Covert communication and physical layer security (PLS) have emerged as two essential pillars for safeguarding wireless networks against increasingly sophisticated eavesdropping and surveillance threats. This paper investigates a downlink hybrid power–frequency multiple access (HPMA) system aided by a friendly jammer, where a single source communicates with two legitimate users in the presence of a passive eavesdropper. By jointly exploiting the power and frequency domains, the proposed HPMA scheme achieves clear advantages in terms of covertness and secrecy performance compared to conventional orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. For performance analysis, we derive closed-form and approximated expressions for key metrics, including detection error probability (DEP), secrecy outage probability (SOP), effective secrecy throughput (EST), and average secrecy capacity (ASC), which reveal important trade-offs among resource allocation, jammer power, and secrecy reliability. To further reduce the computational burden of conventional analysis, a regression-based deep neural network (DNN) framework is developed to efficiently predict the secrecy performance of the considered system. Extensive Monte Carlo simulations validate the analytical results and confirm the superiority of the proposed HPMA scheme with friendly jamming. These findings provide useful insights for the design of future secure and covert wireless systems with low-complexity and intelligent physical-layer security requirements.

This study investigates a two-way relaying non-orthogonal multiple access (TWR-NOMA) enabled Internet-of-Things (IoT) network, in which two NOMA users communicate via an IoT access point (IAP) relay using a decode-and-forward (DF) protocol. A power beacon (PB) is used to power the IAP to address the IAP's limited lifetime due to energy constraints. Since co-channel interference (CCI) is inevitable in IoT systems, this effect is also studied in the proposed system to improve practicality. Based on the proposed system model, the closed-form equations for the exact and asymptotic outage probability (OP) and ergodic data (ED) of the NOMA users' signals are first derived to describe the performance of TWR-NOMA systems. The system's diversity order and throughput are then evaluated according to the derived results. To further improve the system's performance, a low-complexity strategy 2D golden section search (GSS) is performed, subject to power allocation (PA) and time-switching (TS) factors, to optimize the outage performance. Finally, a deep learning design with minimal computing complexity and precision OP prediction is established for a real-time IoT network configuration. The numerical results are discussed and analyzed in terms of the effects of the CCI, the TS ratio, the PA factor, the fading parameter on the OP, system throughput, and ED.

Unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) systems have been implemented for over a decade, from transportation to military surveillance, and is proven worthy of integration in the next generation of wireless protocols. Though UAVs have immense potential, they have major drawbacks when it comes to real-world implementation, such as energy capacity, loss of signal quality, and spectrum limitations. To overcome these challenges, integration of UAVs with spectrum-efficient techniques, including cognitive radio (CR) and nonorthogonal multiple access (NOMA) has been proposed. In this article, we incorporate transmit-antenna selection (TAS) into an underlay cognitive radio NOMA network, which provides additional benefits through employing multiple-antenna-selection approach at the UAV with the goal of better serving the ground NOMA devices. The links associated with the multiantenna UAV are theoretically assumed to experience Nakagami-$m$fading distribution. We also emphasize the degraded performance caused by imperfect successive interference cancelation (SIC) when decoding signals at the ground NOMA devices. The closed-form expressions for the proposed model are derived to evaluate two main performance metrics, namely, the outage probability and the ergodic capacity. Monte Carlo simulations are performed to analyze the performance of the system in different scenarios. We observe that the power allocation factors for the devices in a group and the altitude of UAV have a noticeable impact on the performance of the system. Furthermore, the increase in the number of antennas at the UAV can complement these effects and further improve the system performance.

In this paper, we investigate improved spectrum concern by employing non-orthogonal multiple access (NOMA) scheme in cognitive radio (CR) network to form CR-NOMA and to serve many destination users. In secondary network of our proposed system, device-to-device (D2D) strategy is designed to support further transmission for NOMA users under impacts from interference from the primary network. Outage performance gap exists among these NOMA users since different power allocation factors assigned to the destinations. Unlike existing NOMA schemes as considering power allocation factors, which are not optimal in terms of outage performance, our proposed paradigm exhibits optimal outage in scenario of D2D transmission. In particular, the outage probability is first derived and approximate outage performance can be further achieved. Simulation results confirm exactness of the analytical expressions and show advantages of the proposed CR-NOMA system in terms of outage performance and throughput.