A General Approach to Fully Linearize the Power Amplifiers in mMIMO With Low Complexity

A radio frequency (RF) power amplifier (PA) plays an important role to amplify the message signal at higher power to transmit it to a distant receiver. Due to a typical nonlinear behavior of the PA at high power transmission, digital predistortion (DPD), exploiting the preinversion of the nonlineari...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on communications Jg. 73; H. 7; S. 4749 - 4765
Hauptverfasser: Prasad, Ganesh, Johansson, Hakan, Hussain Laskar, Rabul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
band-limited digital predistortion
Complexity
Convergence
Crosstalk
Digital predistortion
direct learning architecture
indirect learning architecture
Machine learning
massive MIMO
Nonlinearity
Polynomials
Power amplifiers
Prediction algorithms
Predistortion
Principal component analysis
Radio frequency
Radio transmitters
recursive prediction error method
Transmitters
Tuning
Digital predistortion
Radio transmitters
Crosstalk
Predistortion
band-limited digital predistortion
Tuning
direct learning architecture
Convergence
Radio frequency
recursive prediction error method
Transmitters
indirect learning architecture
massive MIMO
Prediction algorithms
Polynomials
Principal component analysis
ISSN:0090-6778, 1558-0857, 1558-0857
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A radio frequency (RF) power amplifier (PA) plays an important role to amplify the message signal at higher power to transmit it to a distant receiver. Due to a typical nonlinear behavior of the PA at high power transmission, digital predistortion (DPD), exploiting the preinversion of the nonlinearity, is used to linearize the PA. However, in a massive MIMO (mMIMO) transmitter, a single DPD is not sufficient to fully linearize multiple PAs. Further, for the full linearization, assigning a separate DPD to each PA is complex and not economical. In this work, we address these challenges via the proposed low-complexity DPD (LC-DPD) scheme. Initially, we describe the fully-featured DPD (FF-DPD) scheme to linearize the multiple PAs and examine its complexity. Thereafter, using it, we derive the LC-DPD scheme that can adaptively linearize the PAs as per the requirement. The coefficients in the two schemes are learned using the algorithms that adopt indirect learning architecture based recursive prediction error method (ILA-RPEM) due to its adaptive and free from matrix inversion operations. Furthermore, for the LC-DPD structure, we have proposed three algorithms based on correlation of its common coefficients with the distinct coefficients.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0090-6778
1558-0857
1558-0857
DOI:10.1109/TCOMM.2024.3516514