Generative AI Agent Empowered Power Allocation for HAP Propulsion and Communication Systems

2026-04-10

AI Summary

This study addresses the challenge of limited onboard energy allocation between propulsion and communication in high-altitude platform (HAP)-enabled 6G networks. Existing approaches often neglect propulsion energy consumption or rely on oversimplified models, leading to inaccurate communication power budgets and degraded beamforming performance. To overcome this, the work introduces a generative AI agent to construct a precise propulsion power consumption model under aerodynamic disturbances and jointly optimizes communication beamforming through a novel QoS-aware energy-efficient (Q3E) algorithm. The proposed framework enables cross-domain co-modeling and co-optimization across aerodynamics, propulsion, and communication subsystems. Simulation results demonstrate that the developed model significantly improves propulsion power estimation accuracy, while the Q3E algorithm substantially enhances system energy efficiency without compromising user quality of service.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

High altitude platforms (HAPs) are emerging as a key enabler for 6G coverage, yet limited energy must be split between propulsion and communications. Most prior HAP studies ignore propulsion power or rely on surrogates that miss hull-propeller interference, leading to misestimated communication power budgets and degraded beamforming. More importantly, HAP power allocation is intrinsically a multi-system, multidisciplinary problem in which aerodynamics, propulsion-system efficiency, and communication-system performance (quality of service (QoS) and energy efficiency (EE)) are tightly coupled.To address these challenges, this paper designs an interactive generative artificial intelligence (AI)-empowered HAP power allocation agent.By interacting with the AI agent, we develop an accurate propulsion power consumption model that takes into account the aerodynamic interference between the HAP's hull and the propeller. Assisted by the AI agent, we further formulate a HAP beamforming problem to improve user QoS and enhance the EE of the HAP communication system.This paper also proposes a QoS-enhanced energy-efficient (Q3E) beamforming algorithm to solve the formulated problem.Simulation results demonstrate the accuracy of the propulsion-power model and the effectiveness of the Q3E algorithm.

Problem

• High Altitude Platforms
• Power Allocation
• Propulsion-Communication Coupling
• Aerodynamic Interference
• Energy Efficiency

Innovation

• Generative AI Agent
• High Altitude Platform (HAP)
• Propulsion-Communication Power Allocation
• Aerodynamic Interference Modeling
• QoS-Enhanced Energy-Efficient Beamforming