The Erlang-U model is proposed, which extends classical traffic analysis by incorporating Markov chains and combining Erlang and Hyperexponential distributions to accurately model the heterogeneous and dynamic nature of UAV sojourn times, providing a more realistic estimation of blocking probabilities in cellular networks.
Abstract
The increasing frequency of social and emergency situations in modern cities has exposed the limitations of traditional cellular networks, which are often designed based on average traffic demands. These networks struggle to handle sudden demand peaks, leading to service blockages and degraded quality of service. To address this issue, the use of Unmanned Aerial Vehicles (UAV) as mobile base stations has been proposed as a temporary solution to expand network capacity during high-demand periods. However, existing traffic models, such as Erlang-B, fail to capture the dynamic entry, exit, and variability of dwelling times associated with UAVs, limiting their accuracy in real-world scenarios. To overcome these challenges, this work proposes the Erlang-U model, which extends classical traffic analysis by incorporating Markov chains and combining Erlang and Hyperexponential distributions to accurately model the heterogeneous and dynamic nature of UAV sojourn times. This novel approach enables both analytical and computational modeling of UAV mobility and dynamic availability, providing a more realistic estimation of blocking probabilities in cellular networks. Simulation results demonstrate that the adaptive deployment of UAVs, guided by the proposed model, can reduce blocking probability by over 25% compared to conventional solutions. These findings highlight the importance of selecting appropriate sojourn time models to optimize network resilience and efficiency in dynamic and high-demand environments.
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
The Enhanced RDAP (e-RDAP), a multi-criteria association policy that combines RSSI, Packet Delivery Ratio (PDR), and communication delay with an adaptive deployment strategy is introduced, indicating that QoS-aware multi-criteria association provides additional gains beyond load-aware association alone.
Lucas Baptista de Moraes, N. Fernandes, Fernanda G. O. Passos et al.· Annals of Telecommunications· 0 citations
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.
Ignacio Vidal, Sandy Bolufé, K. Toledo· Italian National Conference...· 0 citations
The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dynamic traffic conditions, infrastructure failures, and stringent quality of service (QoS) requirements. Due to the dynamic mobility and flexible deployment characteristics, UAV Open Radio Units (O-RUs) in the TADRA architecture support the terrestrial infrastructure under overload or failure conditions, dynamically extending coverage, balancing traffic loads, and restoring service to maintain uninterrupted QoS across diverse and heterogeneous traffic demands. Unlike existing static or single-layer solutions, our proposed TADRA integrates RAN Intelligent Controllers (RICs) with a Hierarchical Traffic-Aware Multi-Agent Twin-Delayed (TMT) algorithm to optimize the allocation of computation and radio resources. This joint optimization problem is NP-hard, highly dynamic, and coupled across agents, making TMT a tractable and adaptive alternative. This hierarchical framework performs traffic prioritization at the upper (application) layer and resource allocation at the lower (MAC) layer, facilitating adaptive decision-making under diverse vehicular traffic patterns. Numerical results demonstrate that our solution provides substantial gains over MATD3, MADDPG, and GA, achieving 17% lower latency, 10% higher throughput, 14% lower energy consumption, and 6.5% higher reliability.
Hayla Nahom Abishu, Ahmed Badawy, Amr Mohamed et al.· IEEE Transactions on Network...· 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
The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets.
Yasir Ullah, Mardeni Bin Roslee, Sufian Mousa Mitani et al.· Journal of King Saud Univers...· 28 citations
