Synchronous Clock Recovery of Photon-Counting Underwater Optical Wireless Communication Based on Deep Learning

Abstract

In photon-counting underwater optical wireless communication (UOWC), the recovery of the time slot synchronous clock is extremely important, and it is the basis of symbol synchronization and frame synchronization. We have previously proposed a time slot synchronous clock extraction method based on single photon pulse counting, but the accuracy needs to be further improved. Deep learning is very effective for feature extraction; synchronous information is already implicit in the discrete single photon pulse signal output by single photon avalanche diode (SPAD), which is used as a communication receiver. Aiming at this characteristic, a method of time slot synchronous clock recovery for photon-counting UOWC based on deep learning is proposed in this paper. Based on the establishment of the underwater channel model and SPAD receiver model, the Monte Carlo method is used to generate discrete single photon pulse sequences carrying synchronous information, which are used as training data. Two neural network models based on regression problem and classification problem are designed to predict the phase value of the time slot synchronous clock. Experimental results show that when the average number of photons per time slot is eight, photon-counting UOWC with a data rate of 1Mbps and a bit error rate (BER) of 5.35 × 10−4 can be achieved.