Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 202-207· 0 citations· 48 references
Abstract
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.
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
Uncrewed aerial vehicles (UAVs) integrated with integrated sensing and communication (ISAC) technology have emerged as a compelling platform for sixth-generation (6G) wireless networks, leveraging three-dimensional mobility to perform simultaneous sensing and communication (S&C) across applications ranging from disaster response to airspace monitoring. While the field has advanced rapidly, existing surveys have not sufficiently characterized UAV-specific design challenges nor the cross-cutting trade-offs governing practical deployment. To bridge this gap, this survey provides a systematic review across six interconnected domains—channel estimation (CE) and beam tracking, throughput maximization, weighted sum rate (WSR) and sensing co-optimization, delay and age of information (AoI) minimization, energy efficiency (EE), and PLS—each supported by a structured comparative table covering over 80 methodologies. The survey concludes with a research roadmap addressing propagation modeling, platform dynamics, imperfect channel state information (CSI) robustness, energy-AoI-security co-design, and standardization, providing a structured foundation for future 6G UAV-ISAC research.
Manzoor Ahmed, Syed Tariq Shah, A. A. Nasir et al.· IEEE Open Journal of the Com...· 1 citation
Reliable communication for unmanned aerial vehicles (UAVs) in beyond-5G and 6G cellular networks is challenged by strong line-of-sight interference, limited spatial separability, and inefficient association when UAVs operate along structured aerial highways (AHs). This paper presents a joint UAV association and beam management framework for heterogeneous terrestrial–aerial networks assisted by high-altitude platform stations (HAPS). The approach exploits the spatial structure of AHs through particle swarm optimization (PSO)-based segmentation, enabling consistent segment-level UAV association across network tiers. A tier-aware association metric is combined with an enhanced teaching–learning-based optimization (ETLBO) scheme to jointly optimize synchronization signal block (SSB) beam activation and power allocation. This coordinated design mitigates inter-sector interference and improves beam alignment for aerial users while preserving the performance of ground users. Simulation results based on 3GPP-compliant models demonstrate notable gains in UAV SINR and achievable data rate, particularly at the lower percentiles, with limited impact on terrestrial users. The framework further exhibits robust performance across different AH geometries, confirming its effectiveness in interference-and coverage-limited aerial communication scenarios.
Maadoud Djihane, Hamza Abdelkrim, Houari Keltoum et al.· 2026 International Conferenc...· 0 citations
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.
Dr T Anvesh, Vengala Vishnuvardhan, Tumma Raghavendra· International Scientific Jou...· 0 citations
The rapid expansion of the Internet of Things (IoT) and the emergence of sixth-generation (6G) wireless networks have created unprecedented opportunities for large-scale intelligent sensing, real-time data collection, and ubiquitous connectivity. However, the deployment of massive IoT sensor networks faces significant challenges, including limited energy resources, dynamic network topologies, communication reliability issues, routing inefficiencies, and coverage constraints, particularly in remote, disaster-stricken, and infrastructure-deficient environments where conventional terrestrial communication systems often fail to provide reliable services. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for enhancing network coverage, improving data collection efficiency, and supporting communication services in IoT ecosystems; nevertheless, their integration introduces additional challenges related to energy consumption, trajectory planning, routing optimization, and resource allocation. To address these issues, this paper proposes an AI-Driven Energy-Efficient Routing and UAV Trajectory Optimization Framework for UAV-assisted IoT sensor networks operating in 6G environments. The proposed framework integrates intelligent routing, adaptive energy management, and dynamic UAV trajectory optimization within a unified cross-layer architecture and develops a comprehensive mathematical model to characterize the relationships among energy consumption, communication delay, packet delivery performance, routing decisions, and UAV mobility. Furthermore, a Deep Reinforcement Learning (DRL)-based optimization algorithm is introduced to enable autonomous decision-making and adaptive network control under dynamic environmental conditions. The proposed approach continuously monitors key network parameters, including residual sensor energy, link quality, transmission distance, traffic load, UAV battery status, and data collection requirements, and dynamically determines optimal routing paths and UAV flight trajectories to minimize overall energy consumption while maximizing network lifetime, packet delivery ratio, and data collection efficiency. In addition, the framework leverages the ultra-reliable low-latency communication capabilities envisioned for future 6G infrastructures to facilitate intelligent coordination between UAV platforms and IoT sensor nodes. Performance evaluation under various network densities, mobility scenarios, and communication conditions demonstrates that the proposed framework significantly reduces energy consumption, improves routing efficiency, extends network lifetime and enhances packet delivery performance, and decreases communication overhead and data collection latency compared with conventional approaches. 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
Integrated Sensing, Communication, and Power Transfer (ISCPT) is a key enabler for sixth-generation networks, enhancing resource utilization to support massive low-power devices. However, existing research has predominantly focused on a single Uncrewed Aerial Vehicle (UAV) in communication and power transfer, lacking the capability to facilitate joint sensing and power transfer of multi-UAVs for moving targets considering estimation errors. To tackle the above challenge, we propose for the first time a multi-UAV-assisted ISCPT algorithm serving both multiple Communication Users (CUs) and Energy Receivers (ERs), featuring a sensing-assisted Wireless Power Transfer (WPT) framework. Specifically, we first derive the Fisher information matrix and Cramér–Rao bound for multi-ER positioning under location uncertainty. Then, we formulate a two-phase optimization problem where sensing directly refines location estimation to achieve robust WPT efficiency maximization. To solve the formulated NP-hard problems, we design a two-phase algorithm by jointly optimizing association scheduling with CUs and ERs, beamforming and trajectory design for UAVs, based on generalized Petersen’s sign-definiteness lemma, Lagrangian relaxation and S-procedure. Numerical results validate that the proposed algorithm achieves a maximum WPT efficiency improvement of 42.3% compared with several representative baseline schemes, demonstrating strong practicality for multi-UAV-assisted ISCPT networks.
Zhaolong Ning, Lingfei Li, Xiaojie Wang et al.· IEEE Journal on Selected Are...· 1 citation