Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO

In this letter, we consider the problem of channel estimation for large intelligent metasurface (LIM) assisted massive multiple-input multiple-output (MIMO) systems. The main challenge of this problem is that the LIM integrated with a large number of low-cost metamaterial antennas can only passively...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE wireless communications letters Jg. 9; H. 2; S. 210 - 214
Hauptverfasser: He, Zhen-Qing, Yuan, Xiaojun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation algorithms
Array signal processing
Bilinear factorization
Channel estimation
Computer simulation
large intelligent metasurface
massive MIMO
matrix completion
Metamaterials
Metasurfaces
MIMO (control systems)
Signal processing
Signal processing algorithms
Sparse matrices
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 consider the problem of channel estimation for large intelligent metasurface (LIM) assisted massive multiple-input multiple-output (MIMO) systems. The main challenge of this problem is that the LIM integrated with a large number of low-cost metamaterial antennas can only passively reflect the incident signals by certain phase shifts, and does not have any signal processing capability. To deal with this, we introduce a general framework for the estimation of the transmitter-LIM and LIM-receiver cascaded channel, and propose a two-stage algorithm that includes a sparse matrix factorization stage and a matrix completion stage. Simulation results illustrate that the proposed method can achieve accurate channel estimation for LIM-assisted massive MIMO systems.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2019.2948632