High-speed vehicular environments require optical systems capable of joint sensing, positioning, and communication (JSPC) without mechanical tracking. Existing optical and integrated sensing-communication approaches often rely on point-source emitters or camera-based receivers, limiting spatial coverage and update rate under highway dynamics. This work introduces a new class of tracking-free optical JSPC systems that combine structured line-laser illumination with modulating retroreflector (MRR) arrays on vehicles. Two orthogonal line lasers perform synchronized longitudinal and transverse scanning to provide continuous, wide-area coverage across the roadway. A coverage-driven analytical framework models the coupling between beam divergence, scan geometry, and dwell-time allocation, enabling joint evaluation of sensing reliability and communication quality. An optimization scheme is developed to adapt scanning and divergence parameters for uniform coverage and power efficiency. Simulation results demonstrate significant improvements in spatial coverage uniformity, link stability, and reliability within a fixed scan period. These results establish a practical pathway toward scalable, turbulence-resilient optical architectures for next-generation vehicular JSPC networks.

This paper proposes a novel indoor optical wireless positioning (IOWP) framework that aims to enhance localization precision and robustness through an advanced machine learning (ML)-driven fusion technique. Unlike traditional single-model approaches, the proposed framework uses received signal strength (RSS) data to intelligently combine multiple lightweight ML algorithms, including K-Nearest Neighbors (KNN), Random Forest (RF), and Gaussian Process Regression (GPR). In the training phase, our system utilizes a performance-optimized weight allocation strategy to identify the optimal weights, harnessing the complementary strengths of individual models while mitigating their limitations to achieve exceptional generalization in complex indoor environments. A comprehensive evaluation is conducted under a realistic ray-traced channel model that incorporates typical light distributions, high-order multipath reflections from walls and objects, and mixed diffuse-reflective surface interactions. Performance is assessed in terms of mean positioning error (MPE), 90th percentile (P90) error, and computational complexity. Results demonstrate that the proposed method achieves an MPE of 0.5 cm and a P90 error below 1 cm, offering a practical and scalable solution for next-generation IOWP applications in smart environments.

As 6G and beyond aim for sustainable, high-capacity wireless connectivity, optical wireless communication (OWC) has emerged as a compelling solution. Recent advances in vertical-cavity surface-emitting laser (VCSEL) arrays have significantly enhanced OWC performance, enabling high-speed, low-power data transmission. However, dense VCSEL deployments introduce challenges related to interference and energy efficiency (EE). This paper proposes a scalable precoding framework for EE maximization in fully cooperative VCSEL-based OWC broadcast systems. We formulate a non-convex optimization problem to design the precoding matrix under practical optical constraints while guaranteeing minimum user rates. To solve this, we apply Dinkelbach’s method to handle the fractional objective and the inner approximation technique to iteratively convexify and solve the problem. Simulation results show that our approach consistently outperforms regularized zero-forcing in terms of EE, particularly in large-scale deployments, demonstrating its potential for next-generation sustainable dense OWC networks.

Achieving high-speed optical wireless communication (OWC) with efficient energy usage and full coverage in dynamic environments remains a significant challenge, particularly due to misalignment issues caused by user mobility and random receiver orientations. To address these challenges, this study introduces an innovative reconfigurable intelligent surfaces (RIS)-assisted laser-based light-fidelity (LiFi) system enhanced for energy efficiency and comprehensive coverage. An algorithm is developed to optimize the placement of RIS, reducing the need for continuous real-time adjustments and decreasing system complexity. Moreover, this study introduces a novel power allocation algorithm for multi-tier access points (APs) designed to reduce power consumption. Numerical results demonstrate the superiority of the proposed algorithm over previous designs in terms of transmitted power and outage probability.

In optical wireless communication (OWC) networks, managing interference and enhancing data rates are critical challenges, particularly in environments with multiple users. This paper investigates the impact of the receiver's field of view (FoV) on interference reduction and signal-to-interference-plus-noise ratio (SINR) improvement in light-fidelity (LiFi), which is a networked OWC system. A narrower FoV can effectively limit the reception of unwanted signals, thereby mitigating inter-user interference and enhancing SINR. This work explores how FoV optimization contributes to interference suppression while maximizing SINR. Additionally, the integration of liquid crystal lenses (LCL) to the receiver permits a dynamic FoV for achieving interference-resistant communication. LCLs adjust the focus by applying an electric field to them, and have faster response time in comparison to the coherence time of LiFi. By incorporating realistic scenarios, including both line-of-sight (LoS) and non-LoS (NLoS) propagation, as well as random device orientations, and formulating an optimization problem to determine the optimal FoV, this study provides valuable insights for designing interference-resistant and high-performance LiFi systems. The results emphasize that employing a dynamic FoV enhances the average SINR by approximately 4 dB across the entire room, even under random device orientations. Moreover, configuring the receiver with a dynamic FoV range between 40° to 55° consistently yields high SINR throughout the room.

Laser-based optical wireless communication (OWC) can provide ultra-high-speed mobile connectivity for future networks. To address the misalignment challenges inherent in laser-based OWC systems, this paper introduces a novel intelligent reflecting surface (IRS)-assisted indoor OWC system. This system employs a multi-cell architecture and leverages passive IRS elements to enhance user coverage. The concept of angular coverage in indoor laser-based communication is introduced, and an analytical framework is proposed to calculate the angular coverage probability of users. Additionally, an innovative IRS deployment strategy, inspired by circle-packing principles, is proposed to enhance the angular coverage of users with varying locations and orientations. Numerical results validate the proposed IRS deployment strategy, demonstrating its superior performance compared to a conventional laser-based OWC architecture, achieving at least a threefold improvement in angular coverage probability.

To support performance requirements for smart services in 6G, user positioning is a crucial component. Indoor user position and orientation estimation based on Light Fidelity (LiFi) system is considered as a promising technology, due to its high precision, along with its ease of installation. The main bottleneck of user position and orientation estimation in LiFi is a non-linearity between the metrics, such as the received signal strength (RSS), position and orientation. A deep learning-based estimation methodology holds promise for addressing this issue, because it can learn complex propagation features dependent on user position and orientation. To fully capitalize on this advantage in the time-domain, we propose utilizing both time-series RSS and its received time, i.e. time-of-arrival (ToA) fingerprints, along with a novel neural network architecture named Deep RSS-ToA Fusion Network (DRTFNet). Simulation results demonstrate that the proposed DRTFNet achieves positioning accuracy of less than 3 cm and orientation accuracy of less than 3 degrees, outperforming both the basic Convolutional Neural Network (CNN) architecture using only RSS data and other baseline systems with more light sources.

Light-fidelity (LiFi) is a networked optical wireless communication (OWC) solution to achieve high-speed mobile communications. To address the misalignment challenges encountered in laser-based LiFi, this study introduces an innovative full-coverage indoor LiFi system with integrated communication and positioning capabilities, leveraging intelligent reflected surfaces (IRSs). By design, the proposed system ensures successful wireless downlink connectivity, irrespective of the user's random location and orientation status. An algorithm is developed to ascertain the optimal deployment of both access points (APs) and IRS layers. Moreover, this study introduces a machine learning (ML)-based OWC positioning approach designed to enhance the accuracy of the user positioning, thereby effectively boosting the performance of the IRS-assisted communication system. Numerical results demonstrate the superiority of the proposed positioning approach over traditional methods in terms of average data rate.

Load balancing (LB) is a key challenge in hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets), due to the nature of heterogeneous access points (APs). Machine learning has the potential to provide a complexity-friendly LB solution with near-optimal network performance, at the cost of a non-trivial training process. The state-of-the-art learning-aided LB methods require retraining when the network environment (particularly the user number) changes, significantly limiting their practicability. In this paper a novel deep neural network (DNN) structure, named adaptive target-condition neural network (A-TCNN), is proposed to tackle the LB issue for a varying number of users, without the need for retraining. Unlike the existing LB methods conducting AP selection for all users together, the new method performs AP selection for a single target user, upon the condition of other users. Also, A-TCNN involves an adaptive mechanism which maps any smaller number of users to a preset number by splitting the users’ data rate requirements, without affecting the AP selection result for the target user. Once trained, A-TCNN can be used for any user numbers not exceeding the maximum user number that the network can support. Results show that apart from the adaptiveness to a varying user number, A-TCNN provides a higher network throughput (up to 45%) than the conventional DNN in most cases, especially for a larger scale of network. In terms of computational complexity, A-TCNN can achieve a sub-millisecond level runtime, which is 2 orders of magnitude lower than fuzzy logic and 3 orders of magnitude lower than game theory.

The rise of intelligent reflecting surfaces (IRSs) is opening the door for unprecedented capabilities in visible light communication (VLC) systems. By controlling light propagation in indoor environments, it is possible to manipulate the channel conditions to achieve specific key performance indicators. In this paper, we investigate the role that IRSs can play in boosting the secrecy capacity of non-orthogonal multiple access (NOMA) VLC systems. More specifically, we propose an IRS-based physical layer security (PLS) mechanism that mitigates the information leakage risk inherent in NOMA. Our results demonstrate that the achieved secrecy capacity can be enhanced by up to 105% for a number of 80 IRS elements. To the best of our knowledge, this is the first paper that examines the PLS of NOMA-based IRS-assisted VLC systems.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. In this paper, the joint estimation of user 3D position and user equipment (UE) orientation in indoor LiFi systems with unknown emission power is investigated. Existing solutions for this problem assume either ideal LiFi system settings or perfect knowledge of the UE states, rendering them unsuitable for realistic LiFi systems. In addition, these solutions consider the non-line-of-sight (NLOS) links of the LiFi channel gain as a source of deterioration for the estimation performance instead of harnessing these components in improving the position and the orientation estimation performance. This is mainly due to the lack of appropriate estimation techniques that can extract the position and orientation information hidden in these components. In this paper, and against the above limitations, the UE is assumed to be connected with at least one access point (AP), i.e., at least one active LiFi link. Fingerprinting is employed as an estimation technique and the received signal-to-noise ratio (SNR) is used as an estimation metric, where both the line-of-sight (LOS) and NLOS components of the LiFi channel are considered. Motivated by the success of deep learning techniques in solving several complex estimation and prediction problems, we employ two deep artificial neural network (ANN) models, one based on the multilayer perceptron (MLP) and the second on the convolutional neural network (CNN), that can map efficiently the instantaneous received SNR with the user 3D position and the UE orientation. Through numerous examples, we investigate the performance of the proposed schemes in terms of the average estimation error, precision, computational time, and the bit error rate. We also compare this performance to that of the k-nearest neighbours (KNN) scheme, which is widely used in solving wireless localization problems. It is demonstrated that the proposed schemes achieve significant gains and are superior to the KNN scheme.

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. Unlike in conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. However, due to the lack of proper channel models for LiFi systems, many studies have assumed that the receiver is vertically upward and randomly located within the coverage area, which is not a realistic assumption from a practical point of view. In this paper, novel realistic and measurement-based channel models for indoor LiFi systems are proposed. Precisely, the statistics of the channel gain are derived for the case of randomly oriented stationary and mobile users. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For mobile users, two channel models are proposed, namely, the sum of modified truncated Gaussian (SMTG) model and the sum of modified Beta (SMB) model. Based on the derived models, the impact of random orientation and spatial distribution of users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and the system performance.

In conventional visible light communication (VLC) systems with multiple light-emitting diodes (LEDs) and multiple photodiodes (PDs), high data rate transmission with limited modulation bandwidth can be achieved via spatial multiplexing (SMP) or wavelength division multiplexing (WDM). However, the number of multiplexing channels is limited by the strong spatial correlation in SMP and by the inter-colour crosstalk in WDM. In this paper, we propose a multiple-input multiple-output (MIMO) hybrid multiplexing (HMP) VLC system which avoids the disadvantages of SMP/ WDM and explores the degrees-of-freedom (DoFs) in space and wavelength domains jointly. With appropriate system configuration, a MIMO channel matrix with a better channel condition in HMP can be obtained. Eventually, it is able to increase the number of multiplexing channels and support higher data rate transmission.

The performance of visible light communication (VLC) systems are limited by the modulation bandwidth of light emitting diodes (LEDs), despite the availability of the wide optical spectrum. A multiple-input, multiple-output (MIMO) scheme can be employed to further improve the data rate by means of harnessing the spatial multiplexing gain. Nevertheless, it is known that the MIMO VLC channel can be rank-deficient, and consequently its performance can be degraded significantly. The rank of the MIMO VLC channel is highly affected by the geometries of light emitting diodes and photodiodes (PDs). In this paper, the effects of PD configurations on the MIMO performances are investigated. It will be shown that certain PD configurations can lead to a rank-deficient channel. We propose Irregular PD configurations (IPC) to overcome this issue. Moreover, bi-objective problems are formulized to obtain the optimal IPC. A significant gain in terms of the condition number of the channel is shown with respect to regular PD configurations, which further suggests a benefit of IPC in MIMO VLC.

Light-Fidelity (LiFi) has been considered as a complementary technology to radio frequency (RF) communications. The reliability of a LiFi channel highly depends on the availability and alignment of line-of-sight (LOS) links. In this study, we investigate the effect of receiver orientation including both polar and azimuth angles on the LOS channel gain in a LiFi system. The optimum tilt angle is calculated, which depends on both the user's location and direction. The probability density function (PDF) of signal-to-noise ratio (SNR) is derived for on-off keying (OOK) modulation. Using the derived PDF of SNR, the bit-error ratio (BER) of OOK in an additive-white Gaussian noise (AWGN) channel with random orientation of the receiver is evaluated. It is shown that the effect of random orientation is negligible if the optimum tilt angle is chosen. Finally, we assess the effect of random orientation on the Shannon-Hartley upper bound capacity.

Among the challenges of realizing the full potential of light-fidelity (LiFi) cellular networks are user mobility, random device orientation, and blockage. In this paper, we study the impact of those challenges on the performance of LiFi networks in an indoor environment using measurement-based channel models, unlike existing studies that rely on theoretical channel models. In our paper, we adopt spatial modulation (SM) and consider two configurations for the user equipment (TIE). A multidirectional receiver (MDR) structure is proposed, in which the PDs are located on different sides of the TIE, e.g., a smartphone. This configuration is motivated by the fact that conventional structures exhibit poor performance in the presence of random device orientation and blockage. In fact, we show that the MDR outperforms the benchmark structure by over 10 dB at bit-error ratio (BER) of 3.8 × 10-3. Moreover, an adaptive access point (AP) selection scheme for the SM is considered, where the number of APs is chosen adaptively in an effort to achieve the lowest energy requirement for a target BER and spectral efficiency. The user performance with random orientation and blockage in the entire room is evaluated for sitting and walking activities, for which the orientation-based random waypoint (ORWP) mobility model is invoked. Furthermore, we demonstrate that the proposed adaptive technique with SM outperforms the conventional spatial multiplexing system. We also study the performance of the underlying system on the uplink channel where we apply the same techniques used for the downlink channel. It is shown analytically that the multidirectional transmitter (MDT) with adaptive SM is highly energy efficient.