具有Lyapunov稳定性意识的Stackelberg博弈：低空经济中的控制导向剪枝型DRL方法
Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach
AI总结：
本文提出一种基于剪枝的DRL方法，通过Lyapunov稳定性理论和Stackelberg博弈模型，解决低空经济中资源分配与稳定性之间的冲突，实现动态环境下的控制稳定性与系统效用最大化。
摘要：
随着低空经济的迅速发展，无人驾驶航空器（UAVs）作为关键的空中基站，为用户提供从对时延敏感的紧急任务到带宽需求大的数据流传输等多种服务。然而，此类异构网络的效率常受到有限的机载资源与严格稳定性要求之间的冲突所限制。在超越传统以吞吐量为中心的设计之外，我们提出一个包含感知-通信-计算-控制闭环框架，明确建模通信时延对物理控制稳定性的影响。为保证任务可靠性，我们利用Lyapunov稳定性理论，推导出控制系统状态演变与通信约束之间的内在映射，将抽象的稳定性要求转化为可量化的资源边界。然后，我们将资源分配问题建模为一个Stackelberg博弈，其中UAVs（作为领导者）动态定价资源以平衡负载并确保稳定性，而用户（作为跟随者）根据服务紧急程度优化请求。此外，为了解决标准深度强化学习（DRL）在能量受限的边缘平台上计算开销过大的问题，我们提出了一种新颖且轻量的基于剪枝的近端策略优化（PPO）算法。通过整合动态结构化剪枝机制，所提出算法在训练过程中显著压缩神经网络规模，使UAV能够快速近似博弈均衡，同时保持最小的推理延迟。仿真结果表明，所提出的方案在动态低空环境中有效保障了控制回路的稳定性，同时最大化了系统的效用。
英文摘要：
With the rapid expansion of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) serve as pivotal aerial base stations supporting diverse services from users, ranging from latency-sensitive critical missions to bandwidth-intensive data streaming. However, the efficacy of such heterogeneous networks is often compromised by the conflict between limited onboard resources and stringent stability requirements. Moving beyond traditional throughput-centric designs, we propose a Sensing-Communication-Computing-Control closed-loop framework that explicitly models the impact of communication latency on physical control stability. To guarantee mission reliability, we leverage the Lyapunov stability theory to derive an intrinsic mapping between the state evolution of the control system and communication constraints, transforming abstract stability requirements into quantifiable resource boundaries. Then, we formulate the resource allocation problem as a Stackelberg game, where UAVs (as leaders) dynamically price resources to balance load and ensure stability, while users (as followers) optimize requests based on service urgency. Furthermore, addressing the prohibitive computational overhead of standard Deep Reinforcement Learning (DRL) on energy-constrained edge platforms, we propose a novel and lightweight pruning-based Proximal Policy Optimization (PPO) algorithm. By integrating a dynamic structured pruning mechanism, the proposed algorithm significantly compresses the neural network scale during training, enabling the UAV to rapidly approximate the game equilibrium with minimal inference latency. Simulation results demonstrate that the proposed scheme effectively secures control loop stability while maximizing system utility in dynamic low-altitude environments.
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