Robust Precoding for Resilient Cell-Free Networks

2025-11-29

AI Summary

To address the insufficient robustness of cell-free massive MIMO systems under imperfect channel state information (CSI), this paper proposes a weighted robust precoding framework that jointly optimizes the mean square error (MSE) of the desired signal and the residual interference leakage power. The framework explicitly incorporates the statistical characteristics of CSI errors and operates under a total transmit power constraint. An alternating optimization algorithm—initialized via minimum mean square error (MMSE) precoding—is employed to achieve high-performance convergence with low computational complexity. Compared with conventional linear precoding schemes, the proposed method significantly enhances system resilience to channel uncertainty. Numerical evaluations under typical non-ideal CSI conditions demonstrate superior bit error rate (BER) performance and higher spectral efficiency, thereby validating its strong interference suppression capability and practical engineering applicability.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This paper presents a robust precoder design for resilient cell-free massive MIMO (CF-mMIMO) systems that minimizes the weighted sum of desired signal mean square error (MSE) and residual interference leakage power under a total transmit power constraint. The proposed robust precoder incorporates channel state information (CSI) error statistics to enhance resilience against CSI imperfections. We employ an alternating optimization algorithm initialized with a minimum MSE-type solution, which iteratively refines the precoder while maintaining low computational complexity and ensuring fast convergence. Numerical results show that the proposed method significantly outperforms conventional linear precoders, providing an effective balance between performance and computational efficiency.

Problem

• Designs robust precoder for resilient cell-free massive MIMO systems
• Minimizes signal error and interference under power constraints
• Enhances resilience against imperfect channel state information

Innovation

• Robust precoder design for cell-free MIMO
• Incorporates CSI error statistics for resilience
• Alternating optimization with low complexity