A space-air-ground integrated network (SAGIN) for Internet of Things (IoT) network architecture is investigated, empowered by multi-functional reconfigurable intelligent surfaces (MF-RIS) capable of simultaneously reflecting, amplifying, and harvesting wireless energy. The MF-RIS plays a pivotal role in addressing the energy shortages of low-Earth orbit (LEO) satellites operating in the shadowed regions, while accounting for both communication and computing energy consumption across the SAGIN nodes. To maximize the long-term energy efficiency (EE) of IoT devices, we formulate a joint optimization problem over the MF-RIS parameters, including signal amplification, phase-shifts, energy harvesting ratio, and active element selection as well as the SAGIN parameters of beamforming vectors, high-altitude platform station (HAPS) deployment, IoT device association, and computing capability. The formulated problem is highly non-convex and non-linear and contains mixed discrete-continuous parameters. To tackle this, we conceive a compressed hybrid twin-model enhanced multi-agent deep reinforcement learning (CHIMERA) framework, which integrates semantic state-action compression and parametrized sharing under hybrid reinforcement learning to efficiently explore suitable complex actions. The simulation results have demonstrated that the proposed CHIMERA scheme substantially outperforms the conventional benchmarks, including fixed-configuration or non-harvesting MF-RIS, traditional RIS, and no-RIS cases, as well as centralized and multi-agent deep reinforcement learning baselines in terms of the highest EE. Moreover, the proposed SAGIN-MF-RIS architecture in IoT network achieves superior EE performance due to its complementary coverage, offering notable advantages over either standalone satellite, aerial, or ground-only deployments.

翻译：本文研究了一种面向物联网（IoT）的网络架构——空天地一体化网络（SAGIN），该网络由多功能可重构智能表面（MF-RIS）赋能，MF-RIS能够同时反射、放大和收集无线能量。MF-RIS在解决运行于阴影区域的低地球轨道（LEO）卫星的能量短缺问题中起着关键作用，同时兼顾了SAGIN各节点在通信和计算方面的能量消耗。为了最大化物联网设备的长期能量效率（EE），我们构建了一个联合优化问题，优化变量包括MF-RIS参数（如信号放大、相移、能量收集比和有源单元选择）以及SAGIN参数（如波束成形向量、高空平台站（HAPS）部署、物联网设备关联和计算能力）。该问题具有高度非凸和非线性，且包含混合的离散-连续参数。为了解决此问题，我们构思了一个压缩混合孪生模型增强的多智能体深度强化学习（CHIMERA）框架，该框架在混合强化学习下集成了语义状态-动作压缩和参数化共享，以高效探索合适的复杂动作。仿真结果表明，所提出的CHIMERA方案在最高能量效率方面显著优于传统基准方案，包括固定配置或无能量收集的MF-RIS、传统RIS、无RIS情况，以及集中式和传统多智能体深度强化学习基线。此外，由于其在覆盖上的互补性，所提出的物联网网络中的SAGIN-MF-RIS架构实现了卓越的能量效率性能，相较于单独的卫星、空中或仅地面部署方案具有显著优势。