## 4 Proposed Framework
### Adaptive Fuzzy Logic Deep-Learning Equalizer (AFL-DLE)  
Due to the incoherent properties of LEDs, amplitude regulation and direct sensing approaches are used in UVLC systems that employ LEDs. A high motoring bias is necessary to increase the \"signal-to-noise ratio (\(SNR\))\" due to the important amplification of the water phase. This induces nonlinear disturbance arising from both the underwater environment and spatial hardware responses, including the variational electro-optic transformation of LEDs, the nonlinear reaction of electronic systems, and the square-law detection of photodiodes. The UVLC system has a nonlinear reply to the standardized intensity amplitude slope of the input signal and output signal (a). As the signal's intensity increases, the nonlinearity becomes more noticeable, representing that the received symbols' degrees of distortion vary. The received symbols are normalized by the LMS procedure for an intuitive demonstration. In the subsequent LMS equalization, the green and black spots denote the proper and improper symbols, respectively. The majority of black spots are scattered in the constellation's exterior, suggesting that LMS has insufficient equalization capability to handle received patterns with greater imperfections located there. Therefore, we introduce an adaptable segmented equalization to equalize the affected components with increased effectiveness and reduced complexity. We employ neural networks to address nonlinear distortion. Figure 2 presents the architectural structure of the proposed AFL-DLE. The obtained M-QAM message series is represented as \(Y=[y_{1},y_{2},\ldots,y_{n}]\), where the vector \(y_{l}(l=1,2,\ldots,n)\) denotes the \(l\)th obtained message. Symbols and every vector \(y_{1}\) have difficult values, containing the in-phase (I) and quadrature-phase (Q) components, and are located in the complex domain C. The actual transmitted pattern is designated as \(X=[\widehat{x}_{1},\widehat{x}_{2},\ldots,\widehat{x}_{n}]\), while the matching anticipated sequence is designated as \(X=[\widehat{x}_{1},\widehat{x}_{2},\ldots,\widehat{x}_{n}]\). Normalization aims to attempt to retrieve the expected \(X\) from \(Y\) that closely resembles the actual X. The obtained symbols \(Y=[y_{1},y_{2},\ldots,y_{n}]\) are divided into two divisions by a cutoff first, with \(Y_{\text{inner}}\) located in the inner area of the cluster and \(Y_{\text{outer}}\) located in the outer portion. As seen in Figure 2, the threshold's border has a rectangle form. The amplitude of acquired signs must be considered when using other forms, such as the round. This research divides the signs into rectangles based on I/Q signs for simplification and as proof of concept. Finding signs whose magnitude of real and imaginary portions are inside the threshold border is the first step in the split procedure for \(Y_{\text{inner}}\), after which \(Y_{\text{outer}}\) is responsible for the remaining symbols. Decreased computing difficulty while preserving acceptable equalization effectiveness depends on selecting the appropriate cutoff. We provide a flexible method for determining the best threshold depending on the bit error rate (\(BER\)) after LMS normalization. Then, two complex-valued neural networks equalize \(Y_{\text{inner}}\) and \(Y_{\text{outer}}\) correspondingly. We employ the FCNN design, which consists of a single intake layer, one concealed layer, and one output layer, for the complicated exponential neural network. The incoming signal, \(Y_{\text{inner}}\) (\(i\)), propagates as complex values during the concealed layer using \(L_{\text{inner}}\) symbols positioned on the middle symbol. Equation (1) represents how the network's complicated output is stated. \[\hat{x}_{inner}=W_{in}^{2}\left(f\left(W_{in}^{1}Y_{\text{inner}}+B_{in}^{1} \right)\right)+B_{in}^{2} \tag{1}\]  
The weighted matrices and biased vectors with complex elements are \(W_{in}^{j}\) and \(B_{in}^{j}(j=1,2)\). The activating factor is shown by the symbol \(f\) (\(\cdot\)).