STAR-RIS-Assisted Cell-Free Massive MIMO with Multi-antenna Users and Hardware Impairments Over Correlated Rayleigh Fading Channels

2025-03-17

AI Summary

This work addresses the performance degradation in STAR-RIS-aided cell-free massive MIMO systems caused by the coupling of hardware impairments and spatially correlated Rayleigh fading channels. We establish, for the first time, a unified system model incorporating multi-antenna users, multiple access points (APs), and multi-element STAR-RISs, and propose a comprehensive analytical framework covering two processing architectures: Level 1 (local uplink processing at APs with centralized decoding) and Level 2 (fully centralized processing). Closed-form spectral efficiency expressions are derived for both levels. Theoretical analysis reveals that Level 2 strictly outperforms Level 1 under any combining scheme—by up to 28%—and identifies user-side hardware impairments as the dominant performance bottleneck. Increasing the number of user antennas significantly mitigates this degradation. Numerical results further demonstrate that scaling the numbers of APs, STAR-RIS elements, and user antennas effectively alleviates hardware impairment effects.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multi-Robot Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

Abstract

Integrating cell-free massive multiple-input multiple-output (MIMO) with simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) can provide ubiquitous connectivity and enhance coverage. This paper explores a STAR-RIS-assisted cell-free massive MIMO system featuring multi-antenna users, multi-antenna access points (APs), and multi-element STAR-RISs, accounting for transceiver hardware impairments. We first establish the system model of STAR-RIS-assisted cell-free massive MIMO systems with multi-antenna users. Subsequently, we analyze two uplink implementations: local processing and centralized decoding (Level 1), and fully centralized processing (Level 2), both implementations incorporating hardware impairments. We study the local and global minimum mean square error (MMSE) combining schemes to maximize the uplink spectral efficiency (SE) for Level 1 and Level 2, respectively. The MMSE-based successive interference cancellation detector is utilized to compute the uplink SE. We introduce the optimal large-scale fading decoding at the central processing unit and derive closed-form SE expressions utilizing maximum ratio combining at APs for Level 1. Our numerical results reveal that hardware impairments negatively affect SE performance, particularly at the user end. However, this degradation can be mitigated by increasing the number of user antennas. Enhancing the number of APs and STAR-RIS elements also improves performance and mitigates performance degradation. Notably, unlike conventional results based on direct links, our findings show that Level 2 consistently outperforms Level 1 with arbitrary combining schemes for the proposed STAR-RIS-assisted system.

Problem

• Enhance coverage in cell-free massive MIMO with STAR-RIS.
• Analyze uplink spectral efficiency with hardware impairments.
• Compare centralized vs. local processing in STAR-RIS systems.

Innovation

• STAR-RIS enhances cell-free massive MIMO connectivity.
• MMSE combining maximizes uplink spectral efficiency.
• Increased antennas mitigate hardware impairment effects.