Tu Dac Ho

The rapid growth of the information industry imposes strict demands on future wireless communication systems, especially in high-mobility vehicular scenarios. To address these challenges, we employ orthogonal time frequency space (OTFS) modulation to mitigate Doppler effects and enhance robustness in high-speed environments. In this paper, we examine a superimposed pilot technique for OTFS channel estimation and introduce an optimized two-stage ResNet-based architecture at the receiver. Specifically, the ResNet core leverages residual connections to extract the 2D macroscopic structural envelope of the delay-Doppler channel, effectively decoupling the severe pilot-data interference inherent in superimposed schemes. Furthermore, historical channel estimation data are integrated as a locally quasi-stationary reference to prevent noise overfitting and increase accuracy in subsequent tracking tasks. The system performance is comprehensively evaluated using Normalized Mean Square Error (NMSE) and Bit Error Rate (BER) metrics. Simulation results demonstrate that our proposed method significantly lowers the estimation error floor and outperforms existing traditional and deep learning-based approaches under various high-mobility conditions.

This paper introduces a novel vehicle-to-vehicle (V2V) visible light communication (VLC) system leveraging the multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) framework to improve communication reliability and ensure user fairness under realistic outdoor conditions. The proposed system employs commercial headlamps as dual transmitters and two rear-mounted photodetectors (PDs) as receivers. To enable intensity modulation, we adopt direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) technique, while a zero-force (ZF) detector separates spatial streams at the receiver. Power domain multiplexing utilizes normalized gain difference power allocation (NGDPA), and both perfect and imperfect successive interference cancellation (SIC) scenarios are investigated. For realistic modeling, a non-sequential ray-tracing channel model captures headlamp radiation patterns and environmental effects. A comprehensive evaluation of the system is conducted through the metrics of received power, achievable rate, bit error ratio (BER), and user fairness, considering the effect of key parameters including the size of the PD aperture, weather-induced attenuation, the bandwidth of the system, and artificial light interference. Results indicate that larger PD apertures significantly enhance BER performance, whereas increasing bandwidth tends to raise error rates. Moreover, the proposed system achieves up to 20% higher achievable rates compared to conventional OFDMA, particularly at high transmit power levels. Fairness indices of 0.99 and 0.94 are observed for perfect and imperfect SIC, respectively, confirming the framework’s ability to balance performance and fairness. These findings highlight the potential of the proposed NOMA-based techniques for next-generation intelligent vehicular networks.

With the rapid development of technology, the number of smart mobile users is increasing, accompanied by growing demands from applications such as virtual/augmented reality (VR/XR), remote surgery, autonomous vehicles, and real-time holographic communications, all of which require high transmission rates and ultra-low latency in 6G and beyond networks (6G). This poses enormous challenges for the efficient deployment of large-scale networks, including network design, planning, troubleshooting, optimization, and maintenance, without affecting the user experience. Network Digital Twin (NDT) has emerged as a potential solution, enabling the creation of a virtual model that reflects the actual network and supports the simulation of various network designs, the application of diverse operating policies, and the reproduction complex fault scenarios under real-world conditions. This motivates us to conduct this study, where we provide a comprehensive survey of NDT in the context of 6G, covering areas such as radio access networks (RAN), transport networks, 5G core networks and beyond (5GCORE), cloud/edge computing, applications (blockchain, health system, manufacturing, security, and vehicular networks), non-terrestrial networks (NTNs), and quantum networks, from both academic and industrial perspectives. In particular, we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT. Then, we provide an extensive review of foundation technologies such as transport networks, cloud/edge computing, applications, NTNs, and quantum networks in NDT. Finally, we discuss the key challenges, open issues, and future research directions for NDT in the context of 6G.

The exponential proliferation of the Internet of Things (IoT) and optical IoT (O-IoT) has introduced substantial challenges concerning computational capacity and energy efficiency. IoT devices generate vast volumes of aggregated data and require intensive processing, often resulting in elevated latency and excessive energy consumption. Task offloading has emerged as a viable solution; however, many existing strategies fail to adequately optimize both latency and energy usage. This paper proposes a novel task-offloading approach based on deep Q-network (DQN) learning, designed to intelligently and dynamically balance these critical metrics. The proposed framework continuously refines real-time task offloading decisions by leveraging the adaptive learning capabilities of DQN, thereby substantially reducing latency and energy consumption. To further enhance system performance, the framework incorporates optical networks into the IoT–fog–cloud architecture, capitalizing on their high-bandwidth and low-latency characteristics. This integration facilitates more efficient distribution and processing of tasks, particularly in data-intensive IoT applications. Additionally, we present a comparative analysis between the proposed DQN algorithm and the optimal strategy. Through extensive simulations, we demonstrate the superior effectiveness of the proposed DQN framework across various IoT and O-IoT scenarios compared to the BAT and DJA approaches, achieving improvements in energy consumption and latency of 35%, 50%, 30%, and 40%, respectively. These findings underscore the significance of selecting an appropriate offloading strategy tailored to the specific requirements of IoT and O-IoT applications, particularly with regard to environmental stability and performance demands.

The exponential growth of the Internet of Things (IoT) has generated significant challenges related to computing capacity and energy consumption. IoT devices produce large amounts of combined data and demand processing tasks, often leading to high latency and redundant energy consumption. Task offloading has emerged as an applicable solution; however, existing strategies often neglect optimization of latency and energy consumption. This paper introduces a novel task-offloading approach based on deep Q-network (DQN) learning, designed to intelligently and dynamically balance these important metrics. The proposed framework continuously optimizes real-time task offloading decisions by deploying the adaptive learning capabilities of DQN, thus significantly improving latency and energy consumption. To further enhance performance, the framework integrates optical networks into IoT-fog-cloud architecture, leveraging their high-bandwidth and low-latency capabilities. This integration enables tasks to be distributed and processed more efficiently, especially in data-intensive IoT applications. In addition, we present a comparative analysis of the proposed DQN algorithm and the optimal strategy. Through extensive simulations, we demonstrate the effectiveness of the proposed DQN framework in different IoT scenarios over the BAT and DJA approaches, enhancing energy consumption and latency by 35%, 50%, 30%, and 40%, respectively. Our results further demonstrate the importance of selecting an appropriate strategy according to the specific requirements of the IoT application, particularly in terms of environmental stability and performance requirements.

With the rapid development of technology, the number of smart mobile users is increasing, accompanied by growing demands from applications such as virtual/augmented reality (VR/XR), remote surgery, autonomous vehicles, and real-time holographic communications, all of which require high transmission rates and ultra-low latency in 6G and beyond networks (6G+). This poses enormous challenges in efficiently deploying large-scale networks, including network design, planning, troubleshooting, optimization, and maintenance, without affecting the user experience. Network Digital Twin (NDT) has emerged as a potential solution, enabling the creation of a virtual model that reflects the actual network, supporting the simulation of various network designs, applying diverse operating policies, and reproducing complex fault scenarios under real-world conditions. This motivate us for this study, where we provide a comprehensive survey of NDT in the context of 6G+, covering areas such as radio access networks (RAN), transport networks, 5G core networks and beyond (5GCORE+), cloud/edge computing, applications (blockchain, health system, manufacturing, security, and vehicular networks), non-terrestrial networks (NTNs), and quantum networks, from both academic and industrial perspectives. In particular, we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT. Then, we provide an extensive review of foundation technologies such as transport networks, cloud/edge computing, applications, NTNs, and quantum networks in NDT. Finally, we discuss the key challenges, open issues, and future research directions for NDT in the context of 6G+.

This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. A secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions.

This study investigates the technical aspects of the 1550nm free space optical (FSO) link in extreme Arctic weather, specifically in falling snow, which is currently understudied. We measured the time variation of the received optical power in many snowfall events and derived attenuation statistics. Continuous measurements allowed us to estimate attenuation under varying temperatures and humidity to examine the after-snow channel impairments. Our results show several shortcomings in the International Telecommunication Union-Radio Communication (ITU-R) snow attenuation model and verify that a correlation exists between snow features, snowfall rate, temperature, and humidity in determining the total snow attenuation. We demonstrated the relationship between snow properties, temperature, humidity, and attenuation under both in-snow and post-snow environments. Furthermore, based on the experimental results, we recommend several mitigation techniques to realize FSO communication during challenging Arctic weather conditions. Lastly, we present a feasibility study of FSO in Svalbard at 78°N latitude.