Indoor visible light communication (VLC) has attracted increasing attention as a promising wireless access technology because of its large unlicensed bandwidth and dual functionality of illumination and data transmission. However, practical VLC systems are vulnerable to line-of-sight (LoS) blockage caused by user mobility, human shadowing, and indoor obstacles, which may degrade link reliability and service continuity. Although hybrid VLC/RF networks can improve robustness by using RF transmission as a backup link, excessive RF fallback under severe optical blockage may overload the bandwidth-limited RF interface and reduce the service quality of RF-associated users. To address this issue, this paper investigates a risk-aware illumination-constrained resource allocation scheme for hybrid VLC/RF indoor networks under random optical blockage. A unified system model is developed by considering Lambertian optical propagation, random optical blockage, RF backup transmission, and working-plane illumination constraints. Based on this model, a joint user association and power allocation problem is formulated under QoS, transmit-power, and illumination requirements. The proposed scheme evaluates VLC service utility under blockage uncertainty, controls RF fallback to avoid excessive backup-link loading, allocates VLC/RF transmission power, and performs illumination feasibility adjustment to preserve the required lighting level. Simulation results show that, under severe blockage conditions, the proposed scheme reduces the outage probability to approximately 0.26, compared with 0.68 for VLC-only transmission and 0.47 for threshold-based VLC/RF switching. For a 20-user network, the proposed scheme achieves an average sum rate of approximately 277 Mbps, maintains a 100% illumination compliance ratio, and achieves higher energy efficiency than the benchmark schemes. Further RF backup analysis shows that the proposed scheme can maintain the service quality of RF-associated users by avoiding excessive RF fallback. These results demonstrate the effectiveness of the proposed framework for reliable and illumination-feasible hybrid VLC/RF indoor communication.

Wireless networks are faced with growing demands for high-speed connectivity and reliable coverage in smart buildings, industrial, and space environments. Traditional radio frequency (RF) networks struggle to meet these requirements due to limited bandwidth and spectrum congestion, especially in dense deployments. Visible Light communication (VLC) has emerged as a complementary solution that delivers multi-Gbps backbone links through advanced optical transmission technologies. However, integrating VLC with RF mesh networks presents significant challenges due to VLC's line-of-sight dependence and coverage limitations. Since the physical-layer characteristics of hybrid VLC/RF networks differ fundamentally from RF-only networks, existing routing protocols, such as Q-learning based Traffic-Aware Routing (QTAR) and Multi-Objective Optimized Link State Routing (MO-OLSR), often suffer severe performance degradation. To address this issue, we propose Q-learning-based Hybrid Link Selection (QHLS), a cross-layer routing protocol that employs reinforcement learning to intelligently coordinate VLC and RF transmissions. QHLS introduces a unified link quality metric that jointly considers physical-layer capacity, MAC-layer delay, and network congestion. Using this metric, QHLS enables distributed, adaptive routing decisions that dynamically adjust to real-time network conditions and heterogeneous link characteristics. Simulation results show that QHLS significantly outperforms existing protocols under diverse traffic and mobility scenarios. In high-congestion conditions, QHLS improves throughput by 24.5%–41.8%, reduces end-to-end delay by 60.9%–71.5%, and enhances packet delivery ratio by 6.1%–10.3% compared to QTAR and MO-OLSR. These results highlight the effectiveness of QHLS for hybrid VLC/RF wireless mesh networks.

Side-emitting fibers have been proven to act as distributed transmitters for optical camera communication (OCC) and as distributed receivers for visible light communication (VLC). However, their simultaneous operation over a single side-emitting fiber has not yet been explored. In this work, we demonstrate a bi-directional VLC/OCC link using a single side-emitting fiber. We present the theoretical framework, and through a series of experiments, we show that both OCC and VLC channels perform below the forward-error-correction (FEC) limit of 3.8 · 10-3, thereby confirming the capability of side-emitting fibers as both a distributed transmitter and receiver. These results highlight the potential for bidirectional communication and sensing using low-cost side-emitting fibers, enabling a simple indoor OCC downlink and a short-range VLC uplink. For example, a side-emitting fiber integrated around a doorway could provide identification and verification functionality while simultaneously illuminating an emergency exit.

Optical fibers capable of dynamically generating and/or transporting narrow/broadband spectral signals in the visible spectral region based on the input light, much like how a chameleon changes its color, are quintessential for developing visible light communication devices. Here, we demonstrate a mechanically flexible, blue-violet fluorescent 2-(4,4′-bis(2,6-di(1H-pyrazol-1-yl)pyridin-4-yl)biphenyl) (BPP) crystal waveguide surface coordinated to red fluorescent Eu(tta)3. The BPP microcrystal waveguide, acting as the core, with BPP-Eu(iii) as the shell, provides a hybrid platform for broad and narrow band signal transmission. Depending on the input light and the absorption of the core or shell, the crystal acts as an active–active, passive–active, or passive–passive light-generating and/or transporting optical waveguide. Notably, the pseudo-plasticity of the core–shell hybrid waveguide enables modulation of the signal output direction without compromising its optical performances. The development of such smart optical waveguides has enormous potential for visible light communication and selective light-based microprecision sensing applications.

Accurate indoor positioning in Global Navigation Satellite Systems (GNSS)-denied environments remains a critical challenge for next-generation intelligent spaces, industrial automation, and context-aware services. Visible light communication (VLC) and visible light positioning (VLP) have emerged as attractive solutions due to their dual functionality of illumination and communication, immunity to electromagnetic interference, and potential for high-accuracy localization. However, practical VLC/VLP systems are highly sensitive to receiver orientation, LED/front-end nonlinearities, ambient light, multipath reflections, and hardware-related timing offsets and RSS measurement distortions. In this paper, we propose a robust joint optical delay-received signal strength (RSS) positioning framework for VLC systems, where delay-derived pseudorange information and received optical power measurements are fused under a unified weighted negative log-likelihood formulation. Unlike conventional VLP schemes that rely on either RSS-only or pseudorange-only inference under Gaussian assumptions, the proposed approach explicitly accounts for non-Gaussian measurement distortions and per-link timing bias in the optical domain. The user position and per-link timing-bias parameters are jointly estimated through particle swarm optimization (PSO), enabling robust operation in highly nonlinear and multimodal VLC localization landscapes. In the evaluated LOS-dominant scenario, the proposed joint optical delay-RSS estimator achieves an RMSE of [Formula: see text] and a 95th percentile error of [Formula: see text], outperforming the Optical pseudorange WNLS, RSS-only Lambertian WNLS, and Joint pseudorange-RSS (PSO + Huber) baselines. This framework provides a calibration-efficient and Lambertian-consistent solution for indoor optical positioning and constitutes a promising basis for accurate localization in smart buildings, hospitals, warehouses, and industrial environments. These results should be interpreted within the simulation-based scope of the present study, and experimental validation remains necessary before drawing stronger deployment-oriented conclusions.

Car headlights and taillights, to provide cost-effective and high-data-rate interference-resilient communication, have spawned Vehicular Visible Light Communication (V2LC), a possible additional technology to radio-frequency systems. Since they can be formulated in an empirical or deterministic way, traditional optical channel models can be easily adapted to changing vehicle environments. To counter the weakness of the conventional modelling techniques, the paper examines channel modelling techniques grounded in machine learning and suited to V2LC scenarios. A few variables, such as the shape of LEDs, speed, road topology, and atmospheric perturbations, do significantly influence the received signal strength in vehicular optical channels that are highly non-linear in nature and vary over time. To solve these issues, the paper gives Machine Learning-based Vehicular Visible Light Communication Channel Modelling (ML-V2LC-CM), a hybrid learning system as a proposed framework. The regression models and ensemble learning of the framework and deep neural predictors are all integrated, which can be used to estimate the channel gain, path loss, and signal distortion. Compared to baseline empirical models, experimental tests show that ML-V2LC-CM is significantly more effective. The framework performs well and has the highest performance of 18.7% in terms of improvement in prediction, 22.4% in terms of reduction in Root Mean Square Error (RMSE), Signal-to-Noise Ratio (SNR) estimation error of less than 1.6 dB, and low prediction latency of 4-7ms. It is very robust when blocked with a degradation rate of 6-9% and gives over 92% generalization behaviour under a variety of lighting conditions and provides fusion-layer improvement of 11.3% greater than the best standalone model. The model demonstrates high consistency in channel stability estimation of more than 95% even in high rate of movement of vehicles.

Abstract Underwater visible light communication (UVLC) has emerged as a promising solution for high-speed and low-latency underwater communication; however, its performance is fundamentally constrained by severe channel impairments and the limited energy availability of underwater sensor nodes. In this paper, an intelligent reflecting surface (IRS)-assisted UVLC framework with energy harvesting is proposed, where both spatial and temporal resources are jointly optimized to enhance system reliability and sustainability. Specifically, the IRS is adaptively partitioned into two groups to simultaneously support energy harvesting and data transmission, while the transmission frame is dynamically divided to determine the optimal harvesting duration. Unlike existing works that rely on static IRS allocation and static harvesting time, the proposed scheme formulates a joint optimization problem to maximize the ergodic capacity under a minimum energy constraint. By using Karush-Kuhn-Tucker (KKT) conditions, closed-form expressions for the optimal harvesting time and IRS partitioning are obtained, and key insights are provided on the trade-off between energy harvested and information transfer. Furthermore, the closed-form expressions for ergodic capacity, harvested energy, and outage probability are derived using practical underwater channel conditions, such as absorption, scattering, turbulence, different link distances, etc. The simulation results are presented to demonstrate the effectiveness of the proposed framework over the conventional schemes.

Gallium nitride (GaN)-based laser diodes (LDs) have emerged as pivotal light sources for high-speed visible light communication, underwater optical links, quantum and atomic systems. There is a growing demand for visible LDs that operate beyond room temperature conditions, which has not been well investigated. In this work, we carry out a comprehensive experimental investigation into the temperature dependence of the electro-optical and frequency response characteristics of blue GaN LDs over a wide temperature range from 283 K to 363 K. The impact of structural design parameters, including the quantum-well number and cavity length, on the thermal stability has been experimentally studied. Experimental results demonstrate that triple quantum wells (TQWs) LDs exhibit better temperature stability in both electro-optical and dynamic characteristics than the double quantum wells (DQWs) LDs and the single quantum well (SQW) LDs. Additionally, LDs with a longer cavity (700 µm) show enhanced heat dissipation over LDs with a short cavity (300 µm), suggesting the importance of thermal management in device design. These findings provide important design guidelines for developing blue LDs suited for harsh-environment and high-temperature applications.

Wide-angle visible light array communication based on intensity compressed single-pixel imaging

As the demand for high-speed and secure digital communication continues to grow, optical networks have emergedasthe backbone of modernbroadbandinfrastructure. Among these, Fiber to the Home (FTTH) solutions offer unparalleled performance, but face significant challenges in Routing and Wavelength Assignment (RWA). This paper presents a secure and cost-effective prototype that utilizes Visible Light Communication (VLC) to demonstrate RWA functionality within an FTTH environment. Using laser-based opticaltransmission andLabVIEW-basedmodulesformessage encoding, encryption, and visualization, our systemsimulates a real-worldsecurecommunicationchannel.Atthis50%progress stage, partial implementation has been achieved, including string and single-letter transmission, LED writing, and VLC encryption. The results validate the feasibility of secure data transmission via VLC in controlled environments.

Abstract: The number of studies on communication systems has increased more than ever before. Visible Light Communication studies, one of the popular fields of study, are being further researched regarding its ability to illuminate communication and the support structure of radio communication. Visible Light Communication, designed as a strengthening structure for radio communication, has shown that it can provide sufficient performance capability in the laboratory environment without the need for radio communication. Studies focus on the location of transceivers, positioning in mobile communications, coordinate estimation and hardware resource consumption. Optimization is a common theme of studies. Optimization improvements needed in all areas of communication in current studies have been made through the experimental set data, and an important infrastructure has been provided. In the study, the data set is examined with metaheuristic algorithm. It aims to bring popularity to optimization studies with the study on visible light communication systems still in the development stage. The study is based on comparing the experimental set results developed for Visible Light Communication with particle swarm optimization. With the experimental set running on Layer I, the communication performance results were obtained with the number of data preparation repetitions, payload, optical filters, distance, the ambient light, and different LEDs. The dataset obtained from the results was determined and analyzed by particle swarm optimization of metaheuristic algorithms as parameters affecting performance. As a result of the study in which Visible Light Communication was analyzed with a metaheuristic algorithm, it was found that the amount of data payload was the most effective component in communication performance, as expected. Among the results, the communication performance of the communication designed independently from the effect of ambient light interference is affected, albeit to a small extent; LEDs used as transmitters did not affect the communication performance as much as expected.

A discrete Hartley transform (DHT)-based orthogonal frequency division multiplexing (OFDM) scheme is investigated for intensity modulation/direct detection (IM/DD) visible light communication (VLC) systems, where transmitted signals are required to be real-valued and non-negative. To address this constraint, a practical unipolar transmission framework with corresponding bipolar reconstruction is developed. By exploiting the real-valued and self-inverse properties of the DHT, the proposed scheme removes the need for Hermitian symmetry and enables full utilization of available subcarriers. Under equal-bandwidth conditions, this results in an approximately 50% reduction in computational complexity compared with conventional DCO-OFDM and ACO-OFDM schemes. Theoretical analysis and numerical results further show that the proposed approach achieves comparable bit error rate (BER) performance while exhibiting improved spectral confinement, as reflected by reduced out-of-band sidelobes under identical filtering conditions. In addition, it maintains spectral efficiency equivalent to DCO-OFDM under the same bandwidth constraint. These advantages are achieved at the cost of restricting subcarrier modulation to real-valued constellations, which may reduce flexibility in frequency-selective channels. Overall, these findings support DHT-OFDM as a low-complexity, spectrally confined multicarrier waveform for IM/DD VLC systems, particularly in scenarios where efficient spectrum utilization and reduced adjacent-channel interference are required.

4-PAM Visible Light Communication System with Binary Neural Tree Equalization

This study investigates a platoon-oriented visible light communication (VLC) system equipped with a multi-input multi-output adaptive angle-oriented receiver (MIMO-AAOR) under curved-road driving scenarios. To address the degradation of intra-platoon VLC links caused by geometric misalignment and multi-vehicle interference on curved roads, a comprehensive non-sequential ray-tracing model is established by jointly incorporating the three-dimensional geometry of vehicles and roads, LED radiation characteristics, and reflection-scattering properties of both vehicle bodies and road surfaces. An end-to-end link evaluation framework is developed for curved-road conditions, enabling accurate estimation of path loss and the equivalent signal-to-interference-plus-noise ratio (SINR). A single-photodiode VLC scheme (MIMO-PD-VLC) is adopted as the baseline for comparison. Typical sharp-curve, moderate-curve, and near-straight segments are analyzed to evaluate the impact of an AAOR horizontal field of view (FoV), the platoon size, and the cruising speed on system performance. Simulation results demonstrate that an AAOR horizontal FoV of approximately 20∘-25∘ achieves an effective balance between signal aggregation and interference suppression. As the platoon size increases and the speed rises, the proposed MIMO-AAOR-VLC system consistently outperforms the MIMO-PD-VLC baseline. The median SINR gain remains around 12-17dB, confirming robust suppression of intra-platoon interference under dynamic driving. The findings provide quantitative theoretical guidance and an engineering reference for the FoV configuration of vehicular VLC receivers and the co-design of platoon operating parameters under curved-road environments.

Inefficient urban traffic management remains a critical challenge, as conventional signal controllers—built on fixed timing plans—cannot cope with the dynamic nature of modern city traffic. This study addresses this limitation by developing a decentralized MARL-based framework capable of coordinating five interconnected intersections as a unified traffic cell. Central to the proposed solution is the Strategic Anti-Blocking Phase Adjustment (SAPA) module, which enables intersections to autonomously modify phase durations in response to real-time traffic conditions. The framework is designed to handle heterogeneous demand patterns, with particular emphasis on arterial corridors connecting urban centers to peripheral zones. Integration of a Visible Light Communication (VLC) network allows continuous monitoring of key variables, including vehicle kinematics and pedestrian activity, feeding the agents with rich environmental feedback. Experimental evaluation confirms the effectiveness of the approach: the SAPA-augmented DQN achieves roughly 33% shorter vehicle queues and a ~70% reduction in pedestrian waiting counts relative to a standard DQN baseline. Remarkably, these gains bring the value-based method to a performance level comparable to MAPPO, a considerably more complex multi-agent policy optimization algorithm, establishing SAPA as an efficient and scalable enhancement for intelligent urban traffic control.

With the advent of 6G mobile communication, the demand for ultra-high bandwidth wireless communication has increased rapidly, drawing significant attention to visible light communication (VLC) as a promising emerging technology. GaN-based laser diodes (LDs) are regarded as high-speed light sources for VLC owing to their high modulation bandwidth and high optical power density. Apart from the active region design, the LD’s structure also plays a crucial role in determining their dynamic properties, which have yet to be thoroughly studied in III-nitride LDs. In this work, we systematically investigate InGaN/GaN laser diodes with three ridge waveguide configurations: a conventional single-ridge structure, a dual-ridge large-mesa structure, and a dual-ridge small-mesa structure. The threshold current, small-signal modulation bandwidth of devices with different structures are comparatively analyzed. Experimental results reveal that the double-ridge small mesa laser diode achieves a modulation bandwidth of −3 dB at 6.02 GHz. These results provide valuable insights into the structural optimization of GaN-based high-speed laser diodes and offer practical guidance for the development of high-performance, energy-efficient VLC transmitters.

The increasing demand for high-speed and secure wireless communication has driven the exploration of alternative technologies, with global data traffic expected to exceed 180 zettabytes annually and traditional RF-based systems facing challenges such as bandwidth limitations, interference, and security vulnerabilities. Traditional wireless communication systems like Wi-Fi and Bluetooth operate on radio frequencies, which are prone to interference, congestion, and potential security breaches due to signal leakage beyond physical boundaries. Furthermore, conventional systems may not provide sufficient bandwidth for high-speed data and audio transmission in dense environments. To address these challenges, the proposed Li-Fi communication based secure data and audio transmission system utilizes visible light communication integrated with the Arduino Uno using the UART protocol for efficient asynchronous data transfer. The system employs high-frequency LED flickering to transmit text (hexadecimal data) and audio signals through light waves, which are received and decoded by a photodetector at the receiver end. UART ensures reliable and synchronized communication between modules, enhancing system performance. This optical communication approach provides enhanced security since light signals do not penetrate walls, reducing the risk of interception. The system offers high-speed transmission, low interference, and improved data capacity, making it a robust solution for secure communication applications.

Abstract: Communication dependability and speed are more crucial than ever in the modern world. Even though cellular networks and Wi-Fi have revolutionized connectivity, problems like congestion, interference, and security threats still exist. By using visible light rather than radio waves to transmit data, Li-Fi (Light Fidelity) presents a promising substitute that makes wireless communication quicker and safer. Current Li-Fi systems, however, handle all messages equally, which can delay the delivery of important information, like emergency notifications or medical alerts. Our project presents an AI-Enhanced Li-Fi Communication System that employs artificial intelligence to evaluate, categorize, and rank messages prior to transmission in order to get around this restriction. To ensure that critical information is sent first, the system uses Natural Language Processing (NLP) to comprehend the urgency and content of messages. A smartphone flashlight serves as the Li-Fi transmitter for data transmission, and an LDR sensor that is connected to a microcontroller receives the data. A low-cost, energy-efficient, and context-aware communication model is produced by combining visible light communication with AI intelligence. In addition to improving Li-Fi networks' performance and dependability, it creates new opportunities for smart IoT applications, healthcare systems, and emergency services—all of which depend on prompt communication. Keywords- Li-Fi Communication, Artificial Intelligence, Visible Light Communication, Message Prioritization, LDR Sensor, Microcontroller, NLP.

Abstract Visible light communication (VLC) has emerged as a promising technology for high-speed wireless access in indoor and vehicular environments. However, dense deployment of light-emitting diodes (LEDs) leads to severe co-channel interference, which degrades signal quality and system throughput. This paper proposes a Long Short-Term Memory (LSTM)-based interference prediction and mitigation framework for multiuser VLC networks. The temporal correlation of user mobility and channel variations is exploited using LSTM to forecast future interference levels and dynamically allocate transmit power and bandwidth. A mathematical model of the VLC channel, interference, and signal-to-interference-plus-noise ratio (SINR) is developed, and the optimization problem is formulated to minimize aggregate interference while satisfying quality-of-service constraints. Simulation results demonstrate that the proposed LSTM-based scheme significantly improves SINR, reduces bit error rate, and enhances throughput compared to conventional static and heuristic allocation methods. At a transmit power of 1 W, the throughput under interference is 114 Mbps, while the interference-free benchmark achieves 131 Mbps. The proposed LSTM-assisted framework restores throughput to 128 Mbps, closely approaching ideal conditions. These results confirm the effectiveness of the proposed predictive interference mitigation strategy for next-generation VLC systems.

Visible light communication (VLC) is emerging as a promising technology for future communication systems, with extensive applications in indoor networking, wearable devices, and underwater transmission. Fluorescent antennas (FAs) have demonstrated significant potential in enhancing the field-of-view (FoV) of optical receivers through their absorption and re-emission characteristics. However, a critical mismatch exists between the distributed side-emission pattern of FAs and the concentrated photosensitive area of photodetectors. To address this issue, we propose a novel photodetector design, to the best of our knowledge, employing an array of photodiodes specifically configured to match the spatial emission profile of FAs. Experimental results reveal that the proposed detector achieves higher power gain while maintaining comparable bandwidth to the conventional detector with almost the same photosensitive area. This work presents a cost-effective, high-performance detection solution that significantly improves the performance of wide-FoV VLC systems.