Paper Info
Title
Linearization Of Rf Power Amplifiers In Wideband Communication Systems By Adaptive Indirect Learning Using Rpem Algorithm
Abstract
This paper proposes a new approach of digital predistortion (DPD) technique based on the adaptive indirect learning architecture (ILA) by using a recursive prediction error minimization (RPEM) algorithm for linearizing radio frequency (RF) power amplifiers (PAs) in emerging wideband communication systems. In the proposed RPEM-based linearization approach, the forgetting factor varies with time and is less sensitive to noise. Therefore, the predistorter (PD) parameter estimates become more consistent and accurate in steady state so that the mean square errors can be reduced. Both the error vector magnitude (EVM) and the adjacent channel power ratio (ACPR) are used to evaluate the DPD technique in RF PAs employing the proposed linearization. The efficiency validation of the proposed method is based on a simulated PA Wiener model. The simulation results have clarified the improvement of the proposed adaptive ILA-based DPD with RPEM algorithm in terms of both EVM and ACPR.
Year
DOI
Venue
2020
10.1007/s11036-020-01545-z
MOBILE NETWORKS & APPLICATIONS
Keywords
DocType
Volume
RPEM, Digital predistortion, RF power amplifiers, Linearization, Adaptive indirect learning architecture, Predistorter
Journal
25
Issue
ISSN
Citations 
5
1383-469X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
han le duc1173.39
Van-Phuc Hoang234.89
Minh Hong Nguyen300.34
Nguyen, H.M.464.60
Duc Minh Nguyen54310.96