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，及时的信息传递对于无人机导航、巡检与监视等众多时间敏感型应用至关重要。核心挑战在于平衡三个相互制约的因素：严格的数据新鲜度要求、无人机机载能耗，以及与地面服务之间的干扰。应对这一挑战不仅需要高效的功率与信道分配策略，还需在整体运行周期内实现有效的通信时机规划。本文提出一种基于先进信道感知技术的模型预测通信框架，在该框架下，无人机将经历的信道条件具有高度可预测性。在此框架内，我们构建了一个带约束的双目标优化问题，以在满足严格时效性约束的前提下，实现能量消耗与地面信道占用之间的理想折中。通过帕累托分析求解该问题，并证明原始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，内层确定每个通信区间内的最优功率与信道分配。针对内层问题，我们利用非凸分析开发了一种具有渐近最优性保证的高效算法；而外层问题则通过基于内层解表征边的简单图搜索实现最优求解。所提方法适用于广泛的问题变体，包括目标函数变换及单目标特化情形。数值结果表明，与基准方案相比，所提方案能够实现高达六倍的地面信道占用减少和6分贝的能耗节省。