Solar-powered platforms improve sustainable communication and computing scheduling
SAGE-HAP: Sustainability-Certified Agentic Communication and Computing at a Solar-Powered High-Altitude Platform
Networking and Internet Architecture
Summary
High-altitude platforms powered by solar energy can provide communication and computing services where ground coverage is limited. The authors address challenges in managing these platforms sustainably while meeting changing demands and unpredictable solar power availability. They developed SAGE, a system that uses smart scheduling and energy predictions to ensure reliable operation and efficient use of resources. In simulations, SAGE improved task completion rates and energy efficiency compared to previous methods, especially under difficult conditions.
What this means in practice
- •For network schedulers: Schedule communication and computing tasks on solar-powered platforms while verifying energy feasibility to improve reliability and efficiency.
- •For drone operations teams: Manage energy and resources for solar-powered high-altitude drones providing sustained services beyond cellular coverage.
Authors
Haoxiang Luo, Bang Huang, Mohamed-Slim Alouini
Abstract
The solar-powered High-Altitude Platforms (HAP) can provide persistent communication and computing services beyond terrestrial coverage. However, changing mission priorities, coupled resource demands, and uncertain solar supply complicate sustainable scheduling, while large language model proposals require independent feasibility checks. This paper proposes SAGE, a Sustainability-certified Agentic Ground-service Engine for a HAP. We formulate an energy-causal stochastic optimization problem incorporating semantic modes, multi-user interference, channel aging, essential platform loads, and battery stress. An agent translates operator intent into typed admission, mode, and grouping candidates, while deterministic convex refinement and an action shield verify resource and reserve feasibility using calibrated solar lower bounds before execution. Agent proposals and deterministic fallbacks share the same certification path. Residual-based forecasting and memory-assisted replanning close the control loop. We establish fixed-candidate convexity, reserve safety under calibrated solar coverage and bounded actuation error, and an online utility bound accounting for candidate-generation error. At nominal load, simulations show 2.38 percentage points higher completion and 5.27% greater useful-workload energy efficiency than energy-harvesting Lyapunov control. Under stressed load and solar uncertainty, reserve violations occur in 1/50 runs, versus 47/50 without energy certification. A 600-record calibrated inference replay further shows 23.5--36.5% utility gains over deterministic-only candidates under compound stress.