## 1 Introduction
In this approach, the IRS is used to strategically reflect and direct light signals toward ADR-equipped receivers, maximizing the SNR even in NLoS conditions. The IRS is placed in optimal locations in the environment, and its reflection characteristics are dynamically adjusted using machine learning algorithms to ensure the most effective redirection of signals. The ADRs, on the other hand, further enhance reception by capturing signals from multiple directions and optimizing their combination using intelligent processing techniques. This dual approach leverages the strengths of IRS and ADR technologies to create a robust VLC system capable of maintaining high spectral efficiency and reliability even in challenging indoor scenarios. The optimization problem addressed in this work focuses on determining the ideal placement of the IRS in an indoor VLC environment to maximize ADR performance. In this study, optimization is performed using a framework based on a recurrent neural network (RNN), which dynamically adjusts the IRS positioning and reflection properties to maximize the SNR in the ADRs. RNNs are particularly well suited for indoor scenarios because of their ability to model temporal dependencies and sequential data effectively. This advantage enables the RNN to iteratively update the IRS configuration while learning from the feedback of the received power and SNR metrics at each step. Furthermore, hidden states in RNNs facilitate the retention of relevant past information, further enhancing the optimization process. The use of the RNN allows for efficient exploration of the complex solution space, leveraging its ability to learn and adapt to nonlinear and dynamic environments. This approach enables the system to consider varying signal conditions and obstructions, ensuring optimal performance. By integrating RNN-based IRS optimization with advanced ADR mechanisms, specifically MRC and SBC, this work presents a comprehensive solution for enhancing VLC systems in challenging indoor environments, significantly improving their practicality and adaptability in real-world applications. In this context, the main contributions of this paper are summarized as follows:  
* Integration of IRS and ADR Techniques: This study explores the use of an IRS and ADRs with two distinct mechanisms to enhance signal reception in indoor VLC environments. * RNN-Based Optimization Framework: An RNN model is implemented to dynamically optimize the IRS positioning, ensuring the maximized received power and SNR under challenging conditions, including shadowing and NLoS scenarios. * Comprehensive Performance Evaluation: The proposed system is extensively analyzed in terms of SNR distribution and received power for various ADR configurations, demonstrating the impact of IRS placement and RNN-based optimization on the system's spectral efficiency. * Enhanced Scalability and Applicability: Using an RNN and ADRs, this study presents a robust solution adaptable to various indoor environments, paving the way for future advancements in the design of the VLC system. This work is organized as follows. Section 2 provides a review of the existing literature on VLC systems, IRS-based signal enhancement, and ADR technologies. Section 3 introduces the foundational components of the VLC system. Section 4 formulates the indoor environment and presents the optimization of the power received through IRS placement. Section 5 develops the structural and mathematical framework for ADRs, focusing on pyramidal and hemispherical geometries. Section 6 derives the SNR models and explores MRC and SBC to process received signals effectively. Building on these models, Section 7 introduces an RNN framework to optimize IRS positioning for improved signal reflection and an improved SNR at the ADR. Section 8 evaluates the performance of the proposed system using simulations, comparing scenarios with and without RNN optimization to validate the effectiveness of the methodology.