###### Abstract  
Underwater wireless optical communication (UWOC) systems face challenges due to the significant temporal dispersion caused by the combined effects of scattering, absorption, refractive index variations, optical turbulence, and bio-optical properties. This collective impairment leads to signal distortion and degrades the optical receiver's bit error rate (BER). Optimising the receiver filter and equaliser design is crucial to enhance receiver performance. However, having an optimal design may not be sufficient to ensure that the receiver decision unit can estimate BER quickly and accurately. This study introduces a novel BER estimation strategy based on a Convolutional Neural Network (CNN) to improve the accuracy and speed of BER estimation performed by the decision unit's computational processor compared to traditional methods. Our new CNN algorithm utilises the eye diagram (ED) image processing technique. Despite the incomplete definition of the UWOC channel impulse response (CIR), the CNN model is trained to address the nonlinearity of seawater channels under varying noise conditions and increase the reliability of a given UWOC system. The results demonstrate that our CNN-based BER estimation strategy accurately predicts the corresponding signal-to-noise ratio (SNR) and enables reliable BER estimation. os 2024 12805 127 July 202418 August 20  
However, traditional BER estimation strategies, such as Monte Carlo simulations (MCSs) and analytical methods, are computationally intensive [6] and may not adapt well to the dynamics of the underwater environment. Pilot symbols and training sequences provide more real-time estimation but at the cost of reduced data throughput [7]. Error Vector Magnitude (EVM) and noise variance estimation offer alternative approaches but are often limited by their assumptions about the channel conditions [6; 7], which makes the estimation strategy highly dependent on the full knowledge of the channel impulse response (CIR) temporal profile. Monte Carlo simulations are flexible and can handle complex systems but are computationally expensive. Analytical methods are efficient, but their success depends on the model accuracy level, which the appropriate decision unit should avoid. Empirical methods provide real-world accuracy but are impractical for initial design, i.e., it is design time not run time implementation knowledge. Hence, the choice of BER estimation method depends on the specific numerical needs and constraints of the communication system being analysed. Recent advancements in machine learning (ML) technology solution options (see Figure 1), particularly CNNs in optical performance monitoring, have introduced a new technology to help design an efficient computational processor (for the DU) that deploys a CNN-oriented strategy. CNNs can learn complex patterns embedded in eye diagrams (composed of various pixels) that are generated in real-time (see Figure 2). These patterns are learned from the bit stream received at the input of the DU, making them suitable for dynamically adapting to the varying conditions in UWOC systems. CNNs recognise patterns in visual data, making them suitable for processing eye diagram images--a critical visualisation tool used in digital communication systems to evaluate signal integrity and quality. Eye diagrams encapsulate key performance metrics, including timing jitter and noise levels, providing a comprehensive snapshot of the signal's health. By utilising the capabilities of CNNs, it is possible to develop a more robust and efficient decision unit strategy that improves BER estimation accuracy and overall system performance. This CNN-based DU strategy does not depend on the transmission modulation format, channel stochastic impairments, and the need to set a fixed threshold during the design phase to estimate the BER. The CNN training data pool is continuously enhanced without reducing the data throughput.