The compressive sensing (CS) technique is widely used in the field of radio-frequency channel estimation. However, in optical wireless channel (OWC) estimation, CS needed to be adapted to cope with the nonlinear distortion resultant from the electrical-to-optical and optical-to-electrical conversion process of the OWC. Therefore, a robust CS channel estimation technique is proposed for the optical MIMO–OFDM system. The proposed CS channel estimation technique is based on higher-order statistics (HOS). In which, the correntropy is exploited as a HOS similarity measurement instead of the second-order statistics euclidean distance (ED) used with the conventional CS to handle the OWC nonlinear distortion. Herein, the ED is the most popular iterative CS algorithms such as orthogonal matching pursuits, sparsity adaptive matching pursuit, and self-aware step size sparsity adaptive matching pursuit is efficiently replaced by the HOS correntropy based CS to show the effectiveness of the proposed algorithm. Moreover, the most recent visible light communication system GLIM-OFDM is considered here to test the proposed technique. However, the pre-estimated channel state information (CSI) obtained from the previous OFDM frame is exploited as initial information in estimating the CSI of the current OFDM frame, which improves overall system performance. A comparison with the conventional CS reconstruction algorithms is conducted via extensive simulation analysis. The simulation results show the superiority of the bit error rate of the proposed correntropy based CS channel estimation scheme compared to the conventional CS-based channel estimation algorithms.

压缩感测（CS）技术被广泛用于射频信道估计领域。但是，在光无线信道（OWC）估计中，需要调整CS以应对由OWC的电光和光电转换过程导致的非线性失真。因此，针对光学MIMO-OFDM系统提出了一种鲁棒的CS信道估计技术。所提出的CS信道估计技术基于高阶统计（HOS）。其中，将熵用作HOS相似性度量，而不是使用常规CS处理OWC非线性失真的二阶统计欧氏距离（ED）。在此，ED是最流行的迭代CS算法，例如正交匹配追踪，稀疏自适应匹配追踪，自我感知的步长稀疏性自适应匹配追踪被基于HOS熵的CS所取代，证明了该算法的有效性。此外，这里考虑使用最新的可见光通信系统GLIM-OFDM来测试所提出的技术。然而，在估计当前OFDM帧的CSI时，将从前一OFDM帧获得的预估计信道状态信息（CSI）用作初始信息，这提高了整体系统性能。通过广泛的仿真分析，与常规CS重建算法进行了比较。仿真结果表明，与传统的基于CS的信道估计算法相比，该基于熵的CS信道估计方案的误码率优越。