2026· IEEE Transactions on Network Science and Engineering· Vol 13, pp. 10515-10532· 0 citations· 95 references
Computer Science
Abstract
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.
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.
Ignacio Vidal, Sandy Bolufé, K. Toledo· Italian National Conference...· 0 citations
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
With the continuous improvement of communication requirements, it is difficult for traditional ground cellular networks to achieve seamless wide-area coverage, particularly in remote regions. To solve the insufficient coverage problem of sparse ground cellular network, we propose the Air-and-Ground Cooperative Network (AGCN) architecture to enhance the network coverage performance. Besides, and Cell Range Expansion (CRE) technology is combined to achieve load balancing between aerial Base Stations (BSs) and ground BSs. By setting user connection bias, communication users can access different BSs to coordinate the coverage performance of each layer in the AGCN. Based on stochastic geometry theory, we analyze the user access probability and distance distribution, and then derive the analytical formulas of network throughput and traversal rate. Furthermore, we analyze the influence of user connection bias on network performance in CRE technology. Simulation resulsts verify the correctness of the theoretial analysis and the efficiency of the AGCN in coverage enhancement. It indicates that there is an optimal bias setting to maximize the overall coverage performance of the AGCN, which provides valuable guidance for the future network design.
Vehicular crowdsensing (VCS) is a paradigm that exploits vehicle mobility, on-board sensing capabilities, and drivers' smartphone sensors to collect large-scale, distributed information to provide intelligent, location-based services. Satellite-assisted VCS architecture can complement terrestrial networks by enabling wide-area and infrastructure-independent data collection. Incentivizing vehicles to participate in satellite-assisted VCS campaign remains a major challenge due to associated sensing and communication costs, requiring each vehicle to optimize their sensing level to maximize their received reward. Moreover, unlike conventional assumptions where all vehicles participate simultaneously, practical VCS scenarios are asynchronous, as vehicle may start and complete sensing tasks at different times. To capture this realistic setting, we propose an asynchronous multi-agent proximal policy optimization (A-MAPPO) algorithm within a centralized training and decentralized execution (CTDE) framework to optimize the sensing strategies of individual vehicles in a satellite-assisted VCS setting. A dynamic social network effect among vehicles is also incorporated to encourage vehicle participation driven by social benefits. Extensive numerical experiments are conducted to evaluate the performance of the proposed approach, demonstrating that A-MAPPO achieves superior performance compared with MASAC, DQN, Greedy-Q, and Random baselines.
Arbil Chakma, Jingrong Wang, Quang Nhat Le et al.· IEEE Transactions on Network...· 1 citation
—Unmanned aerial vehicle (UAV) swarm-assisted integrated sensing and communication (ISAC) networks are a crucial technology for providing communication and sensing services in emergency rescue scenarios without base station support. However, the strong coupling between communication and sensing resources in such networks fundamentally limits the communication and sensing performance of ISAC systems. This paper jointly optimizes spectrum allocation, UAV association and deployment to maximize average system throughput while ensuring localization accuracy in such networks, where sensing is realized through localization. We begin by deriving an analytical expression for localization accuracy, which explicitly captures the joint effects of link quality and anchor geometry under shared communication-localization spectrum resources. We then formulate average system throughput maximization as a mixed-integer nonlinear and non-convex optimization problem with the constraints of localization accuracy, sub-channels, UAV association, UAV deployment and signal-to-interference-plus-noise ratio. We further develop an alternating iterative optimization method to solve this complex optimization problem. Within this method, a particle swarm optimization-based method is developed to jointly optimize spectrum allocation and UAV association, and a dueling double deep Q-network-based method is further employed for UAV deployment optimization. Finally, extensive simulation results are presented to validate the efficiency of our optimization method, and also to illustrate how key parameters influence average system throughput and localization accuracy.
Zhuo-Jia Yang, Wei Su, Bin Yang et al.· IEEE Transactions on Mobile...· 0 citations
This paper provides a theoretical analysis of the effects of autonomous vehicles (AVs) on the spatial structures of future cities. We consider two types of AVs, private AVs (PAVs) and shared AVs (SAVs). We assume that AVs have a lower marginal travel time cost than human‐driven traditional vehicles (TVs) due to additional utility caused by free activities in AVs, and PAVs have a lower marginal travel time cost than SAVs due to better privacy, convenience, and comfort. Two urban spatial models are presented and compared: one focusing on a city with only TVs and the other on a city with mixed PAVs and SAVs. Both models account for land competition among firm production, household residence, and parking. The optimal residential lot size and the optimal SAV market share are determined, with an objective of maximizing social welfare. The findings show that introducing AVs may lead the city size to expand or shrink, and both the social welfare and the total congestion cost to increase or decrease, depending on the maturity degree of AV technology and the SAV market share.
Zhi-Chun Li, Wen-jing Liu, A. de Palma et al.· Journal of Regional Science· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
