Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-9· 0 citations
TL;DR
An Artificial Intelligence-Enabled UAV Communication Framework (AI-UCF) designed to optimize aerial communication performance is proposed and demonstrates substantial improvements in coverage probability, throughput, latency, and energy efficiency compared with traditional communication architectures.
Abstract
ABSTRACT
The rapid expansion of wireless communication services and the emergence of smart applications have created a growing demand for flexible, reliable, and high-capacity communication infrastructures. Conventional terrestrial communication networks often encounter challenges in providing continuous connectivity in disaster-stricken regions, remote locations, dense urban environments, and temporary large-scale events. Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as a promising solution for enhancing wireless communication networks due to their mobility, adaptability, and rapid deployment capabilities. UAV-assisted communication networks can function as aerial base stations, relays, data collectors, and mobile edge computing platforms, significantly improving network coverage, capacity, and service quality. This paper presents a comprehensive study of UAV-assisted communication systems and proposes an Artificial Intelligence-Enabled UAV Communication Framework (AI-UCF) designed to optimize aerial communication performance. The proposed framework integrates machine learning-based trajectory optimization, adaptive resource allocation, intelligent routing, and energy-efficient communication management. Simulation analysis demonstrates substantial improvements in coverage probability, throughput, latency, and energy efficiency compared with traditional communication architectures. The findings indicate that UAV-assisted communication networks will become a critical component of future 6G wireless infrastructures and intelligent networking ecosystems.
Keywords— UAV Communication Networks, Drone Networks, Aerial Base Stations, 6G Wireless Systems, Artificial Intelligence, Mobile Edge Computing, Wireless Coverage, Intelligent Networking.
The results confirm that the integration of artificial intelligence, energy-aware routing, and UAV trajectory optimization provides an effective and scalable solution for next-generation UAV-assisted IoT systems and establishes a robust foundation for intelligent 6G-enabled wireless sensor networks.
Mojtaba Nasehi· Internet of Things and Cloud...· 0 citations
Unmanned aerial vehicles (UAVs) have emerged as promising aerial platforms for next-generation wireless networks, offering three-dimensional mobility, rapid deployment, and high line-of-sight (LoS) link probability. This paper presents a structured overview of UAV-assisted wireless communications, covering key network architectures, air-to-ground channel characteristics, mobility-aware deployment and trajectory design, resource management, and multi-UAV cooperation. We further review recent integrations of UAVs with emerging technologies such as artificial intelligence (AI)-driven optimization, reconfigurable intelligent surface (RIS), integrated sensing and communication (ISAC), multiple-input multiple-output (MIMO), and semantic communication. Integration scenarios and recent research trends in beyond-5G and 6G networks are discussed, and open challenges along with future research directions are identified. This survey aims to provide a concise yet comprehensive reference for researchers and engineers working on UAV-assisted wireless network design.
Jueun Jeong, Sehyeon Kwon, Changhui Kim et al.· International Conference on...· 0 citations
Major disasters such as earthquakes, floods, and wildfires can rapidly destroy terrestrial communication infrastructure, producing an extreme operating environment in which power, road, and network outages compound one another. Owing to their rapid deployability, flexible networking, and three-dimensional mobility, unmanned aerial vehicle (UAV) swarms are being studied as a flexible component of emergency communication systems. This paper reviews UAV swarm ad-hoc network communication technology for emergency scenarios. It examines the technical characteristics and applicability boundaries of three network architectures---flat, hierarchical clustering, and space-air-ground integrated---and surveys recent advances in routing and medium access, intelligent networking optimization, and transmission and security assurance. Particular attention is given to the reported performance and applicability of emerging approaches, including reinforcement-learning-based adaptive routing, decentralized federated learning, digital twins, and semantic communication, under highly dynamic and resource-constrained conditions. Drawing on studies of emergency routing, post-disaster data collection, semantic forwarding, and multi-layer coverage, the paper assesses current validation methods and outlines research directions in energy use, scalability, security, resilience, and standardization. Its contribution is a cross-layer comparison that relates architecture choices to protocol requirements, implementation costs, and validation maturity.
Yihang Ren, Huatao Zhu, Jie Zhang· International Journal of Eme...· 0 citations
Sixth-generation (6G) wireless networks are expected to support massive Internet of Things (IoT) connectivity, ultra-reliable low latency services, high-throughput multimedia traffic, and intelligent and autonomous infrastructures. Conventional terrestrial deployments may be insufficient in rural areas, disaster recovery scenarios, emergency zones, and temporary high-density IoT events, where rapid coverage extension and adaptive resource management are required. Unmanned aerial vehicles (UAVs) can operate as aerial base stations to enhance service availability; however, limited onboard energy, altitude-dependent air-to ground channels, constrained bandwidth and transmit power, and heterogeneous quality-of-service (QoS) requirements make static resource allocation inefficient. This revised paper proposes an AI driven energy-efficient network slicing framework for UAV assisted 6G IoT communication. The network is divided into enhanced Mobile Broadband (eMBB), Ultra- Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC) slices. A DQN-based deep reinforcement learning (DRL) agent dynamically allocates the slice-level bandwidth, transmit power, and altitude-control actions after converting continuous decision variables into a finite feasible action set. The reward function jointly maximizes the throughput and energy efficiency while penalizing the latency, packet loss, and QoS violations. To address the reviewers’ concerns, the revised manuscript adds an LoS/NLoS air- to-ground channel model, a propulsion-aware UAV energy model, detailed DRL hyperparameters, a nine-action discretization table, Monte Carlo validation over 30 independent seeds, Welch significance testing, DRL variant comparison, computational complexity analysis, and three relevant references from Sana’a University Journalof Applied Sciences and Technology. The proposed method improves the throughput by 15.7%, reduces the latency by 19.8%, improves the energy efficiency by 16.4%, and reduces the packet loss by 24.6% compared with the greedy baseline. The results confirm that slice-aware DRL improves resource utilization and service reliability in UAV-assisted 6G IoT networks.
Unknown authors· مجلة جامعة صنعاء للعلوم التط...· 0 citations
ABSTRACT
The growing demand for universal internet access has highlighted the limitations of conventional terrestrial communication infrastructures, particularly in remote, rural, maritime, and disaster-affected regions. Satellite-Based Internet Communication Systems have emerged as a transformative solution capable of delivering broadband connectivity across vast geographical areas where traditional wired and wireless networks are either unavailable or economically infeasible. Recent advancements in Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Earth Orbit (GEO) satellite technologies have significantly improved communication speed, coverage, latency, and network reliability. Furthermore, the integration of Artificial Intelligence (AI), Software Defined Networking (SDN), and advanced signal processing techniques has enhanced satellite network performance and resource utilization. This paper presents a comprehensive study of Satellite-Based Internet Communication Systems and proposes an Artificial Intelligence-Enabled Satellite Communication Framework (AI-SCF) designed to optimize network efficiency, coverage, and service quality. The proposed framework integrates intelligent routing, adaptive bandwidth allocation, machine learning-based traffic prediction, and dynamic satellite resource management. Performance evaluation demonstrates significant improvements in throughput, latency reduction, coverage reliability, and network scalability compared with conventional satellite communication architectures. The findings indicate that satellite internet systems will serve as a fundamental pillar of future 6G communication ecosystems and global digital inclusion initiatives.
Keywords— Satellite Internet Communication, LEO Satellites, Broadband Connectivity, Artificial Intelligence, 6G Networks, Space Communication, Global Internet Access, Satellite Networking.
K. Venkatesh, Yadandla Anil, Thatla Venkatesh· International Scientific Jou...· 0 citations
Driven by the vision of a thriving low-altitude economy and aiming to provide on-demand services for diverse entities, this paper investigates an integrated sensing and communication (ISAC)-enabled low-altitude wireless network (LAWN). Benefiting from flexible mobility and cost-effective cooperative deployment, multiple ISAC-enabled uncrewed aerial vehicles (UAVs) are emerging as an ISAC paradigm for on-demand deployment in LAWN. However, due to the complex inter-UAV interference and resource coupling in LAWN, it is difficult to properly coordinate different constrained resources, including spatial deployment, energy, and wireless channels, to simultaneously meet the sensing and communication requirements. To address these challenges, this paper formulates a sensing–communication optimization (SCO) problem in LAWN by jointly optimizing subcarrier allocation, transmit power allocation, and three-dimensional (3D) UAV deployments to maximize network utility while satisfying quality of service (QoS) requirements for multiple users and target sensing mutual information (MI) requirements. To enable efficient solutions, we propose a hierarchical optimization approach that vertically decouples the SCO problem into two subproblems: a top level employing a Gibbs Sampling–based multi-UAV 3D deployment algorithm for efficient exploration and deployment optimization, and a bottom level performing resource allocation via a dual-based joint power and subcarrier allocation algorithm. Simulation results demonstrate that the proposed approach achieves a favorable trade-off between communication and sensing and significantly enhances the overall performance and adaptability of the LAWN.
Cheng Ma, Zewei Jing, Qinghai Yang et al.· IEEE Transactions on Wireles...· 0 citations
