Multi-UAV Collaborative Energy Charging for Battery-Free SWIPT-Enabled Sensor Networks Based on MADDPG

Multi-UAV Energy Consumption Minimization for Multilayer Aerial Wireless-Powered MEC: An Online Stochastic Optimization Approach

Hierarchical Optimization of UAV Deployment and Resource Allocation for ISAC-Enabled Low-Altitude Wireless Networks

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.

Energy-Aware Self-Sustaining Solar-Powered UAV Swarm in Dense Urban 6G Networks With Laser WPT

This paper addresses the self-sustainable operation of unmanned aerial vehicle (UAV) swarms in dense urban 6G networks, where both communication reliability and energy replenishment are strongly affected by building-induced blockage. Although solar harvesting and laser wireless power transfer (WPT) can extend UAV operation, they are tightly coupled with UAV mobility: a communication-favorable position may not be feasible for laser charging, while a charging-oriented position may degrade ground node (GN) service. To capture this coupling, we develop a blockage-aware self-sustaining UAV swarm framework that integrates communication, solar harvesting, safety-compliant laser WPT, and UAV mobility under urban blockage. In the proposed model, buildings serve as common geometric constraints that determine both UAV–GN line-of-sight (LoS) connectivity and laser charging feasibility. We formulate a joint optimization problem of user association, UAV trajectory, laser charging decisions, and battery states to maximize the minimum spectral efficiency among GNs while ensuring sustainable energy operation. To address the resulting nonconvex mixed-integer nonlinear problem, we develop a tailored convexification framework for the coupled communication–charging–mobility design. Simulation results reveal that UAVs adapt their mobility according to solar availability: they prioritize short-range LoS communication when solar energy is sufficient, while moving toward safety-compliant and blockage-free laser charging regions under limited solar harvesting. These results highlight the need for joint mobility control that balances communication service and energy replenishment under building-induced blockage.

Energy-Aware Scheduling and Beamforming for Simultaneous Wireless Information and Power Transfer in Low-Earth-Orbit Satellite and UAV Networks Using Lyapunov Optimization, Successive Convex Approximation, and WMMSE

The integration of low-Earth-orbit (LEO) satellites with unmanned aerial vehicles (UAVs) promises high-throughput and flexible wireless connectivity, yet it faces critical challenges in simultaneously guaranteeing data rates and long-term energy harvesting under mobility and imperfect channel state information (CSI). Additionally, the rate–energy trade-off imposed by simultaneous wireless information and power transfer (SWIPT) further complicates per-slot resource allocation. In this paper, we propose a Lyapunov-based scheduling framework that stabilizes UAV data and virtual energy queues while maximizing weighted throughput. The framework employs a custom inner solver combining successive convex approximation (SCA) and weighted minimum mean-square error (WMMSE) optimization to efficiently compute per-slot beamformers and power-splitting ratios. Our approach explicitly accounts for UAV mobility, Rician fading channels with Doppler, and circuit nonlinearities in energy harvesting, ensuring feasible and energy-aware SWIPT operation. A LEO satellite–UAV integrated communication system is considered, where multiple satellites provide wireless connectivity to energy-constrained UAVs operating in a dynamic three-dimensional environment. The satellites employ multi-antenna transmission, while the UAVs rely on energy harvesting mechanisms to sustain their operation. The communication links are characterized by dominant line-of-sight propagation conditions, and UAV trajectories are adaptively optimized to improve network performance and energy efficiency. Simulation results demonstrate that the proposed Lyapunov-based SCA-WMMSE framework significantly outperforms a fixed baseline approach, providing substantial improvements in signal quality, achievable data rates, and harvested energy. Moreover, the proposed method maintains stable energy management behavior and guarantees long-term energy sustainability for the UAVs.

Joint 3D Trajectory and Power Optimization for UAV Swarms in Cell-Free Massive MIMO Networks: A CTDE-MAPPO Framework for Sensing-Aware Precision Agriculture

The integration of Unmanned Aerial Vehicle (UAV) swarms with Cell-Free massive Multiple Input Multiple Output (CF-mMIMO) networks offers promising prospects for large-scale crop monitoring in precision agriculture. CF-mMIMO provides macro-diversity and uniform channel quality across large agricultural fields. However, practical deployment demands jointly optimizing 3D trajectories and transmit power to maximize energy efficiency and field coverage simultaneously. This is challenging due to the limited battery capacity, mandatory return-to-depot constraints, and collision avoidance requirements. In this paper, we introduce a joint sensing–communication utility function that captures the trade-off between energy efficiency and field coverage completeness. To provide a scalable and distributed solution for rotary-wing UAV swarms, we develop a multi-agent deep reinforcement learning (MADRL) methodology based on the Multi-Agent Proximal Policy Optimization (MAPPO) approach. We adopt the Centralized Training with Decentralized Execution (CTDE) strategy, in which a CF-mMIMO central processing unit (CPU) serves as a global critic during training. At execution time, each UAV independently runs a lightweight local policy that adapts its trajectory and transmit power in real time based on battery state and air-to-ground channel variations. Simulation results reveal that the proposed MAPPO-CTDE approach outperforms existing benchmarks. Unlike prior methods that require instantaneous global CSI or neglect the sensing–communication coupling, the proposed approach simultaneously achieves high field coverage completeness, robust communication energy efficiency, and a high depot-return rate under hard battery constraints without any inter-UAV communication overhead at execution time.

AI-Driven Energy-Efficient Routing and UAV Trajectory Optimization for UAV-Assisted Internet of Things Sensor Networks in 6G Environments

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.