Model Predictive Communication for Timely Status Updates in Low-Altitude Networks

2026-04-22

AI Summary

This work addresses the joint optimization of data timeliness, unmanned aerial vehicle (UAV) energy consumption, and ground interference in low-altitude networks by proposing a Model Predictive Communication (MPComm) framework—the first to integrate model predictive control into low-altitude communication scheduling. Leveraging channel predictability, MPComm formulates a bi-objective constrained optimization model that jointly accounts for information freshness, energy efficiency, and spectrum coexistence, and employs Pareto analysis to decouple the problem into two layers: communication timing and resource allocation. The inner layer combines non-convex analysis with mixed-integer optimization, achieving asymptotic optimality, while the outer layer is solved exactly via a graph-search algorithm. Experiments demonstrate that MPComm reduces ground channel occupancy by up to sixfold, lowers energy consumption by 6 dB, and significantly enhances timeliness and spectral efficiency compared to baseline schemes.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

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.

Problem

• timely status updates
• low-altitude networks
• data freshness
• energy consumption
• terrestrial interference

Innovation

• Model Predictive Communication
• Timely Status Updates
• Low-Altitude Networks
• Non-convex Optimization
• Pareto Optimization