Scalable Statistical Channel Estimation and Its Applications in User-Centric Cell-Free Massive MIMO Systems

Area of Science:

• Wireless communication
• Signal processing
• Information theory

Background:

• Cell-free massive MIMO (mMIMO) enhances system performance through collaborative signal processing.
• Accurate channel state information (CSI) is crucial for mMIMO performance.
• Existing statistical CSI acquisition methods for large-scale fading (LSF) are computationally intensive and not scalable for large networks.

Purpose of the Study:

• To propose a scalable statistical CSI estimation method for user-centric cell-free mMIMO systems.
• To enable blind estimation of partial statistical CSI for LSF schemes using uplink data signals.
• To evaluate the applicability of the estimated CSI for downlink LSF precoding (LSFP) and power control.

Main Methods:

• Blind estimation of partial statistical CSI using uplink data signals in a user-centric cell-free mMIMO architecture.
• Application of estimated partial CSI for LSF precoding (LSFP) and power control in fully distributed precoding.

Main Results:

• The proposed method achieves comparable spectral efficiency (SE) to traditional CSI acquisition schemes under LSFP, ensuring scalability.
• When used for power control, the method significantly reduces fronthaul link CSI overhead.
• Maintains nearly similar SE performance compared to existing solutions in power control applications.

Conclusions:

• The developed scalable statistical CSI estimation method effectively addresses the limitations of traditional approaches in large-scale cell-free mMIMO networks.
• The method offers a practical solution for improving LSF schemes and power control efficiency.
• Demonstrates significant reductions in fronthaul overhead without compromising spectral efficiency.