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.

This paper investigates the throughput of a non-orthogonal multiple access (NOMA)-based cognitive radio (CR) system with multiple unmanned aerial vehicle (UAV)-assisted relays under system performance and security constraints. We propose a communication protocol that includes an energy harvesting (EH) phase and multiple communication phases. In the EH phase, the multiple UAV relays (URs) harvest energy from a power beacon. In the first communication phase, a secondary transmitter (ST) uses the collected energy to send confidential signals to the first UR using NOMA. Simultaneously, a ground base station communicates with a primary receiver (PR) under interference from the ST. In the subsequent communication phases, the next URs apply the decode-and-forward technique to transmit the signals. In the last communication phase, the Internet of Things destinations (IDs) receive their signals in the presence of an eavesdropper (EAV). Accordingly, the outage probability of the primary network, the throughput of the secondary network, and the leakage probability at the EAV are analyzed. On this basis, we propose a hybrid search method combining particle swarm optimization (PSO) and continuous genetic algorithm (CGA) to optimize the UR configurations and the NOMA power allocation to maximize the throughput of the secondary network under performance and security constraints.

Vehicular Visible light communication (VLC) technology has recently attracted much interest from researchers and scientists. This technology enables connectivity between vehicles and infrastructures along the road by using vehicles’ headlights and taillights as wireless transmitters. The reliability of vehicle-to-vehicle (V2V) VLC systems is affected by several factors, such as car mobility, optics system design, and visibility conditions, where the first two have the most impact on the VLC system performance. This paper, therefore, focuses on the relative positions of the cars and the design of the optics, especially on the receiving end, which has been proposed with the use of a polar detector instead of the rectangular detectors commonly used in the literature. We investigate the achievable gain compared to the conventional detector for different vehicle locations, utilizing a professional optical system design and ray tracing approach. Then, to improve the performance, we introduce the utilization of an imaging receiver by integrating the polar detector with different optical commercial lens combinations, such as Fresnel and Aspherical lenses. To further improve the V2V system performance, we propose a novel optical lens combination design by integrating double-convex lens with half-Plano-concave lens, which allows the correction of more optical aberrations, such as chromatic and spherical aberration. Utilizing the non-sequential ray tracing tools, we designed these VLC systems and perform a realistic channel modeling study considering the typical 3D CAD models of vehicles and roads as well as the possibility of horizontal and vertical movement between the vehicles. Based on the channel impulse responses (CIRs) obtained from the ray tracing simulations, we analyzed the performance of V2V VLC systems with all lens combinations at different vehicle positions on the road. We further investigated the impact of different system parameters on the overall V2V system performance, such as receiver diameter and bandwidth. The obtained results demonstrated that with a carefully chosen system and lens parameters, the proposed system design of lens combination provides an enhancement of up to 7 dB in total received power compared to the case without a lens. Our results also revealed that the proposed system design outperforms the benchmark ones for all lateral displacements and longitudinal distances.

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.

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.

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance.