Ma Xu, Yang Fang, Liu Sicong, Song Jian
Opt Express. 2018 Jan 8;26(1):311-321. doi: 10.1364/OE.26.000311.
With the rapid development of light emitting diode (LED), visible light communication (VLC) becomes an important technique for information transmission including underwater applications. However, accurate channel estimation for underwater VLC is still challenging due to the complex environment of the underwater VLC channel. In this paper, by utilizing a proper approximation, where the channel attenuation is linear with the frequency, a new compressive sensing (CS) based channel estimation approach is proposed. Utilizing the sparse property of the reflection path length for the underwater VLC channel, the CS framework is modeled to estimate the reflection path length, which can further recover the underwater VLC channel. Moreover, a Bayesian CS recovery algorithm is investigated to overcome the problem of high coherence for the sensing matrix which outperforms the conventional greedy algorithm such as orthogonal matching pursuit (OMP). Simulation results illustrate that our proposed channel estimation for underwater VLC systems has a superior performance which can significantly reduce the pilot overhead, improve the spectral efficiency, and enhance the estimation accuracy.
随着发光二极管（LED）的快速发展，可见光通信（VLC）成为包括水下应用在内的信息传输的一项重要技术。然而，由于水下VLC信道环境复杂，水下VLC的精确信道估计仍然具有挑战性。本文通过利用适当的近似，即信道衰减与频率呈线性关系，提出了一种基于压缩感知（CS）的新信道估计方法。利用水下VLC信道反射路径长度的稀疏特性，建立CS框架来估计反射路径长度，进而恢复水下VLC信道。此外，研究了一种贝叶斯CS恢复算法来克服感知矩阵高相干性的问题，该算法优于传统的贪婪算法，如正交匹配追踪（OMP）。仿真结果表明，我们提出的水下VLC系统信道估计具有优越的性能，能够显著降低导频开销，提高频谱效率，并提高估计精度。
