Generative AI Agent Empowered Power Allocation for HAP Propulsion and Communication Systems
This addresses the energy allocation challenge for HAPs in 6G networks, which is an incremental improvement over prior work that ignored propulsion power or used inaccurate surrogates.
The paper tackles the problem of allocating limited energy between propulsion and communications in high altitude platforms (HAPs) for 6G, by developing an accurate propulsion power model and a QoS-enhanced energy-efficient beamforming algorithm, with simulation results showing improved accuracy and effectiveness.
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.