Optimizing Mutual Coupling in RIS-Assisted Multi-User MIMO Communication Systems for Enhanced Performance

Core Concepts

The core message of this paper is to propose a novel approach to jointly optimize active and passive beamforming as well as mutual coupling (MC) in RIS-assisted multi-user MIMO wireless communication systems, leading to enhanced system performance.

Abstract

The paper presents a novel framework to optimize the RIS phase configuration and base station (BS) active precoders using a physically-consistent end-to-end channel model that incorporates mutual coupling (MC). The authors propose an offline optimization method, applied to a class of wireless channels, to design the MC at the RIS, as opposed to state-of-the-art approaches that did not perform MC optimization.
The key highlights and insights are:

The authors formulate a novel joint optimization problem for active and passive beamforming as well as MC for RIS-aided multi-user downlink transmission based on a physically-consistent RIS model.

The offline MC optimization exhibits a complex nested structure within the overall optimization problem, involving both outer and inner optimization problems. The inner problem deals with the optimization of the RIS phase configuration and the BS active precoding, while the outer problem involves optimizing MC based on the scattering S-parameters.

The authors propose a solution approach that decomposes the overall system design into two sub-problems and deploys an alternating optimization approach. The first sub-problem is solved using the method described in prior work, while the second sub-problem is solved via a combination of the projected gradient descent method and the method of Lagrange multipliers.

The simulation results showcase that the system performance is further enhanced through joint beamforming and MC optimization. This improvement is achieved even with the proposed offline process for optimizing the MC at the RIS, emphasizing the effectiveness beyond real-time MC adjustments.

Stats

The transmit power P is varied from 30 dBm to 50 dBm.
The number of RIS elements M is varied from 64 to 100.
The number of users K is varied from 6 to 8.
The number of BS antennas N is 32.

Quotes

"Mutual Coupling (MC) is an unavoidable feature in Reconfigurable Intelligent Surfaces (RISs) with sub-wavelength inter-element spacing."
"We particularly formulate a novel problem to jointly optimize active and passive beamforming as well as MC in a physically consistent manner."
"Our simulation results showcase that the system performance is further enhanced through joint beamforming and MC optimization."

Key Insights Distilled From

Electromagnetically-Consistent Modeling and Optimization of Mutual Coupling in RIS-Assisted Multi-User MIMO Communication Systems

by Dilki Wijeko... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04539.pdf

Deeper Inquiries

How can the proposed offline MC optimization approach be extended to handle dynamic channel conditions and real-time adjustments

The proposed offline mutual coupling (MC) optimization approach can be extended to handle dynamic channel conditions and real-time adjustments by incorporating adaptive algorithms and feedback mechanisms. One way to achieve this is by integrating a feedback loop that continuously monitors the channel conditions and updates the MC optimization parameters accordingly. This feedback loop can utilize real-time channel state information (CSI) to adapt the MC optimization based on the changing wireless environment. By dynamically adjusting the MC parameters in response to variations in the channel, the system can optimize performance in real-time.

What are the potential tradeoffs between the complexity of the proposed joint optimization and the achievable performance gains

The potential tradeoffs between the complexity of the proposed joint optimization and the achievable performance gains lie in the balance between computational resources and system efficiency. The complexity of the joint optimization arises from the need to optimize multiple parameters simultaneously, including active and passive beamforming, as well as mutual coupling in a physically consistent manner. This complexity can lead to increased computational overhead and implementation challenges.
However, the achievable performance gains from the joint optimization can outweigh the complexity tradeoffs. By optimizing mutual coupling alongside beamforming, the system can benefit from improved signal quality, reduced interference, and enhanced overall performance. The tradeoff between complexity and performance gains needs to be carefully evaluated based on the specific requirements of the wireless communication system and the available computational resources.

How can the insights from this work on optimizing mutual coupling in RIS-assisted systems be applied to other emerging wireless technologies, such as integrated sensing and communications

The insights from optimizing mutual coupling in Reconfigurable Intelligent Surface (RIS)-assisted systems can be applied to other emerging wireless technologies, such as integrated sensing and communications, by leveraging similar principles of adaptive optimization and physical modeling. In integrated sensing and communications systems, where sensors and communication devices share resources and interact with the environment, optimizing mutual interactions and coupling can enhance system performance.
By applying the concept of physically-consistent modeling and optimization to integrated sensing and communications, it is possible to design adaptive systems that dynamically adjust to changing environmental conditions. This can lead to improved sensing accuracy, reduced interference, and enhanced overall efficiency in integrated systems. The lessons learned from optimizing mutual coupling in RIS-assisted systems can serve as a foundation for developing advanced solutions in the integrated sensing and communications domain.