Reservoir Computing for Symbol Detection of Optical Wireless Scattering Communications

Non-line-of-sight (NLOS) optical wireless communication (OWC) based on atmospheric scattering attracts more and more attention due to their mitigation of pointing, acquisition, and tracking requirement. However, the channel relies on scattering radiation and thus causes significant dispersion and inter-symbol interference (ISI), which limits high data rate communications. Reservoir computing (RC) is a computational framework derived from recurrent neural networks (RNNs). It is suitable for sequential data processing with low training cost. In contrast to multilayer perceptron (MLP)-based symbolwise detectors that treat ISI as noise, a RC performs sequence detection that takes ISI into account. In this paper, we propose to employ the RC approach to detect transmitted symbols of NLOS OWC systems without knowing channel state information. Our simulations show that the RC can perform symbol detection in various channel conditions and it brings lower bit error rate results than the MLP-based detection.