AI 中文总结
针对RIS辅助大规模物联网在线资源分配的高维计算挑战，提出PAAERL框架，通过帕累托感知降维与自动编码器压缩优化，缩短训练时间、加速收敛并降低网络成本，具备良好可扩展性。
AI 中文摘要
随着5G及新兴6G网络的快速发展，可重构智能表面（RIS）已成为增强无线通信场景的关键技术。然而，优化RIS辅助多用户系统通常会引入高维物理层变量与非凸帕累托最优速率集，对实时应用造成严峻的计算挑战。为解决这些局限，本文提出一种降维的分层强化学习（RL）框架，命名为帕累托感知自动编码器辅助强化学习（PAAERL），用于优化RIS辅助物联网（IoT）网络的在线资源分配。该方法首先用严格表征帕累托最优前沿的低维权重向量替代高维连续RIS波束成形变量，从理论上避免凸与非凸速率区域的几何信息丢失；为进一步缓解密集网络中的维度灾难，集成自动编码器架构执行二次数据驱动压缩阶段，将优先级空间映射至高度浓缩的连续潜在动作空间。在多用户移动边缘计算（MEC）网络等实际通信场景中开展的大量仿真表明，与现有最优基准相比，所提PAAERL框架大幅缩短离线训练时间、加快在线策略收敛速度，并显著降低整体网络成本，凸显其对下一代智能物联网环境的卓越可扩展性与实际可行性。
英文摘要
With the rapid evolution of 5G and emerging 6G networks, reconfigurable intelligent surfaces (RIS) have become a critical technology for enhancing wireless communication scenarios. However, optimizing RIS-assisted multi-user systems typically introduces high-dimensional physical layer variables and non-convex Pareto-optimal rate sets, posing severe computational challenges for real-time applications. To address these limitations, this paper proposes a dimension-reduced, hierarchical reinforcement learning (RL) framework, termed Pareto-aware autoencoder-assisted RL (PAAERL), to optimize online resource allocation in RIS-assisted Internet of Things (IoT) networks. Our approach first substitutes high-dimensional continuous RIS beamforming variables with lower-dimensional weight vectors that strictly represent the Pareto-optimal frontier, theoretically avoiding geometric information loss across both convex and non-convex rate regions. To further mitigate the curse of dimensionality in dense networks, an autoencoder architecture is integrated to execute a secondary, data-driven compression phase, mapping the priority space into a highly condensed continuous latent action space. Extensive simulations conducted across practical communication scenarios, including multi-user mobile edge computing (MEC) networks, demonstrate that the proposed PAAERL framework drastically reduces offline training times, accelerates online policy convergence, and significantly decreases overall network costs compared to state-of-the-art benchmarks, underscoring its exceptional scalability and practical viability for next-generation intelligent IoT environments.
