Search Results (145)

Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as a result of worries about possible health problems connected to high-frequency radiofrequency transmission. Using the visible light spectrum is one promising approach; three cutting-edge technologies are emerging in this regard: Optical Camera Communication (OCC), Light Fidelity (Li-Fi), and Visible Light Communication (VLC). In this paper, we propose a Multiple-Input Multiple-Output (MIMO) modulation technology for Internet of Things (IoT) applications, utilizing an LED array and time-domain on-off keying (OOK). The proposed system is compatible with both rolling shutter and global shutter cameras, including commercially available models such as CCTV, webcams, and smart cameras, commonly deployed in buildings and industrial environments. Despite the compact size of the LED array, we demonstrate that, by optimizing parameters such as exposure time, camera focal length, and channel coding, our system can achieve up to 20 communication links over a 20 m distance with low bit error rate. Full article

This paper proposes a tree-based regression model for hybrid channel estimation in wireless sensor networks (WSNs) in generalized frequency division multiplexing (GFDM) over both visible light communication (VLC) and radio frequency (RF) links. The hybrid channel incorporates both additive white Gaussian noise (AWGN) and Rayleigh fading to mimic realistic environments. Traditional estimators, such as MMSE and LMMSE, often underperform in such heterogeneous and nonlinear conditions due to their analytical rigidity. To overcome these limitations, we introduce a data-driven approach using a decision tree regressor trained on 18,000 signal samples across 36 SNR levels. Simulation results show that support vector machine (SVM) achieved 91.34% accuracy and a BER of 0.0866 at 10 dB, as well as 96.77% accuracy with a BER of 0.0323 at 30 dB. Random forest achieved 91.01% accuracy and a BER of 0.0899 at 10 dB, as well as 97.88% accuracy with a BER of 0.0212 at 30 dB. The proposed tree model attained 90.83% and 97.63% accuracy with BERs of 0.0917 and 0.0237, respectively, at the corresponding SNR values. The distinguishing advantage of the tree model lies in its inference efficiency. It completes predictions on the test dataset in just 45.53 s, making it over three times faster than random forest (140.09 s) and more than four times faster than SVM (189.35 s). This significant reduction in inference time makes the proposed tree model particularly well suited for real-time and resource-constrained WSN scenarios, where fast and efficient estimation is often more critical than marginal gains in accuracy. The results also highlight a trade-off, where the tree model provides sub-optimal predictive performance while significantly reducing computational overhead, making it an attractive choice for low-power and latency-sensitive wireless systems. Full article

The growing demand for bandwidth-intensive vehicular applications—particularly ultra-high-definition streaming and immersive panoramic video—is pushing current network infrastructures beyond their limits, especially in urban areas with severe congestion and degraded user experience. To address these challenges, we propose an aerial-assisted vehicular network architecture that integrates 6G base stations, distributed massive MIMO networks, visible light communication (VLC), and a heterogeneous aerial network of high-altitude platforms (HAPs) and drones. At its core is a context-aware dynamic bandwidth allocation algorithm that intelligently routes infotainment data through optimal aerial relays, bridging connectivity gaps in coverage-challenged areas. Simulation results show a 47% increase in average available bandwidth over conventional first-come-first-served schemes. Our system also satisfies the stringent latency and reliability requirements of emergency and live infotainment services, creating a sustainable ecosystem that enhances user experience, service delivery, and network efficiency. This work marks a key step toward enabling high-bandwidth, low-latency smart mobility in next-generation urban networks. Full article

The capabilities of 6G networks surpass those of existing networks, aiming to enable seamless connectivity between all entities and users at any given time. A critical aspect of achieving enhanced and ubiquitous mobile broadband, as promised by 6G networks, is merging satellite networks with land-based networks, which offers significant potential in terms of coverage area. Advanced routing techniques in next-generation network technologies, particularly when incorporating terrestrial and non-terrestrial networks, are essential for optimizing network efficiency and delivering promised services. However, the dynamic nature of the network, the heterogeneity and complexity of next-generation networks, and the relative distance and mobility of satellite networks all present challenges that traditional routing protocols struggle to address. This paper provides an in-depth analysis of 6G networks, addressing key enablers, technologies, commitments, satellite networks, and routing techniques in the context of 6G and satellite network integration. To ensure 6G fulfills its promises, the paper emphasizes necessary scenarios and investigates potential bottlenecks in routing techniques. Additionally, it explores satellite networks and identifies routing challenges within these systems. The paper highlights routing issues that may arise in the integration of 6G and satellite networks and offers a comprehensive examination of essential approaches, technologies, and visions required for future advancements in this area. 6G and satellite networks are associated with technical terms such as AI/ML, quantum computing, THz communication, beamforming, MIMO technology, ultra-wide band and multi-band antennas, hybrid channel models, and quantum encryption methods. These technologies will be utilized to enhance the performance, security, and sustainability of future networks. Full article

Visible light communication (VLC) has become one important candidate technology for beyond 5G and even 6G wireless networks, mainly thanks to its abundant unregulated light spectrum resource and the ubiquitous deployment of light-emitting diodes (LED)-based illumination infrastructures. Due to the high directivity of VLC channel propagation, relay-based cooperative techniques have been introduced and explored to enhance the transmission performance of VLC links. Nevertheless, almost all current works are limited to scenarios adopting well-known Lambertian transmitter and relay, which fail to characterize the scenarios with distinctive non-Lambertian transmitter or relay. For filling this gap, in this article, relay-assisted VLC employing diverse non-Lambertian optical beam configurations is proposed. Unlike the conventional Lambertian transmitter and relay-based research paradigm, the presented scheme employs the commercially available non-Lambertian transmitter and relay to configure the cooperative VLC links. Numerical results illustrate that up to 40.63 dB SNR could be provided by the proposed non-Lambertian relay-assisted VLC scheme, compared with about a 34.22 dB signal-to-noise ratio (SNR) of the benchmark Lambertian configuration. Full article

Nowadays, underwater activities are becoming more and more important. As the number of underwater sensing devices grows rapidly, the amount of bandwidth needed also increases very quickly. Apart from underwater communication, direct communication across the water–air interface is also highly desirable. Air-to-water wireless transmission is crucial for sending control information or instructions from unmanned aerial vehicles (UAVs) or ground stations above the sea surface to autonomous underwater vehicles (AUVs). On the other hand, water-to-air wireless transmission is also required to transmit real-time information from AUVs or underwater sensor nodes to UAVs above the water surface. Previously, we successfully demonstrated a water-to-air optical camera-based OWC system, which is also known as optical camera communication (OCC). However, the reverse transmission (i.e., air-to-water) using OCC has not been analyzed. It is worth noting that in the water-to-air OCC system, since the camera is located in the air, the image of the light source is magnified due to diffraction. Hence, the pixel-per-symbol (PPS) decoding of the OCC pattern is easier. In the proposed air-to-water OCC system reported here, since the camera is located in the water, the image of the light source in the air will be diminished in size due to diffraction. Hence, the PPS decoding of the OCC pattern becomes more difficult. In this work, we propose and experimentally demonstrate a wide field-of-view (FOV) air-to-water OCC system using CUDA Deep-Neural-Network Long-Short-Term-Memory (CuDNNLSTM). Due to water turbulence and air turbulence affecting the AUV and UAV, a precise line-of-sight (LOS) between the AUV and the UAV is difficult to achieve. OCC can provide wide FOV without the need for precise optical alignment. Results revealed that the proposed air-to-water OCC system can support a transmission rate of 7.2 kbit/s through a still water surface, and 6.6 kbit/s through a wavy water surface; this satisfies the hard-decision forward error correction (HD-FEC) bit-error-rate (BER). Full article

Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions. Full article

Increasing reported works identify that drones could and should be sufficiently utilized to work as aerial base stations in the upcoming 6G aerial vehicular networks, for providing emergency communication and flexible coverage. Objectively, light-emitting diode (LED) based lighting devices are ubiquitously integrated into these commercially available drone platforms for the general purposes of illumination and indication. Impresively, for further enhancing and diversifying the wireless air interface capability of the above 6G aerial vehicular networks, the solid-state light emitter, especially LED-based visible light communication (VLC) technologies, is increasingly introduced and explored in the rapidly developing drone communications. However, the emerging investigation dimension of spatial light beam is still waiting for essential research attention for the LED-based drone VLC. Up to now, to the best of our knowledge, almost all LED-based drone VLC schemes are still limited to conventional Lambertian LED beam configuration and objectively reject these technical possibilities and potential value of drone VLC schemes with distinct non-Lambertian LED beam configurations. The core contribution of the study is overcoming the existing limitation of the current rigid Lambertian beam use, and comparatively investigating the performance of drone VLC with non-Lambertian LED beam configurations for future 6G aerial vehicular networks. Objectively, this work opens a novel research dimension and provides a series of valuable research opportunities for the community of drone VLC. Numerical results demonstrate that, for a typical drone VLC scenario, compared with about 6.40 Bits/J/Hz energy efficiency of drone VLC based on the baseline Lambertian LED beam configuration with the same emitted power, up to about 15.64 Bits/J/Hz energy efficiency could be provided by the studied drone VLC with a distinct non-Lambertian LED beam configuration. These results show that the spatial LED beam dimension should be further elaborately explored and utilized to derive more performance improvement of the 6G aerial vehicular networks oriented drone VLC. Full article

As sixth-generation mobile communication (6G) advances towards ultra-high speed and global coverage, visible light communication (VLC) has emerged as a crucial complementary technology due to its ultra-high bandwidth, low power consumption, and immunity to electromagnetic interference. Modulation format recognition (MFR) plays a vital role in the dynamic optimization and adaptive transmission of VLC systems, significantly influencing communication performance in complex channel environments. This paper systematically reviews the research progress in MFR for VLC, comparing the theoretical frameworks and limitations of traditional likelihood-based (LB) and feature-based (FB) methods. It also explores the advancements brought by deep learning (DL) technology, particularly in enhancing noise robustness, classification accuracy, and cross-scenario adaptability through automatic feature extraction and nonlinear mapping. The findings indicate that DL-based MFR substantially enhances recognition performance in intricate channels via multi-dimensional feature fusion, lightweight architectures, and meta-learning paradigms. Nonetheless, challenges remain, including high model complexity and a strong reliance on labeled data. Future research should prioritize multi-domain feature fusion, interdisciplinary collaboration, and hardware–algorithm co-optimization to develop lightweight, high-precision, and real-time MFR technologies that align with the 6G vision of space–air–ground–sea integrated networks. Full article

This study presents the design and implementation of a vehicular visible light communication (VLC) system that establishes an expandable VLC-based chain network within tunnel environments to facilitate the exchange of driving dynamics data, such as target speed and acceleration, between consecutive vehicles. The primary aim of the proposed system is to improve road safety by reducing the risk of chain collisions and hard braking events, particularly in tunnels, where limited visibility and the absence of global positioning system signals hinder drivers’ ability to accurately assess road conditions. A key feature of the proposed system is its adaptive beam alignment mechanism, which dynamically adjusts the orientation of the light-emitting diode (LED) module on the transmitting vehicle based on rhw wheel angle data estimated by the inertial measurement unit sensor. This adjustment ensures a continuous and reliable communication link with surrounding vehicles, even when navigating curves within the tunnel. Additionally, the proposed system can be integrated into actual vehicles with minimal modification by utilizing a built-in lighting system (i.e., LED taillights), offering a cost-effective and scalable solution to achieve the objective. Full article

Airports are complex environments where efficient localization and intelligent traffic management are essential for ensuring smooth navigation and operational efficiency for both pedestrians and Autonomous Guided Vehicles (AGVs). This study presents an Artificial Intelligence (AI)-driven airport traffic management system that integrates Visible Light Communication (VLC), rerouting techniques, and adaptive reward mechanisms to optimize traffic flow, reduce congestion, and enhance safety. VLC-enabled luminaires serve as transmission points for location-specific guidance, forming a hybrid mesh network based on tetrachromatic LEDs with On-Off Keying (OOK) modulation and SiC optical receivers. AI agents, driven by Deep Reinforcement Learning (DRL), continuously analyze traffic conditions, apply adaptive rewards to improve decision-making, and dynamically reroute agents to balance traffic loads and avoid bottlenecks. Traffic states are encoded and processed through Q-learning algorithms, enabling intelligent phase activation and responsive control strategies. Simulation results confirm that the proposed system enables more balanced green time allocation, with reductions of up to 43% in vehicle-prioritized phases (e.g., Phase 1 at C1) to accommodate pedestrian flows. These adjustments lead to improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian traffic across multiple intersections. Additionally, traffic flow responsiveness is preserved, with critical clearance phases maintaining stability or showing slight increases despite pedestrian prioritization. Simulation results confirm improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian flows. The system also enables accurate indoor localization without relying on a Global Positioning System (GPS), supporting seamless movement and operational optimization. By combining VLC, adaptive AI models, and rerouting strategies, the proposed approach contributes to safer, more efficient, and human-centered airport mobility. Full article

With the rapid development of visible light communication (VLC) technology, traditional modulation schemes can no longer meet the high demands for bandwidth efficiency and signal stability in complex application scenarios. In particular, in orthogonal frequency division multiplexing (OFDM) systems, issues such as the nonlinearity of Light-Emitting Diodes (LEDs) and carrier frequency offset have worsened system performance. To address these challenges, this paper proposes an N-order Minimum Shift Keying (NMSK) OFDM system with Fast Hartley Transform (FHT) for signal mapping. Monte Carlo simulations systematically compare the performance of low-order and high-order NMSK modulations under various conditions. The results indicate that low-order NMSK exhibits superior robustness against bit errors and interference, while high-order NMSK can maintain a stable PAPR and provide higher spectral efficiency in high-bandwidth demand scenarios. Further experiments validate the stability of high-order NMSK in high-density multi-user and Industrial Internet of Things (IIoT) environments, proving its adaptability and effectiveness in such scenarios. The high-order NMSK modulation scheme provides strong support for the reliability and bandwidth efficiency of future 6G VLC networks, offering significant application prospects. Full article

This paper investigates handover in hybrid visible light communication (VLC)/radio frequency (RF) networks. In such a network, mobile users are prone to experience frequent handovers (FHOs). To this end, we propose a collaborative online learning-based handover scheme (COLH) in hybrid VLC/RF 5G systems. By selecting the next access point (AP) to which a user should handover, our goal is to make the user–AP connection as long as possible after the handover, defined as a reward that is learned online through a multi-armed bandit (MAB) framework. Unlike previous schemes based on independent and collective learning, first, our scheme dynamically clusters users with similar feedback on a given AP. Second, the users in the same cluster collaborate in estimating the expected reward for that AP, and the one with the maximum expected reward is selected as the next AP. This scheme can be implemented without extensive offline training and location information; thus, its practicality is greatly enhanced. The simulation results show that the proposal outperforms existing benchmarks on reducing handovers. Full article

This paper presents an innovative approach to improving visible light communication (VLC) systems in total shadowing conditions by integrating intelligent reflecting surfaces (IRSs), angle diversity receivers (ADRs), and recurrent neural networks (RNNs). Two ADR configurations (pyramidal and hemispherical) are evaluated, along with signal combination mechanisms: maximum ratio combining (MRC) and select best combining (SBC). The RNN is employed to dynamically optimize the IRS placement, maximizing the signal-to-noise ratio (SNR) at the ADRs and enhancing overall system performance in non-line-of-sight (NLoS) scenarios. This study investigates the spatial distribution of SNRs in VLC systems using RNN-optimized IRSs, comparing the performance of different ADR configurations and signal combination methods. The results demonstrate significant improvements in received power and the SNR compared to non-optimized setups, showcasing the effectiveness of RNN-based optimization for robust signal reception. This article highlights the potential of machine learning in enhancing VLC technology, offering a practical solution for real-world indoor applications. The findings emphasize the importance of adaptive IRS placement and spur further exploration of advanced algorithms and ADR designs to address challenges in complex indoor environments. Full article