Channel Adaptive Decoding in Underwater Optical Camera Communication Systems

Underwater optical camera communication (UOCC) systems that use light-emitting diodes as transmitters and high-frame rate cameras as receivers offer robust and low-cost wireless communication solutions for underwater applications compared to traditional methods. However, its performance is greatly affected by the varying underwater environments such as turbulence, salinity, and oceanic currents. This is mainly due to fixed image processing and data decoding algorithms used in UOCC systems don’t provide adaptive methods that change with the underwater environments. To this end, we introduce an adaptive threshold-based algorithm to enhance the system’s performance by dynamically adjusting the threshold values used in the decoding algorithm on real-time channel conditions.In this context, adaptive thresholding refers to selecting optimal threshold values for various transmission scenarios, ensuring robust data decoding tailored to specific environmental challenges. This paper presents a comprehensive analysis of the presented algorithm under four distinct channel conditions, such as clear water, muddy water, air bubbles, and chlorophyll water. Our simulations and experiments demonstrate the effectiveness of the adaptive threshold-based algorithm in improving bit error rate (BER), making UOCC more reliable and robust in diverse underwater conditions for short-range applications. The BER performance of the presented algorithm in clear water is nearly zero, while under the other three conditions, it is approximately 1 × 10⁻³ within a 1-meter communication distance.