In this work, we investigate the enhancement of secure quality-of-experience (QoE) in unmanned aerial vehicle (UAV)-assisted multiuser rate-splitting multiple access (RSMA) networks under stringent secrecy constraints. The primary objective is to maximize the aggregate mean opinion scores (MOSs) of all legitimate users while guaranteeing robust physical-layer security against eavesdroppers. To this end, the original secure optimization problem is decomposed into two interconnected subproblems: joint secure beamforming and rate allocation, and UAV trajectory optimization. For the secure beamforming and rate allocation, we employ advanced convexification techniques, including epigraph reformulation, polynomial properties, and norm-bounded channel uncertainty modeling, to ensure both optimal user experience and resilience to information leakage. The UAV trajectory optimization, inherently nonconvex, is further addressed by transforming and approximating the secrecy-related constraints to support secure communication throughout the UAV’s path. Simulation results validate the effectiveness and robustness of the proposed framework in simultaneously enhancing user-perceived QoE and ensuring secure transmission, even under varying eavesdropping threats and network conditions.

The integration of unmanned aerial vehicles (UAVs) as base stations is a recent advancement and a promising solution for assisting cellular networks and enhancing coverage. This paper presents an analytical framework for evaluating fractal coverage probability using stochastic geometry. Specifically, we incorporate the air-to-ground (A2G) model and a two-piece path loss model, which undergoes Fox's H channel fading, encompassing all existing fading distributions. First, we derive expressions for the association probabilities for each tier, considering both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. Then, we obtain a downlink coverage expression based on the signal-to-interference-plus-noise ratio (SINR), assuming millimeter-wave frequencies and directional beamforming. Numerical results highlight the advantages of the proposed analysis in a UAV-assisted terrestrial network.

5G and future networks must manage flows with varying Quality of Service (QoS) requirements, even under unpredictable traffic conditions. As user requirements for network capacity evolve over time, it is crucial to allocate resources appropriately to maximize the efficiency of their application. Consequently, these demands are driving the creation of new resource management policies, as conventional methods are no longer sufficient to meet them effectively. Thus, we propose a framework for resource allocation at the radio access network (RAN) level, while taking into consideration throughput and delay probability. To solve the formulated problem and to make it more tractable, the archimedes optimization algorithm (AOA) combined with an artificial neural network (ANN) is introduced to explore the search space and find optimal solutions. Nevertheless, in a 5G environment, the interactions between users, services, and network resources are inherently complex and non-linear. To this end, the radial basis function (RBF) is then used to predict user needs and reallocate resources according to expected results. The simulation results show that the proposed approach has a significant advantage over traditional approaches such as Particle Swarm Optimization (PSO). To the best of our knowledge, this paper is the first attempt to study 5G resource allocation using a combination of AOA and RBFNN algorithms and adequately describes the approach.

Stochastic geometry is emerging as tractable solution and attractive approach to evaluate the performance of 5G heterogeneous network (5G‐Hetnet) such as coverage, outage and rate probability. The base stations (BSs) and fading distribution in 5G‐Hetnet are an almost ubiquitous assumption for analytical study, requiring flexible and scalable approaches. However, the β‐Ginibre point process (β‐GPP) has been proposed as a new method to model wireless network. This study provides a general framework to analyse downlink mmWave cellular network with Nakagami‐ m fading and directional beamforming. First, cell association scheme in both line‐of‐site and non‐line‐of‐site is considered based on the strongest average received power. After deriving cell association probability between user equipment and BS, the computationally coverage probability expression is obtained depending on signal‐to‐interference‐plus‐noise ratio. Furthermore, the impact of biasing factor on rate coverage probability is investigated. Finally, considering inter‐cell‐interference coordination, most performances metrics will be re‐evaluated to determine their influence on the network. Recommended approaches produce satisfactory results when biasing factor and antenna beamforming are used.