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.

翻译：高空平台（HAPs）正成为实现6G覆盖的关键推动力，然而其有限的能量必须在推进和通信之间进行分配。以往的大多数HAP研究忽略了推进功率，或依赖未能考虑机身-螺旋桨干扰的替代模型，导致通信功率预算的误估和波束赋形性能下降。更重要的是，HAP功率分配本质上是一个多系统、多学科交叉问题，其中空气动力学、推进系统效率以及通信系统性能（服务质量（QoS）与能量效率（EE））紧密耦合。为应对这些挑战，本文设计了一种交互式生成式人工智能（AI）赋能的HAP功率分配代理。通过与AI代理交互，我们建立了一个精确的推进功率消耗模型，该模型考虑了HAP机身与螺旋桨之间的气动干扰。在AI代理的辅助下，我们进一步构建了一个HAP波束赋形问题，以提升用户QoS并增强HAP通信系统的EE。本文还提出了一种QoS增强节能（Q3E）波束赋形算法来求解所构造的问题。仿真结果验证了推进功率模型的准确性以及Q3E算法的有效性。