Augmented Iterative Learning Control for Neural-Network-Based Joint Crest Factor Reduction and Digital Predistortion of Power Amplifiers

Siqi Wang (1) , Morgan Roger (1, 2) , Julien Sarrazin (3) , Caroline Lelandais-Perrault (1)

1 GeePs - Laboratoire Génie électrique et électronique de Paris
2 CentraleSupélec
3 GeePs - Laboratoire Génie électrique et électronique de Paris

Digital predistorsion (DPD) is a commonly used approach to compensate for the power amplifiers (PA) nonlinearities and memory effects as well as to improve its power efficiency. To alleviate the restriction brought by high peak-to-average power ratio (PAPR) of input signals, crest factor reduction (CFR) is needed for higher efficiency. In modern communication systems, power of transmitted signals gets lower, which makes complexities of CFR and DPD become non-negligible. This paper proposes a new approach to realize a joint CFR and DPD model using neural networks (NN). The modeling accuracy is guaranteed by a new proposed augmented iterative learning control (AILC) algorithm for the NN training signals. Compared with conventional ILC, the proposed AILC is shown more robust according to simulation and experimental results. The proposed AILC-based NN-CFRDPD is experimentally evaluated on different test benches.

Keywords: crest factor reduction; digital predistortion; iterative learning control; neural networks; power amplifiers efficiency

Domains: Engineering Sciences [physics]; Signal and Image processing; Engineering Sciences [physics]; Micro and nanotechnologies/Microelectronics; Engineering Sciences [physics]; Electronics

Siqi Wang, Morgan Roger, Julien Sarrazin, Caroline Lelandais-Perrault. Augmented Iterative Learning Control for Neural-Network-Based Joint Crest Factor Reduction and Digital Predistortion of Power Amplifiers. IEEE Transactions on Microwave Theory and Techniques, 2020, ⟨10.1109/TMTT.2020.3011152⟩. ⟨hal-02872144⟩