Performance Analysis and Optimization of STAR-RIS-Aided Cell-Free Massive MIMO Systems Relying on Imperfect Hardware

2024-12-31

AI Summary

This work investigates a STAR-RIS-aided cell-free massive MIMO system under practical hardware impairments. It jointly models spatially correlated channels, transceiver hardware distortions, time-varying phase noise, and RIS phase quantization errors, and designs an MMSE cascaded channel estimator robust to pilot contamination. A closed-form expression for the downlink ergodic spectral efficiency (SE) is derived under finite numbers of access points (APs) and RIS elements. The paper proposes a novel joint optimization framework for transmission/reflection beamforming and power allocation to maximize the worst-user SE. To solve the resulting non-convex and quasi-concave problem, an alternating optimization (AO) algorithm is developed, integrating adaptive particle swarm optimization (APSO) with bisection search. Numerical results demonstrate that the proposed scheme significantly outperforms conventional RIS-based approaches in both overall and worst-user ergodic SE. Furthermore, the impact of each hardware impairment on system performance is quantitatively characterized.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems are investigated under spatially correlated fading channels using realistic imperfect hardware. Specifically, the transceiver distortions, extcolor{black}{time-varying phase noise, and RIS phase shift errors} are considered. Upon considering imperfect hardware and pilot contamination, we derive a linear minimum mean-square error (MMSE) criterion-based cascaded channel estimator. Moreover, a closed-form expression of the downlink ergodic spectral efficiency (SE) is derived based on maximum ratio (MR) based transmit precoding and channel statistics, where both a finite number of access points (APs) and STAR-RIS elements as well as imperfect hardware are considered. Furthermore, by exploiting the ergodic signal-to-interference-plus-noise ratios (SINRs) among user equipment (UE), a max-min fairness problem is formulated for the joint optimization of the passive transmitting and reflecting beamforming (BF) at the STAR-RIS as well as of the power control coefficients. An alternating optimization (AO) algorithm is proposed for solving the resultant problems, where iterative adaptive particle swarm optimization (APSO) and bisection methods are proposed for circumventing the non-convexity of the RIS passive BF and the quasi-concave power control sub-problems, respectively. Our simulation results illustrate that the STAR-RIS-aided CF-mMIMO system attains higher SE than its RIS-aided counterpart. The performance of different hardware parameters is also evaluated. Additionally, it is demonstrated that the SE of the worst UE can be significantly improved by exploiting the proposed AO-based algorithm compared to conventional solutions associated with random passive BF and equal-power scenarios.

Problem

• STAR-RIS
• Massive MIMO
• Spectral Efficiency

Innovation

• STAR-RIS Assisted CF-mMIMO
• Hardware-Impaired Performance Optimization
• Signal Efficiency Enhancement