The evolution toward 6G wireless networks envisions a seamlessly intelligent, Open-RAN-enabled architecture where unmanned aerial vehicles (UAVs) play a pivotal role in extending coverage, enhancing resilience, and ensuring reliable connectivity for ground users deployment. However, efficiently managing spectrum and resources in such highly dynamic UAV-assisted environments remains a major challenge due to nonlinear system interactions, mobility-induced topology variations, and stringent latency and energy constraints. To address these challenges, we propose a digital twin (DT)-assisted adaptive deep reinforcement learning (DRL) framework that enables intelligent spectrum sharing and resource allocation across distributed ground users. The complex optimization problem is decomposed into UAV trajectory optimization using particle swarm optimization (PSO) and dynamic spectrum-power-association management via multi-agent DRL (MADRL). This hybrid DT-driven approach empowers intelligent, context-aware decision-making and adaptive coordination among UAVs. Extensive simulations demonstrate significant gains in spectral efficiency, data rates, and energy utilization, showcasing a transformative path toward self-evolving, autonomous 6G UAV and ground users (GUs) connectivity.

翻译：面向6G无线网络的演进方向，构想了无缝智能且基于开放无线接入网（Open-RAN）的体系架构，其中无人机在扩展覆盖范围、增强网络弹性以及确保地面用户部署的可靠连接性方面发挥着关键作用。然而，在此类高度动态的无人机辅助环境中，由于非线性系统交互、移动性导致的拓扑变化以及严格的时延和能量约束，高效管理频谱与资源仍是一项重大挑战。针对这些问题，本文提出一种数字孪生辅助的自适应深度强化学习框架，该框架能够实现分布式地面用户间的智能频谱共享与资源分配。该复杂优化问题被分解为：基于粒子群优化的无人机轨迹规划，以及通过多智能体深度强化学习实现的动态频谱-功率-关联管理。这种混合数字孪生驱动方法赋能无人机实现智能、上下文感知的决策与自适应协同。大量仿真实验表明，该方法在频谱效率、数据传输速率和能量利用率方面均取得了显著提升，为构建面向6G自演进自主连接的无人机与地面用户系统开辟了变革性路径。