With the rapid development of space–air–ground integrated networks (SAGIN), the synergy between the satellite and unmanned aerial vehicles (UAVs) in sensing environmental status information reveals substantial potential. In SAGIN, applications such as disaster response and military operations require fresh status information to respond effectively. The freshness of information, quantified by the age of information (AoI) metric, is crucial for an effective response. Therefore, it is urgent to investigate the AoI in real-time remote sensing systems leveraging satellite–UAV synergy. To this end, we first establish a comprehensive system model, corresponding to the satellite–UAV “multiscale explanation” synergy remote sensing system in SAGIN, in which we focus on the typical information transmission and fusion strategies of the system, the analysis framework of AoI, and the temporal evolution of AoI. Subsequently, the time-varying process of the system model is transformed into a corresponding finite-states continuous-time Markov chain, enabling a precise analysis of its stochastic behavior. By employing the stochastic hybrid system (SHS) approach, the moment generating functions (MGFs) and mean AoI, offering quantitative insights into the freshness of status information, are derived. Following this, a comparative analysis of AoI under different queuing disciplines, highlighting their respective performance characteristics, is conducted. Furthermore, considering transmit power and bandwidth constraints of the system, the AoI performances under full frequency reuse (FFR), and frequency division multiple access (FDMA) strategies are analyzed. The energy advantage and spectrum advantage associated with AoI are also examined to explore the superior AoI-related performance of the FFR strategy in SAGIN.

Integrated Sensing and Communication (ISAC) has been enhanced to serve as a pivotal enabler for next-generation communication systems. In the context of target area detection, a UAV-SAR (Unmanned Aerial Vehicle–Synthetic Aperture Radar) based ISAC system, which shares both physical infrastructure and spectrum, can enhance the utilization of spectrum and hardware resources. However, existing studies on UAV-SAR-based ISAC systems for target imaging remain limited. In this study, we first established an ISAC mechanism to enable SAR imaging and communication. Then, we analyzed the energy consumption model, which includes both UAV propulsion and ISAC energy consumption. To maximize system energy efficiency, we propose an optimization method based on sequential convex optimization with linear state-space approximation. Furthermore, we propose a plan with general constraints, including the initial and final positions, the signal-to-noise ratio (SNR) constraint for SAR imaging, the data transmission rate constraint, and the total power limitation of the UAV. To achieve maximum energy efficiency, we jointly optimized the UAV’s trajectory, velocity, communication beamforming, sensing beamforming, and power allocation. Numerical results demonstrate that compared to existing benchmarks and PSO algorithms, the proposed method significantly improves the energy efficiency of UAV-SAR-based ISAC systems through optimized trajectory design.

Mobile edge computing (MEC) has become an effective framework for latency-sensitive and computation-intensive applications by deploying computing resources at network edge. The unmanned aerial vehicle (UAV)-assisted MEC leverages UAV mobility and communication advantages to enable services in dynamic environments, where frequent adjustments to flight trajectories and user association are required due to dynamic factors such as time-varying task requirements, user mobility, and communication environment variation. This paper addresses the joint optimization problem of UAV flight trajectory control and user association in dynamic environments, which explicitly incorporates the constraints governed by UAV flight dynamics. The joint problem is formulated as a non-convex optimization formulation that involves continuous–discrete hybrid decision variables. To overcome the inherent complexity of this problem, a novel proximal policy optimization-based dynamic control (PPO-DC) algorithm is developed. This algorithm aims to reduce the weighted combination of delay and energy consumption by dynamically controlling the UAV trajectory and user association. The numerical results validate that the PPO-DC algorithm successfully enables real-time UAV trajectory control under flight dynamics constraints, ensuring feasible and efficient flight trajectory. Compared to the state-of-the-art hybrid-action deep reinforcement learning (DRL) algorithms or metaheuristics, the PPO-DC achieves notable improvements in system performance by simultaneously lowering system delay and energy consumption.