Intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) systems provide a new paradigm for reconfigurable and flexible wireless communications. To enable more energy efficient and spectrum efficient IRS assisted UAV wireless communications, this paper introduces a novel IRS-assisted UAV enabled spectrum sharing system with orthogonal frequency division multiplexing (OFDM). The goal is to maximize the energy efficiency (EE) of the secondary network by jointly optimizing the beamforming, subcarrier allocation, IRS phase shifts, and the UAV trajectory subject to practical transmit power and passive reflection constraints as well as UAV physical limitations. A physically grounded propulsion-energy model is adopted, with its tight upper bound used to form a tractable EE lower bound for the spectrum sharing system. To handle highly non convex, time coupled optimization problems with a mixed continuous and discrete policy space, we develop a deep reinforcement learning (DRL) approach based on the actor critic framework. Extended experiments show the significant EE improvement of the proposed DRL-based approach compared to several benchmark schemes, thus demonstrating the effectiveness and robustness of the proposed approach with mobility.

翻译：智能反射面辅助无人机系统为可重构的灵活无线通信提供了新范式。为实现更高能效与频谱效率的IRS辅助无人机无线通信，本文提出一种采用正交频分复用的新型IRS辅助无人机频谱共享系统。其目标是在满足实际发射功率与无源反射约束以及无人机物理限制的条件下，通过联合优化波束成形、子载波分配、IRS相移及无人机轨迹，最大化次级网络的能量效率。研究采用基于物理原理的推进能耗模型，并利用其紧致上界构建频谱共享系统的可处理EE下界。针对具有混合连续-离散策略空间的高度非凸、时间耦合优化问题，我们开发了一种基于演员-评论家框架的深度强化学习方法。扩展实验表明，与多种基准方案相比，所提出的基于DRL的方法能显著提升EE性能，从而验证了所提移动性方案的有效性与鲁棒性。