arXiv subjects

Ahmad Adnan Qidan

Publications and source records attributed to Ahmad Adnan Qidan.

At least 19 recordsLinked to original sources

arXiv2026

LiDAL-Assisted RLNC-NOMA in OWC Systems

Optical wireless communication (OWC) is a key enabler for immersive indoor data transmission in future wireless networks. However, managing multi-user interference remains challenging in dense indoor OWC deployments with multiple optical access points (APs) serving multiple users. In this paper, we propose a dual-function OWC system for communication and localization. For data transmission, we design non-orthogonal multiple access (NOMA) combined with random linear network coding (RLNC): NOMA serves multiple users simultaneously via power-domain multiplexing, while RLNC improves decoding robustness under residual noise and processing impairments. The system is assisted by a light detection and localization (LiDAL) module that estimates users' spatio-temporal presence and location to enable dynamic user association. LiDAL also supports channel state information (CSI) estimation in realistic indoor scenarios where CSI may be noisy and/or highly correlated. We evaluate RLNC-NOMA by analyzing the probability of successful decoding and comparing it with conventional NOMA and orthogonal schemes. In addition, we derive the Cramer-Rao lower bound (CRLB) to evaluate the localization accuracy. The results indicate that RLNC-NOMA increases the probability of successful decoding, and that the proposed unbiased location estimator achieves 44% better CSI accuracy than the baseline CSI estimation and improves the overall system performance.

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arXiv2026

Cooperative Dueling DQN SAC Learning for Energy Efficiency in Dynamic OWC Networks

Growing wireless traffic is increasing pressure on the congested radio-frequency spectrum. Optical wireless communication (OWC) provides a complementary solution by using the abundant unlicensed optical spectrum. However, indoor OWC networks are dynamic: users move, enter or leave the network, and subscribe to different services. Poorly coordinated resource allocation can consequently waste subcarriers, require excessive transmission power and cause frequent AP reassignments. Energy efficiency (EE), defined as the total delivered data rate divided by the total network power consumption, therefore requires the serving AP, number of allocated subcarriers, and transmission power to be jointly adapted while maintaining QoS. Optimising these in a dynamic time series, multi-service OWC environment produces a complex sequential EE optimisation problem. To address this problem, this work proposes Dual-Agent Resource Allocation using Deep Reinforcement Learning (DARA-DRL). DARA-DRL combines a branching duelling deep Q-network for association and subcarrier allocation with a conditional soft actor-critic agent for continuous power control. The agents are coupled through a common reward and a cooperative value update that evaluates each discrete allocation together with its corresponding power decision. Simulation results show that DARA-DRL remains within 5\% of the optimal solution and, compared with state-of-the-art benchmarks, it improves EE by 28.8\% and QoS satisfaction by 10.5\%, while reducing online decision time by 14.5\%. Results demonstrate that agent specialisation simplifies mixed-action learning, and cooperation outperforms independently trained agents.

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arXiv2026

Network Adaptation in IRS-Aided Hybrid RF/VLC Systems Using Cooperative Multi-Agent DRL

Hybrid radio frequency (RF) and visible light communication (VLC) networks have emerged as a promising solution for high-capacity indoor wireless connectivity in sixth-generation (6G) systems. However, the limited optical coverage and vulnerability of VLC links to line-of-sight (LoS) blockage under user mobility remain fundamental challenges. In this work, a mirror-based intelligent reflecting surface (IRS) is deployed to assist a dynamic indoor hybrid RF/VLC network, where each mobile user is exclusively assigned to either the IRS-enhanced VLC subnetwork or the RF subnetwork through a binary selection decision. A joint optimization problem is then formulated to maximize proportional fairness by jointly optimizing the RF/VLC technology selection, power allocation, and IRS mirror roll and yaw orientation angles. To enable real-time adaptability, the problem is reformulated as a Markov decision process (MDP) and solved using a cooperative multi-agent deep reinforcement learning (DRL) algorithm based on centralized training with decentralized execution. Simulation results demonstrate the superior performance of the optimized hybrid network compared with optimized standalone VLC and RF networks. The results further validate the practicality and effectiveness of the proposed DRL framework compared to widely adopted DRL algorithms as well as conventional model-based optimization approaches.

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arXiv2026

DRL-based Power Allocation in LiDAL-Assisted RLNC-NOMA OWC Systems

Non-orthogonal multiple access (NOMA) is a promising technique for optical wireless communication (OWC), enabling multiple users to share the optical spectrum simultaneously through the power domain. However, imperfect channel state information (CSI) and residual decoding errors deteriorate NOMA performance, especially in realistic dense-user indoor scenarios. In this work, we model an OWC system that integrates light detection and localization (LiDAL) and random linear network coding (RLNC) within a NOMA framework. LiDAL exploits spatio-temporal information to improve user CSI, while RLNC enhances data resilience in the successive decoding process, resulting in a LiDAL-assisted RLNC-NOMA OWC system. Power allocation (PA) is crucial in this system due to complex interactions between multiple users and the coding and detection processes, but optimizing continuous PA dynamically can be computationally prohibitive. To address this, we adopt a deep reinforcement learning (DRL) framework to efficiently learn near-optimal PA strategies. In particular, a DRL-based normalized advantage function (NAF) algorithm is proposed to maximize the average sum rate, and its performance is compared to deep deterministic policy gradient (DDPG), gain ratio PA (GRPA), and exhaustive search. The results indicate that NAF closely matches exhaustive search, is 39% faster than DDPG, and improves the average sum rate by 4.6% over GRPA, while accounting for user location estimation errors.

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arXiv2026

Mobility Aware Power Control for VCSEL Based Indoor OWC

Optical wireless communication (OWC) is a promising technology for supporting data intensive services in indoor environments due to its large unregulated spectrum, high spatial reuse, and potential for multigigabit data rates. In particular, vertical cavity surface emitting laser (VCSEL) based systems enable highly directional transmission, allowing efficient spatial separation of users and improved link performance. However, the use of narrow optical beams also makes system performance highly sensitive to user mobility and device orientation, as movement directly affects beam alignment and optical channel gain. Consequently, power allocation strategies that ignore mobility dynamics often provision excess optical power to maintain reliable connectivity, resulting in inefficient energy use. In this work, a power control framework for dynamic indoor OWC networks that explicitly accounts for mobility driven channel variation is developed. It uses a hybrid Gauss Markov and learning based approach that captures both user movement continuity and behaviour driven orientation changes. The mobility states are then used to guide power allocation decisions. Simulation results show that incorporating mobility aware channel prediction enables more accurate power allocation, and improves energy efficiency compared with conventional power control schemes in dynamic indoor environments.

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arXiv2026

A Hybrid Gauss Markov LSTM Mobility Model for Indoor OWC

Optical wireless communication (OWC) has emerged as a promising candidate for future high-capacity indoor wireless networks, driven by its large unregulated spectrum, high spatial reuse, and ability to support multi-gigabit data rates. However, OWC systems are highly sensitive to user mobility, as link performance depends strongly on the spatial alignment between transmitter and receiver. Accurate modelling of user position and device orientation is therefore essential for reliable channel estimation and system evaluation. To that effect, this paper proposes a hybrid Gauss--Markov and long short-term memory (GM--LSTM) mobility model for indoor OWC environments. The Gauss--Markov component captures the temporal correlation of user motion, while the LSTM learns residual behaviour to model non-linear movement patterns and orientation dynamics. The proposed model jointly predicts user position and device orientation, enabling improved representation of mobility in OWC channels. Performance is evaluated using prediction accuracy and per-user data rate evolution. Results show that the proposed hybrid GM--LSTM model outperforms conventional Random Waypoint and Gauss--Markov models, providing more accurate mobility prediction and more stable communication performance in dynamic indoor environments.

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arXiv2025

Machine Learning-Driven Adaptive Power Allocation for Optical Wireless Networks

Vertical Cavity Surface Emitting Lasers (VCSELs) have gained popularity in Optical Wireless Communication (OWC) due to their high modulation bandwidth, narrow spectral width, and directional beam, offering improved spectral efficiency and reduced multipath dispersion compared to Light Emitting Diodes (LEDs). In this work, we explore the deployment of VCSELs as Access Points (APs) in an indoor environment under mobility and time varying user distributions. To enhance performance, a Merged Access Point (MAP) topology is introduced to extend the serving area of each cell, whilst Zero Forcing (ZF) precoding is employed for inter user interference management. A sum rate maximisation problem is then formulated to maintain high quality network operation in the dynamic environment. Although deterministic methods can solve the formulated problem, they become impractical in real time due to computational complexity, particularly under high user mobility and rapidly changing channel conditions. To address this, we propose a hybrid Machine Learning (ML) based solution combining a low complexity distance based user association algorithm with a Convolutional Neural Network (CNN) for adaptive power allocation. Simulation results show that the proposed hybrid association CNN framework achieves near optimal performance while substantially reducing computation complexity relative to optimisation based schemes. Furthermore, it operates in real time, with measured median and P95 inference latencies in the millisecond range, and maintains a small empirical worst-case gap to the Mixed Integer Linear Programming (MILP) optimum, demonstrating both practicality and robustness under mobility.

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arXiv2025

Dynamic Power Allocation For NOMA-Based Transmission in 6G Optical Wireless Networks

OWC has been considered as a key enabling technology to unlock unprecedented speeds of communication, supporting high demands of data traffic. In this paper, infrared lasers are used as optical transmitters operating in an indoor environment under eye safety regulations due to their high modulation speed. To provide efficient multiple access service, NOMA-based transmission is implemented to multiplex messages intended to multiple users in the power domain and maximize the spectral efficiency of our laser-based OWC network. In particular, a BIA outer precoder is designed to coordinate the transmission among multiple APs and determine the precoding matrices for groups of users potential formed according to NOMA principles. For effective use of NOMA, an optimization problem is formulated to maximize the sum rate of the network through forming optimum groups under certain joint conditions, efficient power allocation, high quality of service for each weak and strong users, and high overall system performance. Such optimization problems are defined as max-min fractional programs difficult to solve in practice. Therefore, a dynamic application for NOMA is introduced using two algorithms. First, a RF-aided dynamic algorithm is designed to form multiple groups, where users exchange binary variables among them through an RF system to establish distance-based weight edges, which are used as a metric for the grouping process. Second, a dynamic power allocation is proposed to determine the optimum power allocated to each group, while the users belonging to a certain group receive their traffic demands regardless of their classification as weak or strong. The results show the convergence of the proposed dynamic application to the optimum solution, and its high performance in terms of sum rate, fairness, and energy efficiency compared to counterpart schemes.

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arXiv2025

Adaptive DRL for IRS Mirror Orientation in Dynamic OWC Networks

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

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arXiv2025

Predictive and Proactive Power Allocation For Energy Efficiency in Dynamic OWC Networks

Driven by the exponential growth in data traffic and the limitations of Radio Frequency (RF) networks, Optical Wireless Communication (OWC) has emerged as a promising solution for high data rate communication. However, the inherently dynamic nature of OWC environments resulting from user mobility, and time-varying user demands poses significant challenges for enhanced and sustainable performance. Energy efficiency (EE) is a critical metric for the sustainable operation of next generation wireless networks. Achieving high EE in dynamic OWC environments, especially under time-varying user distributions and heterogeneous service requirements, remains a complex task. In this work, we formulate a discrete-time EE optimisation problem in a dynamic OWC to maximise energy efficiency through determining user connectivity and power allocation. Solving this problem in real time is computationally demanding due to the coupling of user association and power allocation variables over discrete time slots. Therefore, we propose a Probabilistic Demand Prediction and Optimised Power Allocation (PDP-OPA) framework which predicts user arrivals, departures, and traffic demands. Based on these predictions, the framework proactively determines AP-user associations and allocates power dynamically using a Lagrangian-based optimisation strategy. Simulation results demonstrate that the proposed PDP-OPA significantly enhances system performance, providing solutions close to the optimal ones. The proposed framework improves energy efficiency, sum rate, and bit error rate (BER) compared to distance-based user association and uniform power allocation, validating its effectiveness for real time and adaptive resource management in dynamic OWC systems.

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arXiv2025

Two-Agent DRL for Power Allocation and IRS Orientation in Dynamic NOMA-based OWC Networks

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 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 of the proposed DRL algorithm compared to standard DRL algorithms typically used for resource allocation in wireless communication. The results also show that the proposed DRL algorithm achieves higher performance compared to deployments without IRS and with randomly oriented IRS elements.

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arXiv2025

BIA Transmission in Rate Splitting-based Optical Wireless Networks

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 new 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.

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arXiv2024

Reinforcement Learning for Rate Maximization in IRS-aided OWC Networks

Optical wireless communication (OWC) is envisioned as one of the main enabling technologies of 6G networks, complementing radio frequency (RF) systems to provide high data rates. One of the crucial issues in indoor 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 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 the RL algorithms achieve near-optimal solutions that are close to the solutions of mixed integer linear programming (MILP). The results also show that the proposed scheme achieves up to a 45% increase in data rate compared to a traditional scheme that optimizes only the allocation of APs while the mirror elements are assigned to users based on the distance.

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arXiv2024

Energy Efficient Service Placement for IoT Networks

In recent years, there has been a significant expansion in the Internet of Things (IoT), with a growing number of devices being connected to the internet. This has led to an increase in data collection and analysis as well as the development of new technologies and applications. The rise of IoT has also brought about new challenges, such as security concerns and energy efficiency. This study investigates a layered IoT architecture that combines fog and cloud computing, aiming to assess the impact of service placement on energy efficiency. Through simulations, we analyse energy use across Access Fog, Metro Fog, and Cloud Data Centre layers for different IoT request volumes. Findings indicate that Access Fog is optimal for single requests, while Metro Fog efficiently manages higher demands from multiple devices. The study emphasizes the need for adaptive service deployment, responsive to network load variations, to improve energy efficiency. Hence, we propose the implementation of dynamic service placement strategies within Internet of Things (IoT) environments.

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arXiv2024

Intelligent Reflecting Surfaces assisted Laser-based Optical Wireless Communication Networks

The increasing demand for wireless networks of higher capacity requires key-enabling technologies. Optical wireless communication (OWC) arises as a complementary technology to radio frequency (RF) systems that can support high aggregate data rates. However, OWC systems face some challenges including beam-blockage. Intelligent reflecting surfaces (IRSs) can offer alternative pathways for the optical signal, ensuring continuous connectivity. In this work, we investigate the potential of using IRS in an indoor OWC network. In particular, we define a system model of indoor OWC that employs IRS in conjunction with angle diversity transmitters (ADT) using vertical-cavity surface-emitting laser (VCSEL) arrays. The VCSEL beam is narrow, directed, and easy to block, however, it can deliver high data rates under eye safety regulations. Simulation results show that the deployment of IRS can significantly improve the achievable data rates of Laser-based OWC systems.

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arXiv2023

Deep Learning-based Power Allocation in Rate Splitting Optical Wireless Networks

Optical wireless communication (OWC) provides high aggregate data rates in the range of Terabits per second (Tb/s). Specifically, OWC using infrared lasers as transmitters has been considered as a strong candidate in the next generation of wireless communication. Rate splitting (RS) is a transmission scheme derived to improve spectral efficiency in dense wireless networks. In RS, the transmitted power is allocated to different messages, common and private messages, serving multiple users simultaneously, where each user can decode the desired message following a certain procedure. Moreover, two-tier precoding RS scheme has been proposed to overcome the limitations of traditional RS in multi-group scenarios. In this context, power allocation (PA) is a crucial issue, which can affect the performance of RS. Therefore, we formulate a PA optimization problem to enhance the data rates of RS-based OWC networks. However, such optimization problems are complex due to the use of different messages intended to the users. In this paper, we design and train a deep neural network (DNN) model to determine the power allocated to the messages of RS, while fulfilling the demands of users. The results show the accuracy of our trained DNN model when used in an online phase.

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arXiv2023

AI-Driven Resource Allocation in Optical Wireless Communication Systems

Visible light communication (VLC) is a promising solution to satisfy the extreme demands of emerging applications. VLC offers bandwidth that is orders of magnitude higher than what is offered by the radio spectrum, hence making best use of the resources is not a trivial matter. There is a growing interest to make next generation communication networks intelligent using AI based tools to automate the resource management and adapt to variations in the network automatically as opposed to conventional handcrafted schemes based on mathematical models assuming prior knowledge of the network. In this article, a reinforcement learning (RL) scheme is developed to intelligently allocate resources of an optical wireless communication (OWC) system in a HetNet environment. The main goal is to maximise the total reward of the system which is the sum rate of all users. The results of the RL scheme are compared with that of an optimization scheme that is based on Mixed Integer Linear Programming (MILP) model.

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arXiv2023

Energy Efficient Laser-Based Optical Wireless Communication Networks

Rising data demands are a growing concern globally. The task at hand is to evolve current communication networks to support enhanced data rates while maintaining low latency, energy consumption and costs. To meet the above challenge, Optical Wireless Communication (OWC) technology is proposed as a solution to complement traditional Radio Frequency (RF) based communication systems. Recently, Vertical Cavity Surface Emitting Lasers (VCSELS) have been considered for data transmission in OWC indoor environments due to their ability to transmit power through narrow, near-circular beams to receivers. In this paper, we study the energy efficiency of a VCSEL-based OWC system in an indoor environment and compare it to that of a Light Emitting Diode (LED) based system. The main findings show that the VCSEL system performs better and has higher energy efficiency.

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