[Paper Review] Active RIS vs. Passive RIS: Which Will Prevail in 6G?

Zijian Zhang, Linglong Dai+5 more|arXiv (Cornell University)|Mar 28, 2021

Advanced Wireless Communication Technologies34 references29 citations

TL;DR

The paper introduces active RISs as a solution to overcome multiplicative fading in RIS-aided networks, provides a signal model validated by experiments, analyzes asymptotic performance, and proposes a joint beamforming/reflect precoding design showing substantial sum-rate gains over passive RISs.

ABSTRACT

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.

Motivation & Objective

• Motivate the need to overcome the multiplicative fading limitation of passive RISs in RIS-aided 6G networks.
• Propose active RISs with integrated reflection-type amplifiers and develop a validated signal model.
• Characterize asymptotic performance and compare to passive RISs.
• Formulate and solve a sum-rate maximization problem for active RIS-aided MU-MISO systems.
• Extend beamforming design to account for practical self-interference in active RISs.

Proposed method

• Develop a signal model for active RISs that includes amplification and both dynamic and static noise; validate via measurements with a fabricated active RIS element.
• Derive asymptotic SNR expressions for passive and active RISs and compare scaling with the number of RIS elements N.
• Formulate a sum-rate maximization problem for an active RIS–aided MU-MISO system and propose a joint transmit beamforming and reflect precoding scheme using fractional programming.
• Incorporate self-interference modelling for active RISs and propose an ADMM/SUMT-based alternating optimization approach.
• Provide performance insights showing when active RISs outperform passive RISs under realistic power and noise conditions.

Experimental results

Research questions

• RQ1 How does the active RIS signal model differ from the passive RIS model and what noise sources must be accounted for?
• RQ2 What are the asymptotic SNR gains of active RISs compared to passive RISs as the number of RIS elements grows?
• RQ3 How can we jointly design transmit beamforming and active RIS reflect precoding to maximize sum-rate in MU-MISO systems?
• RQ4 How does self-interference in active RISs affect performance and how can it be mitigated in the optimization framework?

Key findings

• Active RISs can overcome the multiplicative fading of passive RISs by amplifying reflected signals, leading to substantial sum-rate gains.
• Asymptotic SNR scales as N for active RISs versus N^2 for passive RISs, yet the active RIS denominator is often much smaller, yielding higher gains in practice.
• Under practical parameters, passive RISs yield around 22% sum-rate gain, while active RISs yield around 130% sum-rate gain.
• A joint beamforming and reflect precoding scheme based on FP achieves effective optimization of the sum-rate in active RIS-aided MU-MISO systems.
• When accounting for self-interference, the proposed optimization framework can still achieve significant performance improvements over passive RISs.