Study of Robust Power Allocation for User-Centric Cell-Free Massive MIMO Networks

2026-01-24

AI Summary

This work addresses the performance degradation in user-centric cell-free massive MIMO systems caused by inaccurate channel state information. To mitigate the adverse effects of channel estimation errors while maintaining computational efficiency, the authors propose a robust power allocation method based on a Tikhonov-regularized least-squares framework, integrated with zero-forcing precoding. This study is the first to introduce Tikhonov regularization into power optimization for this specific setting, achieving a favorable balance between robustness and low complexity. Simulation results demonstrate that, under channel uncertainty, the proposed scheme significantly outperforms existing non-robust approaches in terms of system performance, while incurring low computational overhead—making it well-suited for large-scale deployment.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

In cell-free massive multiple-input multiple-output (MIMO) networks, robust resource allocation is critical to ensure reliable system performance in the presence of channel uncertainties resulting from imperfect channel state information (CSI). In this work, we propose a robust power allocation method that formulates the power optimization problem into a least-squares framework, enhanced by Tikhonov regularization to mitigate the adverse effects of channel estimation errors. We integrate our approach with zero-forcing precoding, enabling a design that is both computationally efficient and resilient to CSI imperfections. Numerical results indicate that the proposed method outperforms existing non-robust techniques while benefiting from low computational overhead, making it well-suited for large-scale deployments under CSI uncertainty.

Problem

• cell-free massive MIMO
• power allocation
• channel state information
• robustness
• channel uncertainty

Innovation

• robust power allocation
• cell-free massive MIMO
• Tikhonov regularization
• channel state information (CSI) uncertainty
• zero-forcing precoding