# Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networks

arXiv: https://arxiv.org/abs/2601.00538 · HTML: https://mlsanity.com/paper/2601.00538/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 23 references that are in the map.
- First posted on arXiv 2 Jan 2026.

## Builds on (most cited first, 18 of 18)

- [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)
- [Active RIS vs. Passive RIS: Which Will Prevail in 6G?](https://mlsanity.com/paper/2103.15154/) (2021)
- [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)
- [Resource Allocation for Multi-Cell IRS-Aided NOMA Networks](https://mlsanity.com/paper/2006.11811/) (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)
- [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)
- [A Survey of NOMA: Current Status and Open Research Challenges](https://mlsanity.com/paper/1912.10561/) (2019)
- [Optimized Energy and Information Relaying in Self-Sustainable IRS-Empowered WPCN](https://mlsanity.com/paper/2004.03108/) (2020)
- [Robust and Secure Sum-Rate Maximization for Multiuser MISO Downlink Systems with Self-sustainable IRS](https://mlsanity.com/paper/2101.10549/) (2021)
- [Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models](https://mlsanity.com/paper/2507.16204/) (2025)
- [Joint Active and Passive Beamforming for Energy-Efficient STARS with Quantization and Element Selection in ISAC Systems](https://mlsanity.com/paper/2507.16210/) (2025)
- [Queueing Theoretic Models for Multiuser MISO Content-Centric Networks with SDMA, NOMA, OMA and Rate-Splitting Downlink](https://mlsanity.com/paper/2111.06352/) (2021)
- [Multi-Functional Reconfigurable Intelligent Surface: System Modeling and Performance Optimization](https://mlsanity.com/paper/2310.02562/) (2023)

## Built on by (most cited first, 1 of 1)

- [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)

Multi-functional reconfigurable intelligent surface (MF-RIS) is conceived to address the communication efficiency thanks to its extended signal coverage from its active RIS capability and self-sustainability from energy harvesting (EH). We investigate the architecture of multi-MF-RISs to assist non-orthogonal multiple access (NOMA) downlink networks. We formulate an energy efficiency (EE) maximization problem by optimizing power allocation, transmit beamforming and MF-RIS configurations of amplitudes, phase-shifts and EH ratios, as well as the position of MF-RISs, while satisfying constraints of available power, user rate requirements, and self-sustainability property. We design a parametrized sharing scheme for multi-agent hybrid deep reinforcement learning (PMHRL), where the multi-agent proximal policy optimization (PPO) and deep-Q network (DQN) handle continuous and discrete variables, respectively. The simulation results have demonstrated that proposed PMHRL has the highest EE compared to other benchmarks, including cases without parametrized sharing, pure PPO and DQN. Moreover, the proposed multi-MF-RISs-aided downlink NOMA achieves the highest EE compared to scenarios of no-EH/amplification, traditional RISs, and deployment without RISs/MF-RISs under different multiple access.