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 broadcast nature of energy harvesting wireless sensor networks (EH-WSNs) allows sensor nodes (SNs) within the coverage range of a transmitter to capture its signals. However, an EH-WSN is vulnerable to eavesdropping and signal interception; therefore, security in the EH-WSNs is of significant interest, and this issue has been addressed over many years. However, no work has studied the existence of a friendly jammer to mitigate the security impact. Thus, this paper proposes a model and optimization scheme that uses a wirelessly powered friendly jammer to improve secrecy in EH-WSNs. The considered EH-WSN model includes multiple power stations, multiple SNs (sources) and their base station, a friendly jammer, and multiple passive eavesdroppers. We divide the model into two phases: 1) the power stations transfer RF energy to the source SNs and 2) the source SNs transmit information to their base station, while a friendly jammer generates jamming signals against multiple eavesdroppers. Using statistical characteristics of the signal-to-noise ratio, the closed-form expressions of the existence probability of the secrecy capacity and secrecy outage probability are derived. We also propose an optimal sensor scheduling scheme to enhance physical layer secrecy (i.e., best-node scheduling), and we demonstrate our method’s superior performance compared with a conventional round-robin scheduling scheme. The analysis of the simulation results supports our hypothesis, which is in line with Monte Carlo simulations.

This paper studies unmanned aerial vehicle (UAV)-aided nonorthogonal multiple access (NOMA)-based mobile-edge computing (MEC) in Internet of Things (IoT) systems in which the uav acts as a relay (UR). Specifically, we consider a scenario with two clusters IoT devices (IDs) (i.e., a high-priority cluster IA and a low-priority cluster IB) with limited resources, so these IDs cannot compute their tasks and must offload them to a base station (BS) through a UR. We propose a protocol named time switching - radio frequency (RF) energy harvesting (EH) UR NOMA (TS-REUN), which is divided into 5 phases. By applying the TS-REUN protocol, the IDs in the two clusters and the UR harvest RF energy from the broadcast signal of the power beacons (PB). Then, the IDs offload their tasks to the MEC server located at the BS. After server processing, the IDs receive the calculation results from the BS via the UR. The effects of both imperfect channel state information (ICSI) and imperfect successive interference cancellation (ISIC) on the REUN-based mec (REUN-MEC) are taken into account. To evaluate the performance of the system, we derive closed-form expressions for the successful computation probability (SCP) and energy consumption probability (ECP) in the Nakagami- $\mathfrak {m}$ fading channel. Moreover, we propose an optimization problem formulation that maximizes the SCP by optimizing the position and the height of the UR and the time switching ratio (TSR). The problem was addressed by employing an algorithm based on particle swarm optimization (PSO). In addition, the Monte Carlo simulation results confirmed the accuracy of our analysis based on system performance simulations with various system parameters, such as the number of antennas at the BS, the number of IDs in each cluster, the TSR, and the position and the height of the UR.

In this paper, we study radio frequency (RF) energy harvesting (EH) in a wireless sensor network (WSN) in the presence of multiple eavesdroppers (EAVs). Specifically, the sensor source and multiple sensor relays harvest energy from multiple power transfer stations (PTSs), and then, the source uses this harvested energy to transmit information to the base station (BS) with the help of the relays. During the transmission of information, the BS typically faces a risk of losing information due to the EAVs. Thus, to enhance the secrecy of the considered system, one of the relays acts as a jammer, using harvested energy to generate interference with the EAVs. We propose a best-relay-and-best-jammer scheme (BBS) for this purpose and compare this scheme with other previous schemes. The exact closed-form expression for the secrecy outage probability (SOP) is obtained and is validated through Monte Carlo simulations. A near-optimal EH time algorithm is also proposed. In addition, the effects on the SOP of key system parameters such as the EH efficiency coefficient, the EH time, the distance between the relay and BS, the number of PTSs, the number of relays, and the number of EAVs are investigated. The results indicate that the proposed scheme generally outperforms both the best-relayand-random-jammer scheme (BRS) and the random-relay-andbest-jammer scheme (RBS) in terms of the secrecy capacity.

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.

This paper investigates system performance in the Internet of Things (IoT) with an energy harvesting (EH) unmanned aerial vehicle (UAV)-enabled relay under Nakagami-m fading, where the time switching (TS) and adaptive power splitting (APS) protocols are applied for the UAV. Our proposed system model consists of a base station (BS), two IoT device (ID) clusters (i.e., a far cluster and a near cluster), and a multiantenna UAV-enabled relay (UR). We adopt a UR-aided TS and APS (U-TSAPS) protocol, in which the UR can dynamically optimize the respective power splitting ratio (PSR) according to the channel conditions. To improve the throughput, the nonorthogonal multiple access (NOMA) technique is applied in the transmission of both hops (i.e., from the BS to the UR and from the UR to the ID clusters). The U-TSAPS protocol is divided into two phases. In the first phase, the BS transmits a signal to the UR. The UR then splits the received signal into two streams for information processing and EH using the APS scheme. In the second phase, the selected antenna of the UR forwards the received signal to the best far ID (BFID) in the far cluster and the best near ID (BNID) in the near cluster using the decode-and-forward (DF) or amplify-and-forward (AF) NOMA scheme. We derive closed-form expressions for the outage probabilities (OPs) at the BFID and BNID with the APS ratio under imperfect channel state information (ICSI) to evaluate the system performance. Based on these derivations, the throughputs of the considered system are also evaluated. Moreover, we propose an algorithm for determining the nearly optimal EH time for the system to minimize the OP. In addition, Monte Carlo simulation results are presented to confirm the accuracy of our analysis based on simulations of the system performance under various system parameters, such as the EH time, the height and position of the UR, the number of UR antennas, and the number of IDs in each cluster.