Underwater wireless optical communication (UWOC) will play an important role in the underwater environment exploration and marine resource development due to its advantages of high data rate and good mobility. However, the significant signal power attenuation in the underwater channel limits the transmission distance of UWOC. Attenuation length (AL) is widely used as an indicator for evaluating the UWOC system's long-distance transmission capability. At present, Gbps UWOC is limited within 7AL. Using a SiPM based receiver can dramatically increase the AL that UWOC can support. In this paper, a novel UWOC receiver built from an off-the-shelf SiPM has been demonstrated. The finite pulse width and limited bandwidth of SiPM limit the SiPM based UWOC system's data rate. To boost the system's data rate, an optimum method to process the SiPM's signal has therefore been investigated. Based on these methods, the communication capabilities of the SiPM based UWOC have been investigated experimentally. Results show that the SiPM based receiver can support 11.6AL without turbulence and 9.28AL within weak turbulence (scintillation index = 0.0447) at 1 Gbps.
In underwater optical wireless communication (UOWC) systems, using single photon avalanche photondiode (SPAD) as the detector can improve the transmission distance. However, the signal detection for SPAD-based systems is greatly challenged by the complex optical channel characteristics and SPAD nonlinear distortion. To address this issue, a novel deep learning aided signal detection scheme is proposed in this paper. By exploiting the physical mechanism and prior expert knowledge of the signal processing, a two-connected multilayer perception (MLP) network is integrated into the receiver. The first subnetwork is regarded as a channel compensation block while the second one works as a demodulator. With sophisticated numerical optical channel model and SPAD non-Poisson model, large amounts of training data are utilized to train the proposed model offline. Afterwards, the online data are recovered with the trained network. Simulation results verify that significant bit error ratio (BER) improvement can be achieved with the proposed scheme. INDEX TERMS Underwater optical wireless communication, nonlinear distortion, deep learning, multilayer perception, signal detection. I. INTRODUCTION With the application of technologies such as massive multi-input multi-output transmission, millimeter-wave (mm-mave) communication and non-orthogonal multiple access scheme, 5G mobile communication has significantly increased the system capacity and supported massive connections [1]. However, the 5G network is still ground based [2]. Vast communication demand at sea is greatly challenged due to the limited 5G network coverage. Thus, the envisaged 6G network is expected to provide global wireless connectivity from space to underwater. As a complementary technology for terrestrial communication, optical wireless communication, like laser communication or visible light The associate editor coordinating the review of this manuscript and approving it for publication was Zinan Wang .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.