Wide dynamic range signal detection for underwater optical wireless communication using a PMT detector

In the underwater optical wireless communication (UOWC) scenario, a photomulti-plier tube (PMT) with higher sensitivity, lower noise, and a larger receiver area is employed as the photon detector to further extend the transmission distance. Due to the complex underwater environment, the high directionality of the light beam, and the vibration of a transceiver, the incident optical power usually spans a very wide dynamic range, and the PMT may operate in any one of the three regimes: pulse, transition, and waveform. While it is difficult to obtain the analytical characterization of the output electric signals across these regimes, this paper resorts to experimental measurements of the upsampled discrete samples within a training symbol duration. Among different statistical distribution fitting options, generalized extreme value (GEV) distribution is found to show excellent performance in fitting the probability density function (PDF) of either multiple samples or the superimposition of all samples within a symbol duration. Then joint sample distribution (JSD) based and superimposed sample distribution (SSD) based symbol detection methods are proposed by adopting the GEV distribution and log-likelihood ratio (LLR) testing criterion. The proposed methods are experimentally evaluated under different received signal optical powers, data rates, and sampling rates. They are shown to outperform the Poisson and Gaussian based maximum likelihood detection methods which are employed for the pulse regime and waveform regime respectively. Furthermore, the effectiveness of the proposed methods in alleviating strong ambient radiation is experimentally verified. & COPY; 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

In this paper, the selection of photon detector and symbol detection methods in underwater optical wireless communication are investigated. Experimental measurements of upsampled discrete samples within a training symbol duration are used to fit the sample distribution with the generalized extreme value (GEV) distribution. Joint sample distribution (JSD) based and superimposed sample distribution (SSD) based symbol detection methods are proposed using the GEV distribution and log-likelihood ratio (LLR) testing criterion. Experimental results show that these methods outperform traditional maximum likelihood detection methods under different conditions. The effectiveness of the proposed methods in alleviating strong ambient radiation is also experimentally verified.