Energy Minimization Oriented Resource Allocation for Integrated Sensing and Communication in Marine IoT Networks

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

Solar-Aware Trajectory Optimization for UAV-Aide IoT Network

This paper investigates a solar-aware trajectory optimization for unmanned aerial vehicles (UAVs) deployed in Internet of Things (IoT) networks for environmental monitoring. We develop a comprehensive energy consumption model that integrates aerodynamic flight dynamics with solar energy harvesting under realistic conditions. Building on this model, we formulate a trajectory optimization problem that considers UAV flight path, velocity, and energy dynamics to maximize reliable data collection and transmission throughput. The problem is inherently non-convex due to probabilistic constraints, energy harvesting non-linearities, and mobility restrictions. To overcome these challenges, we propose a successive convex approximation framework that reformulates the problem into tractable convex subproblems, which are solved iteratively with guaranteed convergence. Simulation results across diverse IoT deployment scenarios demonstrate that the proposed design substantially enhances UAV endurance, energy efficiency, and data throughput compared to benchmark approaches. The results show that solar-powered UAV communications are effective for large-scale IoT networks and contribute to more sustainable, reliable airborne sensing systems.

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.

Rate Optimization for Integrated Energy-Harvesting, Sensing, and Covert Communication Systems

The rapid development of uncrewed aerial vehicle (UAV) technology has brought security threats, which has motivated the development of integrated sensing and communication (ISAC) systems to address these issues. However, the power supply limitations of distributed ISAC nodes and the demand for covert signal transmission in adversarial scenarios remain unresolved. This paper focuses on covert transmission optimization for an integrated energy-harvesting, sensing and covert communication (IEHSCC) system, consisting of energy-harvesting (EH) nodes with switchable sensing/jamming modes and an ISAC base station (BS) to avoid detection by adversarial warden Willie. The minimum detection error probability (DEP) of Willie is derived, and the IEHSCC system’s sensing and covert transmission performance are calculated. Concerning the channel state information (CSI) estimation error, a joint optimization problem for EH node beamforming, BS beamforming, power allocation and EH node mode selection to maximize covert transmission rate is formulated. The problem is decomposed into subproblems and solved via S-Procedure and semi-definite relaxation (SDR) algorithm. An iterative water-filling algorithm is proposed to select EH modes. Simulation results show the proposed algorithm achieves fast convergence and effectively improves the covert transmission rate in the IEHSCC system. Increasing the number of antennas of EH node can increase covert rate, while excessive nodes or BS power degrades system performance, requiring rational configuration in practice.

Joint Beamforming and Trajectory Optimization Algorithm for RSMA-UAV-Enabled Integrated Sensing and Communication System

An unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and a joint beamforming and trajectory optimization framework is investigated and results demonstrate that the proposed algorithm significantly improves the achievable system downlink rate.

Resource Allocation for Secure Dual-UAV-Assisted ISAC System

Integrated sensing and communication (ISAC) is a rising technology in the next wireless communication networks, enabling the simultaneous execution of communication and sensing tasks by fully utilizing limited spectrum resources. In this work, we investigate the secrecy performance of a dual-uncrewed aerial vehicle (UAV)-assisted secure ISAC system. Specifically, a base station UAV communicates with users and transmits radar signals to locate potential eavesdroppers, while simultaneously providing information to a jammer UAV to perform jamming tasks. Considering constraints such as maximum UAV velocity, transmit power, propulsion energy, and sensing thresholds, we maximize the average secrecy rate by optimizing user scheduling strategies, time allocation, transmit power, and UAV trajectories. The presence of a non-convex problem, originating from tightly coupled variables, is tackled by an efficient iterative algorithm. In particular, the original optimization problem is decomposed into six subproblems, and non-convex subproblems are transformed into approximately convex forms via successive convex approximation. Then, block coordinate descent techniques are employed to solve all subproblems sequentially. Numerical results demonstrate the convergence and effectiveness of the proposed algorithm.