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:可见光信号的水下传播环境受到吸收、阴影和反射等复杂因素的影响,因此,实现有效的水下可见光通信(UVLC)频道估计非常困难,UVLC频道很难在时间和频率域中被稀释,这限制了使用稀少的信号处理技术来提高频道估计性能的机会。为此,本文件建立了一个以压缩遥感为基础的框架,充分利用水下可见光频道在传播链接的远程域内的宽度,从而充分利用水下可见光频道在传播链接的远程域内的宽度。与现有的非CS型和基于CS的系统相比,提议的基于水下可见光频道估计的稀疏学习计划显示以水下可见光频道为基础的稀疏广光频道估计(SL-UVCE)计划。具体地说,采用了一个深宽宽的神经网络来模拟光源传递(AMP)的典型迭代稀少恢复算法,将AMP的迭代转换成一系列具有不同可学习参数的层。与现有的非CS型和基于CS的系统计划相比,拟议的计划显示频道估计准确性的性表现得,特别是在大量试验和严重的条件下。</s>