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.
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This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.02-2016.12.
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This paper has been submitted in part to the 5th EAI International Conference on Industrial Networks and Intelligent Systems (INISCOM 2019). The corresponding and the first authors are the main authors contributing equally to the paper.
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Le, D.H., Hoang, VP., Nguyen, M.H. et al. Linearization of RF Power Amplifiers in Wideband Communication Systems by Adaptive Indirect Learning Using RPEM Algorithm. Mobile Netw Appl 25, 1988–1997 (2020). https://doi.org/10.1007/s11036-020-01545-z
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DOI: https://doi.org/10.1007/s11036-020-01545-z