Robust Precoder Design for User-Centric Network Massive MIMO With Symplectic Optimization

In this letter, we propose a symplectic optimization approach for robust precoder design in the user-centric network (UCN) massive MIMO (mMIMO) system. The system implementation of the UCN mMIMO is simplified and the dimension of the precoder is reduced, since each user terminal (UT) communicates wi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE wireless communications letters S. 1
Hauptverfasser: Lin, Pengxu, Lu, An-An, Gao, Xiqi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
Channel estimation
Channel models
Dynamical systems
Gradient methods
Interference
Kinetic energy
Massive MIMO
network massive MIMO
Optimization
Potential energy
robust precoder design
symplectic optimization
User-centric
Vectors
ISSN:2162-2337, 2162-2345
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this letter, we propose a symplectic optimization approach for robust precoder design in the user-centric network (UCN) massive MIMO (mMIMO) system. The system implementation of the UCN mMIMO is simplified and the dimension of the precoder is reduced, since each user terminal (UT) communicates with a properly selected subset of all base stations (BSs). We use an a posteriori channel model that incorporates channel aging to enhance system robustness. Based on this, we develop a robust precoder design method with symplectic optimization, which addresses the high complexity of the matrix inversion in traditional linear precoders optimization. Moreover, we use the relativistic dynamical system in symplectic optimization to get a more stable algorithm. We iteratively obtain the precoder vectors directly by utilizing the transformation between potential and kinetic energy in the dynamical system. Simulation results indicate that the proposed approach significantly outperforms the WMMSE precoder in mobility scenarios with lower computational complexity.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2025.3621236