A Unified Framework for Analysis and Optimization of RIS-Assisted MIMO Multiple Access Networks

Recently, reconfigurable intelligent surfaces (RISs) are gaining increasing attention in communication systems due to their ability to adapt themself according to the wireless environment. Therefore, the communication systems with massive RIS deployments are able to extend high-quality coverage ubiquitously. This is especially beneficial for Internet of Things (IoT) systems as the IoT devices can be located in hard-to-reach area. On the other hand, current methods to analyze and optimize the performance of communication systems with multiple RISs deployment are ad hoc, which depends on the specific system configurations. There lacks a unified theoretical framework that provides general analytical treatment for both passive and active RIS-assisting systems, possibly having multiple concatenated reflections via RISs, which is typical in IoT communication scenarios. Thus, we investigate general uplink RIS-assisted multi-user multiple-input multiple-output (MU-MIMO) communication systems under general Rician fading channels, where the number of cascaded RIS panels are arbitrary and the RIS elements can be active or passive. By utilizing the linearization trick and the operator-valued free probability theory, a unified analytic expression of the ergodic sum rate for the considered MU-MIMO systems is derived. Then, we further propose a low-complexity optimization approach to design the RISs’ phase shifts, thus enhancing the ergodic sum rate. Numerical results verify the accuracy of the analytical results for RIS-assisted systems. In addition, the convergence and effectiveness of the proposed optimization algorithm are demonstrated.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-04-04
DOI
https://doi.org/10.1109/jiot.2025.3557787
Citations
1
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.73
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article

A Unified Framework for Analysis and Optimization of RIS-Assisted MIMO Multiple Access Networks

Zesong Fei, Q. T. Zhang, Siqiang Wang, Jing Guo et al.
1 citations
IEEE Internet of Things Journal
Advanced MIMO Systems Optimization
0.73
article

A Unified Framework for Analysis and Optimization of RIS-Assisted MIMO Multiple Access Networks

Zesong Fei, Q. T. Zhang, Siqiang Wang, Jing Guo, Zhi Sun, Zhong Zheng
article en
1 citations

Abstract

Recently, reconfigurable intelligent surfaces (RISs) are gaining increasing attention in communication systems due to their ability to adapt themself according to the wireless environment. Therefore, the communication systems with massive RIS deployments are able to extend high-quality coverage ubiquitously. This is especially beneficial for Internet of Things (IoT) systems as the IoT devices can be located in hard-to-reach area. On the other hand, current methods to analyze and optimize the performance of communication systems with multiple RISs deployment are ad hoc, which depends on the specific system configurations. There lacks a unified theoretical framework that provides general analytical treatment for both passive and active RIS-assisting systems, possibly having multiple concatenated reflections via RISs, which is typical in IoT communication scenarios. Thus, we investigate general uplink RIS-assisted multi-user multiple-input multiple-output (MU-MIMO) communication systems under general Rician fading channels, where the number of cascaded RIS panels are arbitrary and the RIS elements can be active or passive. By utilizing the linearization trick and the operator-valued free probability theory, a unified analytic expression of the ergodic sum rate for the considered MU-MIMO systems is derived. Then, we further propose a low-complexity optimization approach to design the RISs’ phase shifts, thus enhancing the ergodic sum rate. Numerical results verify the accuracy of the analytical results for RIS-assisted systems. In addition, the convergence and effectiveness of the proposed optimization algorithm are demonstrated.

IEEE Internet of Things JournalVol. 12(13)
Beijing Institute of Technology (CN), Tsinghua University (CN)
Openalex Percentile: Top 30%
Advanced MIMO Systems Optimization
0.73
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