteristics in model training, a CNN combiner is utilized for characteristic analysis and combination, and a CNN demodulator is applied to recover the transmitted information. Evaluation results demonstrate that a reliable performance is achievable by the proposed DL-MBR scheme in UWOC scenarios when a relatively large LM occurs.

中文翻译：

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用于水下光通信的深度学习辅助失准鲁棒盲接收器


水下无线光通信（UWOC）已被提议通过利用充足的光谱来提供高速数据服务。然而，由于吸收、散射和湍流等各种信道效应，水下场景给无线光信号传播带来了恶劣的环境。此外，由湍流水波引起的光发射器和接收器之间的链路失准（LM）会降低可实现的系统性能。所有上述因素使得信息恢复对于UWOC系统来说是一项具有挑战性的任务，特别是对于长距离数据传输。在这封信中，我们介绍了一种基于深度学习 (DL) 的失准鲁棒盲接收器 (MBR)，用于在多输入多输出 (MIMO) UWOC 系统中恢复接收到的数据，其中使用了卷积神经网络 (CNN)为了制定模型训练中的信号特征，利用CNN组合器进行特征分析和组合，并利用CNN解调器恢复传输的信息。评估结果表明，当发生相对较大的LM时，所提出的DL-MBR方案在UWOC场景中可以实现可靠的性能。