# Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models arXiv: https://arxiv.org/abs/2507.16204 ยท HTML: https://mlsanity.com/paper/2507.16204/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 69 references that are in the map. - First posted on arXiv 22 Jul 2025. ## Builds on (most cited first, 26 of 26) - [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015) - [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015) - [Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space](https://mlsanity.com/paper/1810.06394/) (2018) - [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019) - [Five Facets of 6G: Research Challenges and Opportunities](https://mlsanity.com/paper/2212.07902/) (2022) - [A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future](https://mlsanity.com/paper/2007.15088/) (2020) - [Active RIS vs. Passive RIS: Which Will Prevail in 6G?](https://mlsanity.com/paper/2103.15154/) (2021) - [An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems](https://mlsanity.com/paper/2112.05989/) (2021) - [Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer](https://mlsanity.com/paper/2205.05029/) (2022) - [LEO Satellite Access Network (LEO-SAN) Towards 6G: Challenges and Approaches](https://mlsanity.com/paper/2207.11896/) (2022) - [Simultaneously Transmitting and Reflecting Surface (STARS) for Terahertz Communications](https://mlsanity.com/paper/2212.00497/) (2022) - [Joint Deployment and Multiple Access Design for Intelligent Reflecting Surface Assisted Networks](https://mlsanity.com/paper/2005.11544/) (2020) - [D-STAR: Dual Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces for Joint Uplink/Downlink Transmission](https://mlsanity.com/paper/2307.16096/) (2023) - [AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent Surfaces: Deployment, Prototyping, Experiments, and Opportunities](https://mlsanity.com/paper/2311.04241/) (2023) - [Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data](https://mlsanity.com/paper/2405.06299/) (2024) - [Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications](https://mlsanity.com/paper/2308.08279/) (2023) - [RIS-Aided Fluid Antenna Array-Mounted UAV Networks](https://mlsanity.com/paper/2501.09351/) (2025) - [Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks](https://mlsanity.com/paper/2501.11079/) (2025) - [HAPS-assisted Hybrid RF-FSO Multicast Communications: Error and Outage Analysis](https://mlsanity.com/paper/2109.10131/) (2021) - [RIS-Assisted Energy-Efficient LEO Satellite Communications with NOMA](https://mlsanity.com/paper/2306.10422/) (2023) - [Downlink Rate Maximization with Reconfigurable Intelligent Surface Assisted Full-Duplex Transmissions](https://mlsanity.com/paper/2311.03579/) (2023) - [Hierarchical Multi-Agent Multi-Armed Bandit for Resource Allocation in Multi-LEO Satellite Constellation Networks](https://mlsanity.com/paper/2303.14351/) (2023) - [Attention-Enhanced Deep Learning for Device-Free Through-the-Wall Presence Detection Using Indoor WiFi Systems](https://mlsanity.com/paper/2304.13105/) (2023) - [Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiuser Communication Systems Exploiting Statistical CSI of Correlated RIS-User Channels](https://mlsanity.com/paper/2309.16029/) (2023) - [Optimal Operation of Active RIS-Aided Wireless Powered Communications in IoT Networks](https://mlsanity.com/paper/2409.09719/) (2024) - [Channel Estimation and Beamforming Design for MF-RIS-Aided Communication Systems](https://mlsanity.com/paper/2501.10657/) (2025) ## Built on by (most cited first, 2 of 2) - [Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networks](https://mlsanity.com/paper/2601.00538/) (2026) - [Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2604.14309/) (2026) ## Abstract (arXiv) 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.