Large-Scale Beamforming for Massive MIMO via Randomized Sketching

Massive MIMO system yields significant improvements in spectral and energy efficiency for future wireless communication systems. The regularized zero-forcing (RZF) beamforming is able to provide good performance with the capability of achieving numerical stability and robustness to the channel uncer...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on vehicular technology Jg. 70; H. 5; S. 4669 - 4681
Hauptverfasser: Choi, Hayoung, Jiang, Tao, Shi, Yuanming, Liu, Xuan, Zhou, Yong, Letaief, Khaled B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Approximation
Array signal processing
Beamforming
Convergence
Energy conversion efficiency
Iterative methods
Linear algebra
Linear systems
Massive MIMO
MIMO (control systems)
Numerical stability
randomized sketching algorithm
Regularized zero-forcing beamforming
Robustness (mathematics)
Simulation
sketching method
Uncertainty
Wireless communication systems
Wireless networks
ISSN:0018-9545, 1939-9359
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Massive MIMO system yields significant improvements in spectral and energy efficiency for future wireless communication systems. The regularized zero-forcing (RZF) beamforming is able to provide good performance with the capability of achieving numerical stability and robustness to the channel uncertainty. However, in massive MIMO systems, the matrix inversion operation in RZF beamforming becomes computationally expensive. To address this computational issue, we shall propose a novel randomized sketching based RZF beamforming approach with low computational complexity. This is achieved by solving a linear system via randomized sketching based on the preconditioned Richard iteration, which guarantees high quality approximations to the optimal solution. We theoretically prove that the sequence of approximations obtained iteratively converges to the exact RZF beamforming matrix linearly fast as the number of iterations increases. Also, it turns out that the system sum-rate for such sequence of approximations converges to the exact one at a linear convergence rate. Our simulation results verify our theoretical findings.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3071543