Active RIS vs. Passive RIS: Which Will Prevail in 6G?
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Abstract (arXiv)
As a revolutionary paradigm for controlling wireless channels, reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the "multiplicative fading" effect, the existing passive RISs only achieve limited capacity gains in many scenarios with strong direct links. In this paper, the concept of active RISs is proposed to overcome this fundamental limitation. Unlike passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we develop and verify the signal model of active RISs through the experimental measurements based on a fabricated active RIS element. Based on the verified signal model, we further analyze the asymptotic performance of active RISs to reveal the substantial capacity gain they provide for wireless communications. Finally, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit beamforming and reflect precoding scheme is proposed to solve this problem. Simulation results show that, in a typical wireless system, passive RISs can realize only a limited sum-rate gain of 22%, while active RISs can achieve a significant sum-rate gain of 130%, thus overcoming the "multiplicative fading" effect.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Mar 2021.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 2.
8 papers.
- 2025Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Modelscited by 2
- 2026Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networkscited by 1
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approachcited by 0
- 2024QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-STAR-RIScited by 0
- 2024Multi-stream Transmission for Directional Modulation Network via Distributed Multi-UAV-aided Multi-active-IRScited by 0
- 2025Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systemscited by 0
- 2025Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimizationcited by 0
- 2026Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusingcited by 0
- 2026Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusing7 Mar 2026
- 2026Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networks2 Jan 2026
- 2025Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimization17 Oct 2025
- 2025Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems9 Aug 2025
- 2025Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models22 Jul 2025
- 2024Multi-stream Transmission for Directional Modulation Network via Distributed Multi-UAV-aided Multi-active-IRS26 Mar 2024
- 2024QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-STAR-RIS16 Jan 2024
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach11 Apr 2023