Agentic AI-Enabled Solar-Powered High-Altitude Platforms for Sustainable SAGINs

2026-08-15

AI Summary

This study addresses the challenge of tightly coupled communication, computing, and energy resources in solar-powered High-Altitude Platforms (HAPs) within integrated space-air-ground networks. We propose a pioneering HAP-native intelligent agent AI framework that employs multi-timescale decoupling design and closed-loop feedback mechanisms to enable autonomous collaborative decision-making across these domains. Validated in disaster response scenarios, the proposed method significantly enhances system energy efficiency and task completion rates while effectively reducing latency. Comprehensive evaluations demonstrate that our approach outperforms existing baselines across all key metrics, providing an innovative solution for the sustainable operation of integrated space-air-ground networks.

Abstract

Space-Air-Ground Integrated Networks (SAGINs) can extend connectivity, but their communication, computing, and platform operations create tightly coupled energy demands. Solar-powered High-Altitude Platforms (HAPs) offer a promising middle layer by combining persistent regional coverage, renewable-energy harvesting, and onboard computing. However, realizing this potential requires more than optimizing individual links or processors, as radio transmission, task execution, backhaul use, and battery preservation share a common energy budget. Therefore, we introduce a HAP-native Agentic AI framework. It continuously perceives communication, computing, energy, mobility, and mission states; invokes quantitative tools for prediction and verification; and coordinates executable actions through a closed control loop. Then, a multi-timescale design separates fast radio control from task orchestration and long-term energy planning. Furthermore, a disaster-recovery case study illustrates how the framework responds to backhaul congestion, traffic surges, and declining solar generation, improving energy efficiency, task completion, and latency over other baselines. We finally identify trustworthy control, collaborative multi-HAP orchestration, and digital-twin-assisted lifelong adaptation as key steps toward deployable, sustainable, and resilient SAGIN intelligence.

Problem

• Space-Air-Ground Integrated Networks
• Solar-Powered High-Altitude Platforms
• Coupled Energy Demands
• Sustainable SAGINs

Innovation

• Agentic AI
• High-Altitude Platforms
• Multi-timescale Design
• SAGINs
• Closed-loop Control