## 2 Literature Survey  
[PERSON] et al. [11] demonstrated the effectiveness of a \"Super Gaussian kernel assisted deep neural network (SGK-DNN)\" equalization in \"blue LED CAP 16 QAM visible light communication\", spanning from 10 cm of open space to a 100 m cable. Although SGK-DNN equalization efficiently mitigates highly nonlinear distortion, it has higher computational complexity. [PERSON] et al. [12] introduced a post-equalization approach based on Bidirectional Gated Recurrent Units (BGRU) for mitigating linear and nonlinear distortion in underwater visible light communication using precise mathematical programming and structure amplitude phase shift keying. However, this approach consumes more energy during operation. [PERSON] et al. [13] proposed a \"Functional Link Neural Network\" as a post-equalization technique in underwater visible light communication technology for nonlinear equalization. [PERSON] et al. [14] presented an in-depth analysis of intelligent hardware and network layer techniques for an AI-enabled advanced UVLC system. They discussed the issues arising from nonlinear distortions and potential solutions. They also suggested and empirically demonstrated an innovative \"fully-connected deep neural network (FC-DNN)\" based transmitter in an \"underwater wireless optical communication (UWOC) system to directly decode orthogonal frequency division multiplexing (OFDM)\" data using deep learning techniques [14]. [PERSON] et al. [15] advocated for using a support vector machine (SVM), which has been extensively studied in quadruple frequency modulation for decryption to make softer decisions, manage to increase computational complexity, and eliminate distortion. [PERSON] et al. [16] suggested using a gated recurrent unit (GRU) neural network-driven equalization to compensate for linear and nonlinear distortions in carrier-less amplitude-phase band constrained UVLC systems. The GRU method is the primary basis of the equalization technique, using a gated approach to capture the relationships between segments stored within the system. In their study, [PERSON] et al. [17] designed an optoelectronic convolutional neural network (OECNN) for data processing equalization based on an optoelectronic multilayer processing unit. This enabled them to recover information in optical transmission channels while reducing linear and nonlinear distortions and lowering the substantial computing expense. The optical transmission information restoration system is founded on an optical convolution decoder implemented using a propagation delay module and frequency-division multiplexer. [PERSON] et al. [18] suggested using transmission learning-depends on artificial neural networks as subsequent equalizers for UVLC to mitigate linear and nonlinear distortion while reducing computational complexity. [PERSON] et al. [19] developed a novel nonlinear robust learning post-equalization for UVLC systems in their research. The proposed \"Time-frequency domain network (TFDNet)\" leverages time-frequency pixel analysis, simultaneously considering both time and frequency contexts. This converts the signal into a 2D feature, which a neural system then learns to address nonlinear imbalances in UVLC. Conventional equalizers only account for the time domain. [PERSON] et al. [20] demonstrated a new post-equalizer dependent on a \"dual branch multi-layer perceptron (DBMLP)\". The dual branching of the DBMLP compensates for linear and nonlinear distortions in the received data, reducing the difficulty of the artificial system and improving the post-equalizer's effectiveness. [PERSON] and [PERSON] [21] implemented a partial reduction technique for post-adjustment dependent on a \"dual-branch multilayer perceptron-based post-equalizer (DBMLP-PE)\" in a UVLC system to remove distortions and significantly reduce computational cost. [PERSON] et al. [22] recommended and experimentally verified a high-speed and robust UVLC technology utilizing a single input multi-output oriented adaptive diversity combining technique for efficiency improvements.