Novel Compressive Sensing Based Channel Estimation for Wideband Underwater Visible Light Communication

As the underwater applications require reliable, fast and efficient communications, the underwater visible light communication (VLC) is attracting more and more attention. Due to the complicated channel condition of the underwater environment, accurate channel estimation is still challenging for underwater VLC systems. This paper investigates the underwater VLC channel estimation problem by following two main assumptions. Firstly, the number of the channel paths is assumed to be much smaller than the channel length, which will obtain the sparsity property in the distance domain. Secondly, the attenuation coefficient is assumed to be approximately linear with the frequency, which is true when the modulation bandwidth is much more narrow compared with the carrier frequency. The compressive sensing (CS) framework is formulated based on the two assumptions, whose sensing matrix has a large coherence. In this condition, the Bayesian CS algorithm is adopted instead of the conventional schemes, and the subspace scheme is also investigated for comparison. The simulation results demonstrate that the proposed scheme outperforms the conventional ones in terms of mean square error (MSE), hence better system performance can be expected.