Intelligent reflecting surfaces (IRSs) have emerged as a promising solution to mitigate line-of-sight (LoS) blockages and enhance signal coverage in optical wireless communication (OWC) systems with minimal additional power. In this work, we consider a mirror-based IRS to assist a dynamic indoor visible light communication (VLC) environment. We formulate an optimization problem that aims to maximize the sum rate by adjusting the orientation of the IRS mirrors. To enable real-time adaptability, the problem is modelled as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is developed based on the deterministic policy gradient for real-time mirror-based IRS optimization in dynamic VLC networks. The proposed DRL is employed to optimize mirror orientation toward mobile users under blockage and mobility constraints. Simulation results demonstrate that our proposed DRL algorithm outperforms the conventional deep Q- learning (DQL) algorithm and achieves substantial improvements in sum rate compared to random-orientation IRS configurations

Intelligent reflecting surfaces (IRSs) technology has been considered a promising solution in visible light communication (VLC) systems due to its potential to overcome the line-of-sight (LoS) blockage issue and enhance coverage. Moreover, integrating IRS with a downlink non-orthogonal multiple access (NOMA) transmission technique for multi-users is a smart solution to achieve a high sum rate and improve system performance. In this paper, a dynamic IRS-assisted indoor NOMA-VLC system is modeled, and an optimization problem is formulated to maximize sum energy efficiency (SEE) and fairness among multiple mobile users under power allocation and IRS mirror orientation constraints. Due to the non-convex nature of the optimization problem and the non-linearity of the constraints, conventional optimization methods are impractical for real-time solutions. Therefore, a two-agent deep reinforcement learning (DRL) algorithm is designed for optimizing power allocation and IRS orientation based on centralized training with decentralized execution to obtain fast and real-time solutions in dynamic environments. The results show the superior performance in terms of SEE, sum rate and fairness index of the proposed DRL algorithm compared to standard DRL algorithms and conventional algorithms typically used for resource allocation in wireless communication. The results also show that the proposed two-agent DRL algorithm achieves higher performance compared to deployments without IRS and with randomly oriented IRS elements.

One of the crucial issues in indoor optical wireless communication (OWC) is service interruptions due to blockages that obstruct the line of sight (LoS) between users and their access points (APs). Recently, reflecting surfaces referred to as intelligent reflecting surfaces (IRSs) have been considered to provide improved connectivity in OWC systems by reflecting AP signals toward users. In this study, we investigate the integration of IRSs into an indoor OWC system to improve the sum rate of the users and to ensure service continuity. We formulate an optimization problem for the sum rate maximization, where the allocation of both APs and mirror elements of IRSs to users is determined to enhance the aggregate data rate. Moreover, reinforcement learning (RL) algorithms, specifically Q-learning and SARSA algorithms, are proposed to provide real-time solutions with low complexity and without prior system knowledge. The results show that using RL algorithms achieves near-optimal solutions that are close to the solutions of mixed integer linear programming (MILP). The results also show that the proposed scheme achieves a significant data rate increase compared to a traditional scheme that allocates the resources, APs and mirror elements, based on the distance.

Optical wireless communication (OWC) has recently received massive interest as a new technology that can support the enormous data traffic increasing on daily basis. In particular, laser-based OWC networks can provide terabits per second (Tbps) aggregate data rates. However, the emerging OWC networks require a high number of optical access points (APs), each AP corresponding to an optical cell, to provide uniform coverage for multiple users. Therefore, inter-cell interference (ICI) and multi-user interference (MUI) are crucial issues that must be managed efficiently to provide high spectral efficiency. In radio frequency (RF) networks, rate splitting (RS) is proposed as a transmission scheme to serve multiple users simultaneously following a certain strategy. It was shown that RS provides high data rates compared to orthogonal and non-orthogonal interference management schemes. Considering the high density of OWC networks, the application of RS within each optical cell might not be practical due to severe ICI. In this paper, a novel strategy is derived, referred to as blind interference alignment-rate splitting (BIA-RS), to fully coordinate the transmission among the optical APs, while determining the precoding matrices of multiple groups of users formed beforehand. Therefore, RS can be implemented within each group to manage MUI. The proposed BIA-RS scheme requires two layers of power allocation to achieve high performance. Given that, a max-min fractional optimization problem is formulated to optimally distribute the power budget among the groups and the messages intended to the users of each group. Finally, a power allocation algorithm is designed with multiple Lagrangian multipliers to provide practical and sub-optimal solutions. The results show the high performance of the proposed scheme compared to other counterpart schemes.

This paper investigates the integration of Open Radio Access Network (O-RAN) within non-terrestrial networks (NTN), and optimizing the dynamic functional split between Centralized Units (CU) and Distributed Units (DU) for enhanced energy efficiency in the network. We introduce a novel framework utilizing a Deep Q-Network (DQN)-based reinforcement learning approach to dynamically find the optimal RAN functional split option and the best NTN-based RAN network out of the available NTN-platforms according to real-time conditions, traffic demands, and limited energy resources in NTN platforms. This approach supports capability of adapting to various NTN-based RANs across different platforms such as LEO satellites and high-altitude platform stations (HAPS), enabling adaptive network reconfiguration to ensure optimal service quality and energy utilization. Simulation results validate the effectiveness of our method, offering significant improvements in energy efficiency and sustainability under diverse NTN scenarios.

Recently, Optical wireless communication (OWC) have been considered as a key element in the next generation of wireless communications due to its potential in supporting unprecedented communication speeds. In this paper, infrared lasers referred to as vertical-cavity surface-emitting lasers (VC-SELs) are used as transmitters sending information to multiple users. In OWC, rate-maximization optimization problems are usually complex due to the high number of optical access points (APs) needed to ensure coverage. Therefore, practical solutions with low computational time are essential to cope with frequent updates in user-requirements that might occur. In this context, we formulate an optimization problem to determine the optimal user association and resource allocation in the network, while the serving time is partitioned into a series of time periods. Therefore, cooperative ANN models are designed to estimate and predict the association and resource allocation variables for each user such that sub-optimal solutions can be obtained within a certain period of time prior to its actual starting, which makes the solutions valid and in accordance with the demands of the users at a given time. The results show the effectiveness of the proposed model in maximizing the sum rate of the network compared with counterpart models. Moreover, ANN-based solutions are close to the optimal ones with low computational time.

Optical wireless communication offers unprecedented communication speeds that can support the massive use of the Internet on a daily basis. In indoor environments, optical wireless networks are usually multi-user multiple-input multiple-output (MU-MIMO) systems, where a high number of optical access points (APs) is required to ensure coverage. In this work, a laser-based optical wireless network is considered for serving multiple users. Moreover, blind inference alignment (BIA) is implemented to achieve a high degree of freedom (DoF) without the need for channel state information (CSI) at transmitters, which is difficult to provide in such wireless networks. Then, an objective function is defined to allocate the resources of the network taking into consideration the requirements of users and the available resources. This optimization problem can be solved through exhaustive search or distributed algorithms. However, a practical algorithm that provides immediate solutions in real time scenarios is required. In this context, an artificial neural network (ANN) model is derived in order to obtain a sub-optimal solution with low computational time. The implementation of the ANN model involves three important steps, dataset generation, offline training, and real time application. The results show that the trained ANN model provides a significant solution close to the optimal one.

Optical wireless Communication (OWC) is a strong candidate in the next generation (6G) of cellular networks. In this paper, a laser-based optical wireless network is deployed in an indoor environment using Vertical Cavity Surface Emitting Lasers (VCSELS) as transmitters serving multiple users. Specifically, a commercially available low-cost VCSEL operating at S50nm wavelength is used. Considering the confined coverage area of each VCSEL, an array of VCSELs is designed to transmit data to multiple users through narrow beams taking into account eye safety regulations. To manage multi-user interference (MUI), Zero Forcing (ZF) is implemented to maximize the multiplexing gain of the network. The energy efficiency of the network is studied under different laser beam waists to find the effective laser beam size that results in throughput enhancement. The results show that the energy efficiency increases with the laser beam waist. Moreover, using micro lenses placed in front of the VCSELs leads to significant increase in the energy efficiency.

Optical wireless communication provides data transmission at high speeds which can satisfy the increasing demands for connecting a massive number of devices to the Internet. In this paper, vertical-cavity surface-emitting(VCSEL) lasers are used as transmitters due to their high modulation speed and energy efficiency. However, a high number of VCSEL lasers is required to ensure coverage where each laser source illuminates a confined area. Therefore, multiple users are classified into different sets according to their connectivity. Given this point, a transmission scheme that uses blind interference alignment (BIA) is implemented to manage the interference in the laser-based network. In addition, an optimization problem is formulated to maximize the utility sum rate taking into consideration the classification of the users. To solve this problem, a decentralized algorithm is proposed where the main problem is divided into sub-problems, each can be solved independently avoiding complexity. The results demonstrate the optimality of the decentralized algorithm where a sub-optimal solution is provided. Finally, it is shown that BIA can provide high performance in laser-based networks compared with zero forcing (ZF) transmit precoding scheme.

Visible light communication (VLC) is considered a promising technology for providing high data rates in indoor environments. In this sense, each optical access point (AP) in a VLC network can be managed as a small cell usually referred to as attocell. Therefore, the received signal is subject to both multi-user interference (MUI) and inter-cell interference (ICI). In this work, we propose a user centric (UC) cell formation approach jointly with the use of blind interference alignment (BIA) in order to manage the interference. We formulate an optimization problem based on maximizing the sum utility of users rates for the sake of jointly finding the optimal cell formation and minimizing the limitations of BIA, i.e., the noise enhancement and the required coherence time given by the mobility of the users. This problem can be solved through exhaustive research, which involves an unpractical complexity. After that, we decouple the main problem into two sub-problems that may be solved separately. The simulation results show that the proposed schemes are more suitable for VLC networks than the considered benchmark schemes.