Timely information delivery in low-altitude networks is critical for many time-sensitive applications, such as unmanned aerial vehicle (UAV) navigation, inspection, and surveillance. The key challenge lies in balancing three competing factors: stringent data freshness requirements, UAV onboard energy consumption, and interference with terrestrial services. Addressing this challenge requires not only efficient power and channel allocation strategies but also effective communication timing over the entire operation horizon. In this work, we propose a model predictive communication (MPComm) framework, enabled by advanced channel sensing techniques, in which the channel conditions that the UAV will experience are largely predictable. Within this framework, we formulate a constrained bi-objective optimization problem to achieve a desired trade-off between energy consumption and terrestrial channel occupation, subject to a strict timeliness constraint. We solve this problem using Pareto analysis and show that the original non-convex, mixed-integer problem can be decomposed into a two-layer structure: the outer layer determines the optimal communication timing, while the inner layer determines the optimal power and channel allocation for each communication interval. An efficient algorithm for the inner problem is developed using non-convex analysis, with asymptotic optimality guarantees, while the outer problem is solved optimally via a simple graph search, with edges characterized by inner solutions. The proposed approach applies to a broad class of problem variants, including objective transformations and single-objective specializations. Numerical results demonstrate the efficiency of the proposed solution, achieving up to a six-fold reduction in terrestrial channel occupation and a 6dB energy saving compared to benchmark schemes.

翻译：在低空网络中，及时的信息交付对于许多时间敏感型应用（例如无人机导航、巡检和监视）至关重要。关键挑战在于平衡三个相互竞争的因素：严格的数据新鲜度要求、无人机机载能耗以及与地面业务的干扰。应对这一挑战不仅需要高效的功率和信道分配策略，还需要在整个运行周期内进行有效的通信时机规划。在本工作中，我们提出了一种基于模型预测通信（MPComm）的框架，该框架借助先进的信道感知技术，使无人机将经历的信道条件在很大程度上可预测。在此框架内，我们构建了一个带约束的双目标优化问题，以在严格的时间约束下实现能耗与地面信道占用之间的期望权衡。我们使用帕累托分析求解该问题，并表明原始的非凸混合整数问题可分解为两层结构：外层确定最优通信时机，内层确定每个通信间隔的最优功率和信道分配。针对内层问题，我们利用非凸分析开发了一种高效算法，并保证了渐近最优性；而外层问题则通过一种简单的图搜索获得最优解，其边由内层解表征。所提出的方法适用于广泛的问题变体，包括目标变换和单目标特例。数值结果证明了所提方案的效率，与基准方案相比，实现了高达六倍的地面信道占用减少以及6dB的能耗节省。