Performance Analysis of STAR-RIS-Assisted Cell-Free Massive MIMO Systems with Electromagnetic Interference and Phase Errors

TL;DR

This work analyzes STAR-RIS–assisted cell-free massive MIMO under EMI and phase errors, introducing a tailored projected gradient descent method to optimize STAR-RIS coefficients for NMSE minimization. It derives closed-form uplink and downlink SE expressions with fractional power control, and evaluates performance through analytical results and simulations. The findings show substantial NMSE and SE gains from the proposed optimization (approximately 30% NMSE and over 10% SE improvements), with SE increasing as APs, antennas per AP, and STAR-RIS elements rise, while severe EMI/phase errors reduce the advantages of STAR-RIS relative to conventional RIS. Compared to RIS, STAR-RIS provides better resilience in highly impaired environments, underscoring its potential for robust 6G deployments. The study also highlights scalability and optimization challenges, motivating future work on scalable implementations, time-varying channels, and more advanced EMI mitigation strategies.

Abstract

Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) are being explored for sixth-generation (6G) wireless networks. A promising configuration for their deployment is within cell-free massive multiple-input multiple-output (MIMO) systems. However, despite the advantages that STAR-RISs could bring, challenges such as electromagnetic interference (EMI) and phase errors may lead to significant performance degradation. In this paper, we investigate the impact of EMI and phase errors on STAR-RIS-assisted cell-free massive MIMO systems and propose techniques to mitigate these effects. We introduce a tailored projected gradient descent (GD) algorithm for STAR-RIS coefficient matrix design by minimizing the local channel estimation normalized mean square error (NMSE). We also derive the novel closed-form expressions of the uplink and downlink spectral efficiency (SE) to analyze system performance with EMI and phase errors, in which fractional power control methods are introduced for performance improvement. The results reveal that the projected GD algorithm can effectively tackle EMI and phase errors to improve estimation accuracy and compensate for performance degradation with nearly 30% NMSE improvement and over 10% SE improvement. Moreover, increasing the number of access points (APs), antennas per AP, and STAR-RIS elements can also improve SE performance. However, the advantages of employing STAR-RIS are reduced when EMI and phase errors are severe. Notably, compared to conventional RISs, the incorporation of STAR-RIS in the proposed system yields better performance and presents less performance degradation in highly impaired environments.

Figures (6)

• Figure 1: Illustration of a STAR-RIS-assisted cell-free massive MIMO system.
• Figure 2: NMSE versus the pilot transmit power $p_p$ operating at different EMI powers and ranges of phase errors with $M=10$, $N=4$, $K=6$, $K_t=K_r=3$, $L=16$ (MC Simulations and Analytical Results).
• Figure 3: Sum SE versus the number of APs $M$ operating at different uplink beamforming schemes with LSFD and $N=4$, $K=6$, $K_t=K_r=3$, $L=16$ (MC Simulations and Analytical Results).
• Figure 4: Sum SE versus the number of antennas per AP $N$ operating at different ranges of phase errors with LSFD and $M=10$, $K=6$, $K_t=K_r=3$, $L=16$ (Analytical Results).
• Figure 5: Average SE per user versus the number of users $K$ operating at different EMI powers with LSFD and $M=10$, $N=4$, $K_t=K_r$, $L=16$ (Analytical Results).