用深度强化学习优化无人机+智能反射面的频谱共享，提升能效。
DRL-Based Resource Allocation for Energy-Efficient IRS-Assisted UAV Spectrum Sharing Systems
- 基于深度强化学习联合优化波束成形、子载波分配等参数。
- 相比基准方案，能效提升显著，验证了方法有效性。
- 适合关注无人机通信能效与智能反射面应用的研究者。
智能反射面（IRS）辅助的无人机（UAV）系统为可重构、灵活的无线通信提供了新范式。为实现更节能、高效的IRS辅助无人机频谱共享通信，本文提出一种基于正交频分复用（OFDM）的新型系统。目标是在满足实际发射功率、被动反射约束及无人机物理限制的前提下，通过联合优化波束成形、子载波分配、IRS相位偏移和无人机轨迹，最大化次级网络的能效（EE）。采用物理驱动的推进-能量模型，并以其紧上界构造可处理的能效下界。针对高度非凸、时序耦合且具有连续与离散混合策略空间的优化难题，提出基于演员-评论家框架的深度强化学习（DRL）方法。大量实验表明，所提DRL方法在能效方面显著优于多个基准方案，验证了其在移动场景下的有效性和鲁棒性。
原文摘要 · Abstract (English)
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.
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