RIS-Assisted Cell-Free Massive MIMO: RIS-MS Selection in FR1 and FR3

TL;DR

This paper investigates RIS-assisted cell-free massive MIMO in FR1 and FR3 bands, proposing a novel RIS-user association algorithm that enhances spectral efficiency and discusses trade-offs in training overhead.

Contribution

It introduces a new RIS-user association method tailored for realistic propagation conditions, improving spectral efficiency in RIS-assisted CF-mMIMO systems.

Findings

RIS-user selection significantly boosts spectral efficiency.

Trade-off analysis shows excessive training overhead can negate benefits.

FR3 bands are promising for RIS-assisted CF-mMIMO with advanced estimation.

Abstract

This paper explores the integration of reconfigurable intelligent surfaces (RISs) into cell-free massive multiple-input-multiple-output (CF-mMIMO) networks operating in FR1 and FR3 frequency bands. We present a comprehensive framework for analyzing RIS-assisted CF-mMIMO systems under realistic propagation conditions, accounting for frequency-dependent characteristics and RIS configurations. A novel RIS-user association algorithm is proposed to optimize phase-shift settings by assigning each RIS to a single user based on line of sight (LoS) connectivity. The system model incorporates spatially correlated Ricean fading channels and employs scalable partial-minimum mean square error (P-MMSE) combining. The numerical results demonstrate that the proposed RIS-user selection strategy significantly improves the spectral efficiency compared to random or exhaustive RIS configurations, particularly when the number of RISs is moderate. We also analyze the trade-off between training overhead and performance gains, showing that excessive pilot requirements can offset benefits when RIS density or element count increases. The results highlight the potential of the FR3 bands for RIS-assisted CF-mMIMO, provided advanced channel estimation techniques are adopted to mitigate overhead. These findings emphasize the importance of intelligent RIS-user pairing and scalable estimation methods for future 6G deployments.