A pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated to improve the task offloading efficiency and latency performance in dynamic wireless environments. By leveraging dielectric waveguides and flexibly adjustable pinching antennas, PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage, making it a promising solution for high-frequency MEC systems. We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading. The resulting problem is modeled as a Markov decision process (MDP) and solved via the deep reinforcement learning (DRL) method. To address the instability introduced by the $\max$ operator in the objective function, we propose a load balancing-aware proximal policy optimization (LBPPO) algorithm. LBPPO incorporates both node-level and waveguide-level load balancing information into the policy design, maintaining computational and transmission delay equilibrium, respectively. Simulation results demonstrate that the proposed PASS-enhanced MEC with adaptive uplink PASS beamforming exhibit stronger convergence capability than fixed-PA baselines and conventional MIMO-assisted MEC, especially in scenarios with a large number of UEs or high transmit power.

翻译：本文研究了一种基于夹持天线系统（PASS）增强的移动边缘计算（MEC）架构，旨在提升动态无线环境中的任务卸载效率和延迟性能。通过利用介质波导和灵活可调的夹持天线，PASS能够建立短距离视距（LoS）链路，同时有效缓解显著的路径损耗和潜在的信号阻塞，使其成为高频MEC系统的有前景解决方案。我们将网络延迟最小化问题建模为联合优化上行PASS波束成形和任务卸载的优化问题。该问题被建模为马尔可夫决策过程（MDP），并通过深度强化学习（DRL）方法求解。为解决目标函数中$\max$算子引入的不稳定性，我们提出了一种负载均衡感知的近端策略优化（LBPPO）算法。LBPPO将节点级和波导级的负载均衡信息同时纳入策略设计，分别维持计算延迟和传输延迟的均衡。仿真结果表明，所提出的具有自适应上行PASS波束成形的PASS增强型MEC，相比固定PA基线方案和传统MIMO辅助MEC，展现出更强的收敛能力，尤其是在用户设备数量众多或发射功率较高的场景中。