自适应与弹性双层资源切片用于悬停空中回程网络
Adaptive and Resilient Dual-Layer Resource Slicing for Hovering Aerial Backhaul Networks
- National Chung Cheng University（国立中正大学）
- Feng Chia University（逢甲大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出RAPO-TD3框架，通过双软最大投影和弹性优先级编排机制，解决悬停空中代理辅助回程网络中的双层资源切片问题，实现URLLC延迟保障与高效性能。
AI中文摘要：
本文研究了在悬停空中代理（HAA）辅助的回程网络中，针对异构5G/6G服务（包括增强移动宽带（eMBB）、超可靠低延迟通信（URLLC）和大规模机器类型通信（mMTC））的自适应与弹性双层资源切片问题。为解决非平稳环境下该双层架构的复杂耦合问题，我们提出了弹性自适应优先级编排增强型双延迟深度确定性策略梯度（RAPO-TD3）框架。我们引入了一种新颖的双软最大投影机制，将连续动作空间映射为物理可行的带宽分配，确保严格满足约束条件。此外，嵌入了一种弹性自适应优先级编排（RAPO）机制，以保障关键任务URLLC延迟。关键的是，我们建立了严格的数学基础，证明我们的框架确保Lipschitz连续性并满足Robbins-Monro条件，以实现稳定的渐近收敛。在非平稳流量下的大量仿真表明，我们的RAPO-TD3框架相对于PPO、DDPG和传统求解器实现了优越的性能。值得注意的是，通过RAPO机制，即使在500%需求激增期间，我们的方法也能使URLLC满意度水平接近理论最优。此外，可扩展性评估表明，亚毫秒级执行延迟严格满足1毫秒URLLC预算，证明了我们提出框架的性能有效性。
英文摘要：
This paper investigates adaptive and resilient dual-layer resource slicing in hovering aerial agent (HAA)-assisted backhaul networks for heterogeneous 5G/6G services, including enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine-type communications (mMTC). To address the complex coupling of this dual-layer architecture in non-stationary environments, we propose the resilient adaptive priority orchestration enhanced twin delayed deep deterministic policy gradient (RAPO-TD3) framework. We introduce a novel double soft-max projection mechanism to map the continuous action space into physically feasible bandwidth distributions, ensuring strict constraint adherence. Additionally, a resilient adaptive priority orchestration (RAPO) mechanism is embedded to safeguard mission-critical URLLC latency. Crucially, we establish a rigorous mathematical foundation proving that our framework ensures Lipschitz continuity and satisfies the Robbins-Monro conditions for stable asymptotic convergence. Extensive simulations under non-stationary traffic demonstrate that our RAPO-TD3 framework achieves superior performance relative to PPO, DDPG, and traditional solvers. Notably, via the RAPO mechanism, our approach maintains URLLC satisfaction levels closely approaching theoretical optima even during 500% demand surges. Furthermore, scalability evaluations indicate that sub-millisecond execution latencies strictly satisfy the 1 ms URLLC budget, demonstrating the performance efficacy of our proposed framework.
