Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 38 references
Medicine
TL;DR
A realistic UAV-assisted vehicular networking framework is developed that integrates microscopic traffic simulation through Simulation of Urban MObility (SUMO), network control via Traffic Control Interface (TraCI), and standard-compliant 5G communication modeling using MATLAB R2025b 5G Toolbox and proposes a low-complexity trajectory optimization strategy.
Abstract
This paper investigates the use of unmanned aerial vehicles (UAVs) as flying base stations (BSs) to enhance fifth generation (5G) vehicular communications on highways, where traffic congestion and fluctuating user demand can challenge the capacity of terrestrial infrastructure. While UAV-assisted vehicular networking has attracted significant attention, many existing studies rely on simplified mobility, propagation, or communication models that limit the assessment of practical deployment performance. To address these limitations, we develop a realistic UAV-assisted vehicular networking framework that integrates microscopic traffic simulation through Simulation of Urban MObility (SUMO), network control via Traffic Control Interface (TraCI), and standard-compliant 5G communication modeling using MATLAB R2025b 5G Toolbox. The framework incorporates a 3rd Generation Partnership Project (3GPP) rural macro cell (RMa) highway scenario, detailed clustered delay line (CDL)-based channel characterization, and cross-layer communication procedures. Within this framework, we propose a low-complexity trajectory optimization strategy that adapts the UAV position in real time to maximize the average received signal to noise ratio (SNR) while respecting practical motion constraints. Simulation results demonstrate that adaptive UAV positioning enhances communication performance, achieving mean SNR gains of up to 2.04 dB, throughput improvement of up to 11.2%, and block error rate (BLER) reductions of up to 27.3%. These findings highlight the potential of UAV-assisted communications to enhance user-perceived quality of service (QoS) for bandwidth-demanding vehicular applications under realistic 5G highway operating conditions.
Simulation results demonstrate that the proposed adaptive scheme demonstrates notable improvements over classical loss-based and delay-based baselines in reducing queuing delays at UAV relay nodes, enhances the transmission efficiency of multi-hop terminals, and effectively maintains end-to-end goodput stability in high-latency environments.
L. Zong, Yun Cheng, Yi Yao· Italian National Conference...· 0 citations
Autonomous aerial vehicles (AAVs) networks, combining AAVs with mobile communication technology, can promote the rational utilization of airspace resources and produce enormous economic value. Due to the complex effects of network deployment areas (NDAs), AAV mobility, and channel fading characteristics, the received signal strength at the AAV exhibits randomness and is susceptible to eavesdropping. However, existing research commonly ignores AAVs’ mobility and only considers the communications and movements within regularly-shaped NDAs. To solve these limitations, we propose a distance distribution-based modeling and analysis framework considering both node randomness and mobility under arbitrarily-shaped convex NDAs. More concretely, this paper focuses on a AAV network for a low-altitude data collection scenario, in which the mobile AAVs serve as an aerial base station to collect the information from the ground randomly distributed Internet of Things (IoT) devices. To involve both the randomness of IoT devices and mobility of AAVs, we propose a method combining random waypoint mobility model and kinematic measure method to derive the distributions of two types of distances for arbitrarily-shaped convex NDAs, including the distance between a random IoT device and a mobile AAV (referred to as R2M) and that between two mobile AAVs (referred to as M2M). Based on the obtained R2M and M2M distance distributions, the communication, coverage, and security performance are derived and analyzed for single-AAV, multi-AAV, and eavesdropping scenarios. The accuracy and effectiveness of the proposed framework are evaluated by extensive numerical studies.
Fei Tong, Yujiao Li, Ziyan Zhu et al.· IEEE Transactions on Network...· 0 citations
This study investigates the optimization of three-dimensional (3D) trajectory planning and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless networks with no-fly zones (NFZs) using a deep learning framework. The objective is to maximize the minimum average spectral efficiency (SE) among mobile users served by multiple UAVs while addressing key challenges, including interference from concurrent UAV transmissions, collision avoidance, and NFZ constraints. A realistic probabilistic channel model is considered, where the likelihood of a line-of-sight (LoS) condition is modeled as a function of the elevation angle in the air-to-ground (A2G) link. To solve the formulated optimization problem, a novel deep learning framework with specialized deep neural network (DNN) structures is developed. This framework jointly optimizes 3D UAV trajectory planning and resource allocation, employing an unsupervised learning-based training approach that eliminates the need for labeled data. Performance evaluations demonstrate that the proposed scheme effectively accounts for the probabilistic channel model and co-channel interference while accounting for collision avoidance and NFZ-related constraints. Moreover, it outperforms baseline methods by achieving a higher minimum average SE with real-time computational efficiency, making it practical for UAV-assisted wireless networks.
Due to the sparse node distribution and the harsh propagation environment in Maritime Internet of Things (MIoT), traditional local mobile self-organizing networks relying on direct Device-to-device (D2D) communications face limited coverage and frequent link outages. To address these issues, this letter investigates the unmanned aerial vehicle (UAV)-assisted MIoT, where UAVs serve as aerial base stations to provide enhanced coverage. Using stochastic geometry, we develop a system model that consists of the D2D tier and the UAV tier, respectively employing the Fluctuating Two-Ray (FTR) model and Nakagami- $m$ model. Then, analytical expressions of coverage probability and achievable rate, along with their tight upper and lower bounds, are derived. Simulation results validate the theoretical analysis, confirming both the coverage improvement from UAV deployment and the effectiveness of the FTR model. It is further shown that by optimizing the UAV deployment with appropriate density, altitude, and antenna array size, the inter-layer interference can be effectively mitigated thus improving the coverage probability and achievable rate.
Xinyu Du, Xian Zhang, Jiu Xie et al.· IEEE Wireless Communications...· 0 citations
Integrated satellite–aerial networks (ISANs) are emerging as a promising architecture that combines high-throughput inter-satellite transmission with the agility of uncrewed aerial vehicles (UAVs) to support flexible and low-latency traffic delivery. Owing to the inherently uneven traffic distribution in the satellite layer, traffic flows often suffer from congestion and excessive multi-hop forwarding delays. UAVs can act as adaptive relays to offload congested traffic and mitigate routing detours, thereby reducing end-to-end latency. However, latency-aware traffic management in ISANs is fundamentally challenged by highly dynamic satellite topologies, heterogeneous link characteristics, and the tight coupling between satellite traffic dynamics and UAV mobility. Existing approaches often suffer from cross-layer misalignment between satellite routing and aerial relaying, which limits coordinated latency adaptation. To address these challenges, this paper proposes an agentic UAV-assisted relay framework, termed DUS-SACUD, in which an autonomous UAV acts as an embodied agent that proactively steers traffic. First, a graph-conditioned diffusion model is developed for generative UAV–satellite link (USL) selection under dynamic network states. Second, a soft actor–critic-based reinforcement learning scheme is employed for embodied UAV deployment to minimize USL-induced delay. Through closed-loop alternating execution, DUS-SACUD jointly optimizes connectivity adaptation and mobility control in ISANs. Extensive simulations based on a realistic satellite constellation demonstrate significant end-to-end latency reduction over existing routing and UAV-assisted baselines, while maintaining robust performance under diverse ISAN conditions.
Xintong Li, Feng Wang, Qi Wu et al.· IEEE Transactions on Cogniti...· 0 citations
This paper evaluates the downlink performance of 5G non-terrestrial networks (NTNs) realized via tethered unmanned aerial vehicle (TUAV)-mounted base stations, and compares it against conventional 5G terrestrial networks (TNs) in a realistic dense urban scenario. Unlike battery-limited UAVs, TUAVs are connected to ground stations via lightweight cables, enabling stable positioning and near-line-of-sight links to users without the endurance constraints of untethered platforms. Using a 3GPP-compliant multi-cell hexagonal layout with 19 sites and 57 sectors, we model TUAV altitudes ranging from 100 to 1000\,m and evaluate the average effective signal-to-interference-plus-noise ratio (SINR) and user throughput via system-level simulations. Results show that TUAV-based NTN deployments can significantly outperform terrestrial 5G in per-user throughput, with the largest gains observed for cell-edge and low-SINR users, provided the TUAV altitude is properly chosen to balance improved line-of-sight probability against increased propagation loss and interference at higher altitudes.
G. Svistunov, Azim Akhtarshenas· 0 citations
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