Jul 2026· International Conference on Smart Communications and Networking· pp. 1-6· 0 citations· 11 references
Abstract
Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are two emerging technologies envisioned for sixth-generation (6G) wireless systems. These technologies enhance conventional cellular networks by extending coverage and enabling ubiquitous connectivity. However, spectrum scarcity and interoperability challenges create a strong need for efficient spectrum sharing among cellular users supported by these technologies. In this context, this paper considers a dynamic spectrum-sharing method in which data rate-aware spectrum sharing plays a critical role in managing base station power consumption and mitigating interference. We investigate a UAV-RIS-assisted cellular system where legacy cellular users share the spectrum with cellular Internet-of-Things (IoT) devices. To enhance the overall system sum data rate, we propose a joint user pairing, spectrum, power allocation, and RIS phase shift optimization approach based on matching theory. Simulation results demonstrate the effectiveness of the proposed method in improving resource allocation efficiency and significantly enhancing the sum data rate performance of wireless communication systems.
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
The proliferation of Internet of Things (IoT) devices in emerging 6G networks demands computing architectures that simultaneously deliver high throughput, low latency, and persistent coverage across heterogeneous deployment environments. Existing two-tier unmanned aerial vehicle–high-altitude platform (UAV–HAP) frameworks offer flexible edge processing but suffer from limited battery endurance, constrained computational capacity, and susceptibility to co-channel interference (CCI) when multiple aerial platforms share the same spectrum. This paper proposes a novel three-tier RIS-enhanced hierarchical aerial computing architecture that integrates a grid-powered reconfigurable intelligent surface–equipped base station (BS-RIS) alongside four RIS-equipped UAVs and a stratospheric HAP, so as to provide persistent, interference-managed 6G IoT coverage. The proposed architecture introduces a sub-array RIS partitioning mechanism in which each RIS panel, consisting of 256 elements divided into 4 sub-arrays, dedicates one sub-array per neighboring interfering platform, achieving 85 % inter-platform interference suppression (residual fraction \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\psi ^{\textrm{sup}}=0.15$$\end{document}). A comprehensive signal-to-interference-plus-noise ratio model is derived that captures both intra-platform CCI and inter-platform interference across all tiers. The resulting joint mixed-integer nonlinear programming problem is decomposed into three sequential stages: (i) a three-way hotspot-aware stable matching algorithm that associates IoT devices to platforms while penalising interference-heavy assignments; (ii) a sub-array-aware Riemannian conjugate gradient phase optimization that simultaneously enhances desired signal gains and suppresses inter-platform leakage; and (iii) a platform-aware hierarchical task distribution algorithm applying differentiated local-processing thresholds for battery-constrained UAVs (70 % delay margin) versus the grid-powered BS (100 % threshold). Extensive Monte Carlo simulations demonstrate that the proposed framework achieves approximately \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$30\,\%$$\end{document} higher total computed data volume, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$15\,\%$$\end{document} points higher task completion rate, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20\,\%$$\end{document} lower average end-to-end delay compared to the two-tier UAV–HAP.
Basma Diaa, Ibrahim I. Ibrahim, Ahmed M. Abd El-Haleem et al.· Scientific Reports· 0 citations
In temporary emergency communication coverage scenarios where terrestrial communication infrastructure is damaged or lacks sufficient capacity, UAVs equipped with base stations have emerged as an effective solution due to their flexible deployment and rapid response capability. However, in multi-UAV networks, the three-dimensional deployment of UAVs significantly affects air-to-ground link quality, while power allocation further determines the level of system interference and throughput performance. To address this issue, this paper considers a multi-UAV communication system and jointly takes into account user link reliability and service requirement satisfaction, thereby establishing a joint optimization model for QoS-constrained coverage and network throughput. To address the non-convex joint optimization problem, a problem-tailored dual-population cooperative NSGA-II framework, termed IDPC-NSGA-II, is developed. By coupling dual-population evolution, adaptive mutation, uncovered-user-guided local search, and interference-aware repair with the characteristics of multi-UAV emergency communications, the proposed method improves the trade-off between QoS-constrained coverage and network throughput. Simulation results in a representative emergency communication scenario show that the proposed method achieves a favorable trade-off between QoS-constrained coverage and throughput, and outperforms the compared algorithms under the considered network setting.
Guifen Chen, Ruiyang Liu· Digital Signal and Computer...· 0 citations
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is a key enabler for meeting the stringent low-latency and energy-efficiency requirements of emerging low-altitude economy applications. However, achieving these objectives remains challenging due to dynamic environments, limited communication and computation resources, and the heterogeneity of network entities. This paper investigates the long-term joint optimization framework that minimizes system-wide latency and energy consumption simultaneously by coordinating UAV association, subchannel selection, uplink/downlink power allocation, and computational resource distribution. This sequential decision-making process is formulated into a partially observable Markov decision process (POMDP) to account for localized observations and dynamic channel states. To solve it, we propose a heterogeneous multi-agent proximal policy optimization (MAPPO)-based framework where both user devices (UDs) and UAVs act as heterogeneous agents. This architecture utilizes a centralized training and decentralized execution (CTDE) paradigm to enable collaborative strategies between computing requesters and providers. Numerical results demonstrate that the proposed scheme effectively navigates the high-dimensional action space and achieves superior convergence and cost reduction compared to benchmarks, including PPO, independent PPO (iPPO), Q-learning multi-agent extension (QMIX), value decomposition networks (VDN), independent deep Q-network (iDQN), and genetic algorithm (GA).
Ming Cheng, Canlin Zhu, Jiang-Hang Tang et al.· Journal of King Saud Univers...· 0 citations
This study investigates the performance of a cooperative non‐orthogonal multiple access (NOMA)‐based hybrid cognitive radio (CR) network aided by rotating unmanned aerial vehicle (UAV). RF energy harvesting, Nakagami‐m fading between terrestrial users, and line‐of‐sight (LOS) linkages between UAV and different users have been taken into account for this proposed CR network. UAV rotates periodically over a circular region of interest (CROI) to locate ideal primary user (PU) bands, which is used by UAV to establish links between secondary users (SU) that experience severe fading on direct connections. All sources and the UAV make sensing judgments and transmit them to the fusion center (FC) in order to collaboratively determine the condition of a PU band using AND rule. According to the fusion center result, SU and UAV make adaptive transition between overlay and underlay protocols in order to improve spectrum efficiency (SE) of the system. The secondary throughput for NOMA‐based rotating UAV‐equipped CR networks under conditions of imperfect successive interference cancellation (i‐SIC) has been evaluated through closed‐form mathematical expressions. The proposed system throughput is examined for different network factors, like the radius of the UAV trajectory, height of UAV, and harvesting angle. Lastly, every analytical closed form equation has been validated using the MATLAB simulation testbed.
Jayanta Kumar Bag, Chanchal Kumar De, Abhijit Chandra· International Journal of Com...· 0 citations
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