This paper investigates a network architecture based on unmanned aerial vehicles (UAVs), wherein an aerial jammer (AJ) is deployed to counter unauthorized interception attempts by an aerial eavesdropper (AE). To enhance both network security and energy efficiency, an optimization framework is introduced that aims to maximize the secure energy efficiency (SEE) through the joint adjustment of transmit power, jamming power, and the flight trajectories of legitimate UAVs. The fractional optimization problem is reformulated into a subtractive-form optimization problem using Dinkelbach’s method. Given the non-convex nature of both the objective function and constraints, block coordinate descent (BCD) and successive convex approximation (SCA) techniques are employed. Simulation results demonstrate the effectiveness of this approach in improving SEE and the influence of channel fading on the physical-layer security (PLS).

This paper investigates physical-layer security (PLS) in UAV-assisted communication networks featuring an untrusted amplify-and-forward aerial relay. To counter potential eavesdropping threats, a destination-aided jamming strategy is employed. The proposed solution adopts a hybrid framework that integrates particle swarm optimization (PSO) and a dual-layer deep neural network (DNN). In this framework, PSO is responsible for optimizing the three-dimensional (3-D) placement of the UAV relay by minimizing the total aggregate path loss, while the DNN tackles the transmit antenna selection (TAS) problem at the source to maximize the average secrecy rate. The effectiveness of the suggested approach is assessed by comparing diverse relay placement techniques and antenna selection methods. The simulation results demonstrate that the proposed framework consistently outperforms conventional machine learning (ML)-based approaches and random baselines. Interestingly, single-antenna selection achieves slightly better secrecy rates than dual-antenna configurations. Overall, the proposed method provides a scalable and computationally efficient solution for enhancing the security of UAV-enabled communications in scenarios involving untrusted relays.

In this paper, multiple relay selection (MRS) schemes for untrusted unmanned aerial vehicle (UAV)-enabled networks are proposed. In this context, various machine learning (ML) models are employed to improve secrecy performance by optimally selecting multiple aerial relays from the available ones, all of which are regarded as untrusted. Notably, these ML models can cope with the quickly changing and random positioning of the aerial relays, effectively decoupling the intricate coupling relationship between the secrecy rate, channel coefficients, and inter-node distances. The results indicate that the ML-based MRS schemes obtain sufficient accuracy and better decoupling than a respective exhaustive searching (ES) approach, while also maintaining a lower computational complexity.

This paper proposes a security-aware computation offloading framework tailored for mobile edge computing (MEC)-enabled Internet of Things (IoT) networks operating in environments with aerial eavesdroppers (AEs) and ground eavesdroppers (GEs). It is envisaged that multiple ground nodes (GNs) should perform computation tasks partly locally and partly remotely by offloading a portion of these tasks to MEC servers. To facilitate this paradigm, an unmanned aerial vehicle (UAV) is deployed, serving as both an aerial MEC server and a relay for forwarding part of the tasks to a ground access point (AP) for computing. The computation offloading is further reinforced by incorporating a reconfigurable intelligent surface (RIS) unit in close proximity to the AP. Within this context, this paper provides an analysis of the secrecy outage probability (SOP) and formulates an optimization problem aimed at maximizing the minimum secure computation efficiency (SCE) by jointly optimizing transmit power allocation, time slot scheduling, task allocation, and RIS’s phase shifts. Given the non-convex nature of the problem, an iterative algorithm is introduced to address the fractional objective function and coupled optimization variables by employing Dinkelbach- and block coordinate descent (BCD)-based methods, respectively. The obtained results confirm the efficacy of the optimized scheme.

In this work, relay selection policies for a network where a terrestrial source communicates with a terrestrial destination through multiple relay nodes which reside in the stratosphere are presented. More specifically considering realistic three-dimensional (3-D) non-isotropic scattering channel modelling combined with opportunistic relay selection for various fading conditions. Moreover, the stratospheric nodes are battery-dependent and relay selection aims at power reduction. As Channel State Information (CSI) overhead is introduced by the selection process, proactive and reactive versions are presented based on instantaneous CSI knowledge. In this setting, the selection policies in terms of outage probability, throughput and power reduction are evaluated and comparisons with statistical channels and with a relaying scheme that does not perform power adaptation are provided.

This paper proposes a three-dimensional (3-D) three-concentric-cylinders model for multiple-input-multiple-output (MIMO) mobile-to-mobile (M-to-M) relay fading channels in amplify-and-forward (AF) communication networks. Based on this model, a parametric reference model for wideband MIMO M-to-M relay fading channels in urban macrocells is developed. From the reference model, the space-time-frequency correlation function among the links of the underlying MIMO communication channels is derived, under realistic 3-D non-isotropic scattering conditions. The numerical results depict that the correlation is significantly affected by the multipath elevation angles. These results also demonstrate that the time and frequency dispersion of a wide sense stationary uncorrelated scattering relay-based channel are not statistically independent. The proposed channel model provides an important framework and guidelines for the system design and performance analysis of multi-antenna M-to-M communication systems in relay-based wideband channels with non-line-of-sight double-bounce connections in the transmission links from the source to the destination via the relay.

This paper proposes a theoretical framework for the evaluation of the capacity of multiple-input multiple-output (MIMO) mobile-to-mobile (M-to-M) fading channels in dual-hop amplify-and-forward (AF) single-relay wireless communications networks. The evaluation is based on a recently introduced three-dimensional (3-D) geometry-based reference model for these channels. From this model, 3-D deterministic and statistical sum-of-sinusoids (SoS) based simulation models are also developed, under 3-D non-isotropic scattering conditions. The performance of the simulation models is verified through simulation results.

High-altitude platforms (HAPs) are considered as an alternative technology for next generation broadband wireless communications. This paper proposes three- dimensional (3-D) sum-of-sinusoids (SoS) simulation models for wideband HAP multiple-input-multiple- output (MIMO) channels. The starting point of the design procedure is a non-realizable reference model. The performance of the proposed models is studied in terms of the space-time-frequency correlation function (STFCF). The results show that the simulation models approximate the reference model with high precision.