To mitigate the scintillation effect caused by oceanic turbulence on received light intensity and reduce its influence on the performance of underwater wireless optical communication (UWOC) systems, we propose an adaptive combining algorithm based on traditional anti-scintillation methods to enhance the system’s stability and save energy.

Based on a single-input-multiple-output (SIMO) system, our proposed adaptive dynamic combining algorithm uses multiple branches to receive signals, and the activation state and combining strategy of each branch are adaptively controlled according to the signal-to-noise ratio (SNR). We conduct simulations to evaluate bit error rate (BER), outage probability, and energy consumption performance of two-branch and three-branch systems under channel conditions with a scintillation index of 0.32 and Gaussian white noise variance of 0.49. Additionally, under the condition of two-branch reception, we simulate the influence of turbulence intensity on UWOC system performance by placing 1, 2, and 3 heating rods in a water tank to verify the performance of the adaptive algorithm under two-branch reception. The results confirm the effectiveness of our proposed adaptive combining algorithm.

To demonstrate the performance of our proposed algorithm, we first conduct a simulation analysis under channel conditions with a scintillation index of 0.32 and noise variance of 0.49, focusing on the performance of both two-branch and three-branch configurations. The analysis covered BER, outage probability, and energy consumption. The results indicate that the anti-scintillation performance of the three-branch combination is superior to that of the two-branch combination, with lower BER and outage probabilities in the three-branch setup (Figs. 7 and 9); however, the energy consumption is higher for the three-branch reception. Additionally, a comparison between the performance of our adaptive algorithm and traditional algorithms reveals that our proposed adaptive algorithm exhibits BER and outage performance only slightly lower than that of the maximum ratio combining (MRC) algorithm while outperforming the equal gain combining (EGC) and selection combining (SC) algorithms (Figs. 6?9). From the perspective of energy consumption, our adaptive algorithm demonstrates superior performance, effectively reducing energy usage. Furthermore, we conduct experiments to validate the performance of our proposed adaptive algorithm under two-branch reception across different scintillation index conditions (Table 1), confirming the effectiveness of the adaptive algorithm presented in this study.

The results show that our proposed adaptive combining algorithm exhibits excellent anti-scintillation performance and outperforms traditional combining algorithms in terms of energy efficiency. Simulation results for two- and three-branch anti-scintillation performance demonstrate that the system’s anti-scintillation capability improves with the number of branches, but merely increasing the number of branches offers limited enhancement. It cannot infinitely improve the system’s BER, SNR, and outage performance, and system energy consumption also rises with more branches. Additionally, by using two-branch reception, we simulate turbulence intensity by adding one, two, and three heating rods in a 1 m water tank to assess the influence on the UWOC system. This experiment validates the advantages of our proposed adaptive algorithm in terms of BER, outage performance, and energy consumption. Our adaptive combining algorithm achieves BER and outage performance slightly lower than MRC but superior to EGC and SC algorithms, while showing better energy efficiency than traditional methods.