Jul 2026· Digital Signal and Computer Communications· Vol 14294, pp. 142940O - 142940O-6· 0 citations· 7 references
Engineering
Abstract
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.
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
When natural disasters strike, the destruction of terrestrial communication infrastructure creates urgent demands for emergency networks. Efficient UAV deployment in capsule airport–UAV hierarchical networks has emerged as a critical challenge due to limited aerial resources and stringent quality-of-service requirements. This paper develops a QoS-aware joint optimization model for UAV deployment, integrating air-to-ground (A2G) channel modeling with resource allocation, where upper-level position optimization is coordinated with lower-level frequency allocation and power control through a hierarchical decomposition strategy. The proposed QoS-TLK-VNS-K algorithm combines graph coloring for interference mitigation with iterative power control for SINR guarantee. Empirical evaluation using multi-scenario simulations demonstrates that the proposed approach significantly outperforms the traditional distance-based coverage method. Statistical validation over 30 independent runs demonstrates significant improvements in QoS satisfaction (+23.8%, p<0.001), average SINR (+104.0%, p<0.001), minimum user rate (+194.9%, p<0.001), and Jain’s fairness index (+16.2%, p<0.001) compared to the distance-based baseline. These results demonstrate that the framework effectively addresses the trade-off between interference suppression and network connectivity in multi-UAV emergency communication systems.
Chaofeng Wang, Longfei Zhang, Jie Luo et al.· Drones· 0 citations
This study investigates the optimization of three-dimensional (3D) trajectory planning and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless networks with no-fly zones (NFZs) using a deep learning framework. The objective is to maximize the minimum average spectral efficiency (SE) among mobile users served by multiple UAVs while addressing key challenges, including interference from concurrent UAV transmissions, collision avoidance, and NFZ constraints. A realistic probabilistic channel model is considered, where the likelihood of a line-of-sight (LoS) condition is modeled as a function of the elevation angle in the air-to-ground (A2G) link. To solve the formulated optimization problem, a novel deep learning framework with specialized deep neural network (DNN) structures is developed. This framework jointly optimizes 3D UAV trajectory planning and resource allocation, employing an unsupervised learning-based training approach that eliminates the need for labeled data. Performance evaluations demonstrate that the proposed scheme effectively accounts for the probabilistic channel model and co-channel interference while accounting for collision avoidance and NFZ-related constraints. Moreover, it outperforms baseline methods by achieving a higher minimum average SE with real-time computational efficiency, making it practical for UAV-assisted wireless networks.
This paper investigates the coordinated deployment and routing of communication equipment by a Sub-mother UAV swarm in power-grid emergency communication scenarios. Considering mission timeliness and payload constraints, a heterogeneous MUAV–SUAV coordinated deployment-and-routing model is established to minimize the total system cost, including platform flight cost, SUAV activation cost, and penalty cost caused by delayed deployment. To solve this problem, a two-stage optimization framework is proposed. In the first stage, an improved K-means clustering algorithm with neighborhood search (K-means-NS) is developed to divide deployment points into feasible sub-regions while satisfying SUAV endurance constraints and maintaining the deployment–retrieval payload balance required by the MUAV. In the second stage, the MUAV inter-region visiting sequence is treated as a routing subproblem, and an improved adaptive genetic algorithm (IAGA) is designed to optimize the coordinated routes of the MUAV and SUAVs within each sub-region. The IAGA adopts hybrid encoding, feasible-solution adjustment, elitist selection, and adaptive crossover–mutation operations to improve search efficiency under complex constraints. Numerical experiments on small-, medium-, and large-scale scenarios show that the proposed method can generate feasible sub-region divisions and coordinated routing schemes. Compared with GA and G-PSHA, IAGA reduces the total flight cost by approximately 21.2%, 10.5%, and 23.2% relative to GA and by approximately 0.2%, 2.5%, and 8.1% relative to G-PSHA in the three scenarios, respectively. Sensitivity analysis further indicates that stricter mission-timeliness requirements increase penalty costs, highlighting the importance of timely communication-device deployment in emergency restoration.
Youfan Gu, Yu Song, Minkun He et al.· Applied Sciences· 0 citations
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
Simulation results validate the effectiveness of the proposed OP approximation, demonstrate the significant outage capacity improvement of the proposed robust optimization schemes over benchmark schemes, and illustrate the superiority of the IWS-based scheme over the CE-based scheme.
Yifan Jiang, Qing-Bin Wu, Hongxun Hui et al.· 0 citations