The underwater propagation environment for visible light signals is affected by complex factors such as absorption, shadowing, and reflection, making it very challengeable to achieve effective underwater visible light communication (UVLC) channel estimation. It is difficult for the UVLC channel to be sparse represented in the time and frequency domains, which limits the chance of using sparse signal processing techniques to achieve better performance of channel estimation. To this end, a compressed sensing (CS) based framework is established in this paper by fully exploiting the sparsity of the underwater visible light channel in the distance domain of the propagation links. In order to solve the sparse recovery problem and achieve more accurate UVLC channel estimation, a sparse learning based underwater visible light channel estimation (SL-UVCE) scheme is proposed. Specifically, a deep-unfolding neural network mimicking the classical iterative sparse recovery algorithm of approximate message passing (AMP) is employed, which decomposes the iterations of AMP into a series of layers with different learnable parameters. Compared with the existing non-CS-based and CS-based schemes, the proposed scheme shows better performance of accuracy in channel estimation, especially in severe conditions such as insufficient measurement pilots and large number of multipath components.

翻译：水下可见光信号的传播环境受吸收、阴影和反射等复杂因素影响，使得实现有效的水下可见光通信信道估计极具挑战性。由于水下可见光信道在时域和频域难以实现稀疏表示，这限制了利用稀疏信号处理技术提升信道估计性能的可能性。为此，本文通过充分挖掘水下可见光信道在传播链路距离域上的稀疏性，建立了一个基于压缩感知的框架。为解决稀疏恢复问题并实现更精确的水下可见光信道估计，提出了一种基于稀疏学习的水下可见光信道估计方案。具体而言，该方案采用了一种模拟经典迭代稀疏恢复算法——近似消息传递的深度展开神经网络，将AMP的迭代过程分解为一系列具有不同可学习参数的层。与现有非CS及CS方案相比，该方案在信道估计精度方面展现出更优性能，尤其是在测量导频不足和多径分量数量大的恶劣条件下表现尤为突出。