Partial Least Squares Identification of Multi Look-Up Table Digital Predistorters for Concurrent Dual-Band Envelope Tracking Power Amplifiers

This paper presents a technique to estimate the coefficients of a multiple-look-up table (LUT) digital predistortion (DPD) architecture based on the partial least-squares (PLS) regression method. The proposed 3-D distributed memory LUT architecture is suitable for efficient FPGA implementation and c...

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Veröffentlicht in:IEEE transactions on microwave theory and techniques Jg. 66; H. 12; S. 5143 - 5150
Hauptverfasser: Pham, Quynh Anh, Lopez-Bueno, David, Wang, Teng, Montoro, Gabriel, Gilabert, Pere L.
Format: Journal Article Verlag
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
IEEE Microwave Theory and Techniques Society
Schlagworte:
Coefficients
Complexity
Computational modeling
Data structures (Computer science)
Digital predistortion (DPD)
Distortion
Distributed memory
Enginyeria de la telecomunicació
envelope tracking (ET)
Estructures de dades (Informàtica)
Feedback
Least squares
Linearity
look-up tables (LUTs)
Matching pursuit algorithms
Matrix converters
Orthogonality
Parameter identification
partial least squares (PLS)
power amplifier (PA)
Power amplifiers
Principal component analysis
principal component analysis (PCA)
Regularization
Table lookup
Telemàtica i xarxes d'ordinadors
Tracking
Àrees temàtiques de la UPC
ISSN:0018-9480, 1557-9670
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Beschreibung
Zusammenfassung:This paper presents a technique to estimate the coefficients of a multiple-look-up table (LUT) digital predistortion (DPD) architecture based on the partial least-squares (PLS) regression method. The proposed 3-D distributed memory LUT architecture is suitable for efficient FPGA implementation and compensates for the distortion arising in concurrent dual-band envelope tracking power amplifiers. On the one hand, a new variant of the orthogonal matching pursuit algorithm is proposed to properly select only the best LUTs of the DPD function in the forward path, and thus reduce the number of required coefficients. On the other hand, the PLS regression method is proposed to address both the regularization problem of the coefficient estimation and, at the same time, reducing the number of coefficients to be estimated in the DPD feedback identification path. Moreover, by exploiting the orthogonality of the PLS transformed matrix, the computational complexity of the parameters' identification can be significantly simplified. Experimental results will prove how it is possible to reduce the DPD complexity (i.e., the number of coefficients) in both the forward and feedback paths while meeting the targeted linearity levels.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9480
1557-9670
DOI:10.1109/TMTT.2018.2857819