Sep 2026· IEEE Internet of Things Journal· Vol 13, pp. 39971-39993· 0 citations· 42 references
Abstract
In resource-constrained low Earth orbit satellite-terrestrial cooperative Internet of Things (L-STCIoT), overloaded user demand poses severe challenges to underlying network performance optimization. To address the resource allocation problem in this scenario, a joint resource allocation framework is proposed, enabling the satellite to make coordinated decisions on user selection and transmission resource allocation based on user demands. In this framework, satellite resource cost and user-side satisfaction are defined as quantifiable metrics, respectively, and are jointly modeled to maximize the system’s weighted profit. Due to the nonconvexity of the original problem and strong variable coupling, it is decomposed into two subproblems: user selection and transmission resource allocation. Specifically, the former is addressed using a relaxation-based minorize–maximization (MM) algorithm combined with a discrete mapping (DM) mechanism, where a strongly concave surrogate function is constructed to improve decision efficiency and stability. The latter is reformulated as a linear programming (LP) problem via variable decoupling and solved using the interior-point method, thereby reducing computational complexity. In the performance analysis section, the convergence of the proposed joint optimization algorithm is proved based on the Lyapunov convergence framework, demonstrating that the performance loss introduced by the DM mechanism is bounded and that the final solution obtained by the algorithm is a globally approximate optimal solution to the original optimization problem. Simulation results demonstrate that the method proposed in this article effectively balances satellite resource cost and user-side satisfaction under different system scales and user demand distributions, while significantly improving the overall system profit, thereby exhibiting superior performance.
This formulation provides a rigorous and tractable framework for distributed spectrum sharing in 6G O-RAN systems, with the potential to support intelligent and adaptive control in future wireless networks.
E. Spyrou, Chrysostomos D. Stylios, V. Kappatos et al.· Future Internet· 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.
Gui-Fen Chen, Ruiyang Liu· Digital Signal and Computer...· 0 citations
This paper proposes a heterogeneous multi-agent proximal policy optimization (MAPPO)-based framework where both user devices and UAVs act as heterogeneous agents and utilizes a centralized training and decentralized execution (CTDE) paradigm to enable collaborative strategies between computing requesters and providers.
Ming Cheng, Canlin Zhu, Jiang-Hang Tang et al.· Journal of King Saud Univers...· 0 citations
Comparative analyses against ablation experiment frameworks and multiple access benchmark frameworks demonstrate that the proposed joint resource allocation distributed rate-splitting multiple access framework can improve the performance of low Earth orbit satellite communication systems while satisfying multiple constraint conditions.
Xianpeng Wang, Xi Han, Mingqi Gao et al.· IEEE Access· 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
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 is developed.
Chaofeng Wang, Longfei Zhang, Jie Luo et al.· Drones· 0 citations
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